Decision Support and Business Intelligence Systems, 8E Efraim Solution Manual

Decision Support and Business Intelligence Systems, 8E Efraim By Efraim

Email: Richard@qwconsultancy.com

CHAPTER

1

Decision Support Systems and Business Intelligence

Learning Objectives for Part I ⧫ ⧫ ⧫ ⧫

Understand the complexity of today’s business environment Understand the foundations and key issues of managerial decision making Recognize the difficulties in managerial decision making today Learn the major frameworks of computerized decision support: decision support systems (DSS) and business intelligence

Learning Objectives for Chapter 1 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand today’s turbulent business environment and describe how organizations survive and even excel in such an environment (solving problems and exploring opportunities) Understand the need for computerized support of managerial decision making Understand an early framework for managerial decision making Learn the conceptual foundations of the decision support systems (DSS1) methodology Describe the business intelligence (BI) methodology and concepts and relate them to DSS Describe the concept of work systems and its relationship to decision support List the major tools of computerized decision support Understand the major issues in implementing computerized support systems

CHAPTER OVERVIEW This chapter introduces the subject by providing a series of frameworks within which the rest of the book, and the topics to come, will fit. It gives students an overview of the subject area from 1

The acronym DSS is treated as both singular and plural throughout this book. Similarly, other acronyms, such as MIS and GSS, designate both plural and singular forms.

1

2

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

several viewpoints and an indication of what is to come—both in the book and in the course.

Finally, it introduces supporting resources such as Teradata University Network.

CHAPTER OUTLINE 1.1

OPENING VIGNETTE: TOYOTA USES BUSINESS INTELLIGENCE TO EXCEL A. PROBLEM B. SOLUTION C. RESULTS  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

1.2

CHANGING BUSINESS ENVIRONMENTS AND COMPUTERIZED DECISION SUPPORT A. THE BUSINESS PRESSURES–RESPONSES–SUPPORT MODEL 1.

The Business Environment 2.

Organizational Responses: Be Reactive, Anticipative, Adaptive, and Proactive 3.

Closing the Strategy Gap  Section 1.2 Review Questions

1.3

MANAGERIAL DECISION MAKING A. THE NATURE OF MANAGERS’ WORK B. THE PROCESS OF DECISION MAKING ⧫ Technology Insights 1.1: Decision Making Ability Rated First in Survey  Section 1.3 Review Questions

1.4

COMPUTERIZED SUPPORT FOR DECISION MAKING A. WHY WE USE COMPUTERIZED DECISION SUPPORT SYSTEMS  Section 1.4 Review Questions

1.5

AN EARLY FRAMEWORK FOR COMPUTERIZED DECISION SUPPORT A. THE GORRY AND SCOTT-MORTON CLASSICAL FRAMEWORK 1.

Degree of Structuredness 2.

Types of Control 3.

The Decision Support Matrix B. COMPUTER SUPPORT FOR STRUCTURED DECISIONS 1.

Management Science 2.

Automated Decision Making ⧫ Application Case 1.2: Intelligent Price Setting Using an ADS C. COMPUTER SUPPORT FOR UNSTRUCTURED DECISIONS D. COMPUTER SUPPORT FOR SEMISTRUCTURED PROBLEMS E. THE BENEFITS OF COMPUTERIZED DECISION SUPPORT ⧫ Application Case 1.3: Decision Support at Hallmark for Better

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual



Strategy and Performance Section 1.5 Review Questions

1.6

THE CONCEPT OF DECISION SUPPORT SYSTEMS (DSS) A. DSS AS AN UMBRELLA TERM ⧫ Application Case 1.4: The Houston Minerals Case 1.

DSS As A Specific Application 2.

The Architecture of DSS 3.

Types of DSS B. CONCLUDING REMARKS ⧫ Application Case 1.5: Helping Atlantic Electric Company Survive in the Deregulated Marketplace  Section 1.6 Review Questions

1.7

A FRAMEWORK FOR BUSINESS INTELLIGENCE (BI) A. CHARACTERISTICS OF TOYOTA’S SYSTEM B. DEFINITIONS OF BI C. A BRIEF HISTORY OF BI D. THE ARCHITECTURE OF BI 1.

Data Warehousing 2.

Business Analytics 3.

Business Performance Management 4.

The User Interface: Dashboards and Other Information Broadcasting Tools E. STYLES OF BI F. THE BENEFITS OF BI ⧫ Application Case 1.6: Predictive Analytics Help Texas Collect Taxes G. THE DSS–BI CONNECTION 1.

Management Support Systems (MSS)  Section 1.7 Review Questions

1.8

A WORK SYSTEM VIEW OF DECISION SUPPORT  Section 1.8 Review Questions

1.9

THE MAJOR TOOLS AND TECHNIQUES OF MANAGERIAL DECISION SUPPORT A. THE TOOLS AND TECHNIQUES B. THE TOOLS–WEB CONNECTION C. HYBRID SUPPORT SYSTEMS ⧫ Application Case 1.7: United Sugars Corporation Optimizes Production, Distribution, and Inventory Capacity with Different Decision Support Tools D. EMERGING TECHNOLOGIES AND TECHNOLOGY TRENDS  Section 1.9 Review Questions

3

4

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

1.10

IMPLEMENTING COMPUTER-BASED MANAGERIAL DSS A. DEVELOPING OR ACQUIRING SUPPORT SYSTEMS B. JUSTIFICATION AND COST-BENEFIT ANALYSIS C. SECURITY AND PROTECTION OF PRIVACY D. INTEGRATION OF SYSTEMS AND APPLICATIONS E. THE WEB IN DSS/BI IMPLEMENTATION 1.

Information Portals and MSS  Section 1.10 Review Questions

1.11

PLAN OF THE BOOK A. PART I: DECISION SUPPORT AND BUSINESS INTELLIGENCE B. PART II: COMPUTERIZED DECISION SUPPORT C. PART III: BUSINESS INTELLIGENCE D. PART IV: COLLABORATION, COMMUNICATION, GROUP SUPPORT SYSTEMS, AND KNOWLEDGE MANAGEMENT E. PART V: INTELLIGENT SYSTEMS F. PART VI: IMPLEMENTING DECISION SUPPORT SYSTEMS G. PART VII: ONLINE SUPPLEMENTS

1.12

RESOURCES, LINKS, AND THE TERADATA UNIVERSITY NETWORK CONNECTION A. RESOURCES AND LINKS B. CASES C. MILLER’S MIS CASES D. VENDORS, PRODUCTS, AND DEMOS E. PERIODICALS F. ADDITIONAL REFERENCES G. THE TERADATA UNIVERSITY NETWORK CONNECTION H. THE BOOK’S WEB SITE

Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata Student Network (TSN) and Other Hands-On Exercises Team Assignments and Role-Playing Internet Exercises ⧫ End of Chapter Application Case: Decision Support at a Digital Hospital  Questions for the Case References TEACHING TIPS/ADDITIONAL INFORMATION

         

It is important to stress the frameworks when covering this chapter. The specifics of each subject are less important at this stage, as they will be revisited in more detail in the chapters to come.

Going into detail about, say, BI or integration at this stage will mean taking longer on this

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

5

chapter than the course has time for or skimping on the perspective that students should get from the start.

It can also be useful at this stage to position DSS in the overall context of organizational information systems. They stand above transaction processing systems in the dual sense of typically being used higher up in the organization and having greater impact on it each time they are used. However, they are not independent of other systems: Transaction processing is a major source of data for decision support, in general, and for data warehouses, in particular. External information sources are vital to any DSS. This point is brought out repeatedly throughout the chapter, but it doesn’t always jump out at the typical student reader so you may have to draw attention to it.

Some of the frameworks in the chapter relate more to decision making, while others are more on the information systems side. Specifically, Sections 2, 3, and 8 are more about decision making as a managerial activity independent of how it is supported through technology. Sections 4, 9, and 10 are more on the technology side. Sections 5, 6, and 7 have elements of both in different subsections. (Sections 1, 11, and 12 cannot be positioned along this dimension.) It will help students put the material in perspective if you position the topics for them: “This is process, this is technology, now we’re back to process.”

You may find some transitions from one section to the next abrupt. This is efficient in terms of content density but may leave students wondering how things are related. For example, Section 1.3 (Managerial Decision Making) jumps right into the importance of decision making as a managerial activity without explaining its importance to the study of DSS. This is obvious, of course, to any instructor who thinks about it: How can one develop systems to support decision making, if one doesn’t understand how decisions are made? It may not be equally obvious to younger students. You will have to make some of the transitions among sections explicit where they are implicit in the book.

You may also be used to a more rigorous distinction between decision making and problem solving than is used here. This book uses the two terms interchangeably. The authors explain this in Section 2.2 under the subhead “Decision Making and Problem Solving.” You should be aware of their usage from the start, though—at least to inform how you discuss what the book says, even if you choose not to make the terminology issue explicit until it’s mentioned in the next chapter.

A secondary purpose of any introductory chapter is to motivate students to be interested in the remainder of the course (and book). The real-life cases, beginning with Toyota and continuing with the others, will show students that decision support is not just an academic subject; it is something real companies use that makes a noticeable difference to their bottom line. So, try to relate the subject matter to these cases. For example, consider the types of actions managers take to counter pressures (Section 1.2). The text says that Toyota used BI “to improve communication and to support executives [with information].” The other cases in the chapter, while not tied directly to this list, still have many examples of managerial actions taken in response to pressure. By referring back to this list when discussing other cases, you demonstrate the unity of the decision support field.

All this should show students that a new professional who understands how information systems can support decision making, and can help his or her employer obtain those benefits, has a bright career path. Since students in this course are typically within a year of graduation, that will get their attention!

6

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 1.1 Review Questions (This section has no review questions.)

Section 1.2 Review Questions 1.

List the components of and explain the Business Pressures–Responses–Support model.

The components of the pressure-response-support model are business pressures, companies’ responses to these pressures, and computerized support. The model suggests that responses are made to counter the pressures or to take advantage of opportunities, support facilitates monitoring the environment (e.g., for opportunities) and enhances the quality of the responses.

2.

What are the major factors in today’s business environment?

The major factors in today’s business environment are: • (Market-related) strong competition, expansion of global markets, electronic markets blooming on the Internet, innovative marketing methods, opportunities for outsourcing with IT support, and need for real time on-demand transactions • (Consumer demand-related) wanting customization, wanting quality, diversity of products, and speed of delivery, and customers becoming powerful and less loyal • (Technology-related) more innovations, new products and services, obsolescence rate is increasing, information overload is increasing • (Societal) growth of government regulations and deregulation are growing, work force becoming more diversified, older, and composed of more women; homeland security and terrorist attacks are prime concerns; compliance with the SarbanesOxley Act and other reporting-related legislation is a must; social responsibility of companies is increasing.

3.

What are some of the major response activities organizations take?

Responses taken by organizations include, but are not limited to: • Employ strategic planning • Use of new and innovative business models • Restructure business processes • Participate in business alliances • Improve corporate information systems • Improve partnership relationships • Encourage innovation and creativity • Improve customer service and relationships • Move to electronic commerce (e-commerce) • Move to make-to-order production and on-demand manufacturing and services

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

7

•

Use new IT to improve communication, data access (discovery of information) and collaboration • Respond quickly to competitors’ actions (e.g., in pricing, promotions, new products and services) • Automate many tasks of white-collar employees • Automate certain decision processes especially those dealing with customers • Improve decision making (Since the question reads “some,” a correct answer need not list all of these.)

4.

Define the strategy gap.

The strategy gap is the gap between the current performance of an organization and its desired performance as expressed in the mission, objectives and goals, and the strategy to achieve them.

Section 1.3 Review Questions 1.

Describe the three major managerial roles, and list some of the specific activities in each.

Managerial roles fall into the three major categories: • Interpersonal activities. Examples of interpersonal activities include being a figurehead, a leader and a liaison.

• Informational activities. Examples of informational activities include monitoring, disseminating and acting as a spokesperson.

• Decisional activities. Examples of decisional roles include being an entrepreneur, handling disturbances, allocating resources and negotiating.

2.

Why have some argued that management is the same as decision making?

Some argue that decision making is consistently rated as either the most important or one of the most important components of managing. However, as also seen in this section, others have defined many managerial roles besides decision making.

3.

Describe the four steps managers take in making a decision.

Step 1. Define the problem (i.e., a decision situation that may deal with some difficulty or with an opportunity).

Step 2. Construct a model that describes the real-world problem.

Step 3. Identify possible solutions to the modeled problem and evaluate the solutions.

Step 4. Compare, choose, and recommend a potential solution to the problem.

4.

Explain why intuition and trial-and-error approaches to managerial decision making may not be effective in today’s business environment.

Intuition and trial-and error approaches to managerial decision making may not be effective in today’s business environment due to three trends: (1) more alternatives from which to choose as a result of technology, information systems, advanced search engines and globalization; (2) more uncertainty due to factors including compliance with government regulations, political instability and terrorism, competition and changing

8

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

customer demands, making it more difficult to predict the future consequences of one’s actions; and (3) the need to make rapid decisions coupled with the potentially large cost of mistakes.

Section 1.4 Review Questions 1.

How have the capabilities of computing evolved over time?

Computer applications have moved from transaction processing and monitoring activities to problem analysis and solution applications, with much of the activity using Web-based technologies. Business intelligence tools such as data warehousing, data mining, online analytical processing, dashboards, and the use of the Web for decision support have become cornerstones of modern management. High-speed, networked information systems have become necessary to assist managers with their most important task, which is making decisions.

2.

List some capabilities of computing that can facilitate managerial decision making.

These include speedy computation, improved communication and collaboration, increased group member productivity, improved data management, managing data warehouses, quality and agility support, overcoming human cognitive limits in information processing and storage, using the Web, and the ability to access information anywhere, anytime. (Since the question reads “some,” a correct answer need not list all of these.)

3.

How can a computer help overcome the cognitive limits of humans?

Computers can store, access and process vast amounts of information, beyond the limited ability of the human mind to do these.

4.

Why is the Web considered so important for decision support?

The Web provides: (1) access to a vast body of data, information, and knowledge available around the world; (2) a common, user-friendly and readily available graphical user interface; (3) the ability to collaborate effectively with remote partners; and (4) availability of intelligent search tools that enable managers to find information they need quickly and inexpensively.

Section 1.5 Review Questions 1.

What are structured, unstructured, and semistructured decisions? Provide two examples of each.

An unstructured decision is one in which none of the three decision phases (intelligence, design, choice) is structured. Examples: writing a corporate mission statement, selecting a location for a company picnic.

A structured decision is one in which all phases are structured. Examples: finding an appropriate inventory level, choosing an optimal investment strategy.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

9

Semistructured decisions fall between structured and unstructured problems, having some structured elements and some unstructured elements. Examples: trading bonds, setting marketing budgets for consumer products, performing capital acquisition analysis.

(Other correct examples are possible.)

2.

Define operational control, managerial control, and strategic planning. Provide two examples of each.

Operational control is the efficient and effective execution of specific tasks. Examples: scheduling computer storage backups, planning next weeks’ company cafeteria menu.

Management control is the acquisition and efficient use of resources to accomplish organizational goals. Examples: hiring a production coordinator, planning an advertising program.

Strategic planning is defining long-range goals and policies for resource allocation.

Examples: choosing which of three new products to develop, deciding whether or not to outsource customer telephone support to a region with lower labor costs than where it is now based.

(Other correct examples are possible.)

3.

What are the nine cells of the decision framework? Explain what each is for.

Each of the nine cells represents a combination of one degree of decision structure with one type of management control. Cell 1 represents a structured decision that falls within the span of operational control, and so on.

4.

How can computers provide support to structured decisions?

The high level of structure that structured problems have makes it possible to abstract them, analyze them, and classify them into specific categories. For each category, it is possible to develop a standard solution approach, usually with a standard quantitative model. This model can then be implemented in easy-to-apply software.

5.

Define automated decision systems (ADS).

Automated Decision Systems are rule-based systems that provide a solution to a specific repetitive managerial problem, usually in one industry and functional area.

6.

How can computers provide support to semistructured and unstructured decisions?

Unstructured decisions can be only partially supported by standard computerized quantitative methods. Usually it is necessary to develop a customized solution. Intuition and judgment play a larger role in this type of decision than they do in making structured decisions. They may benefit from computerized communication and collaboration technologies and from knowledge management. Intelligent systems can also sometimes provide expertise that such solutions require.

10

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Making semistructured decisions may involve a combination of both standard solution procedures and human judgment. Management Science can provide models for the portion of the decision-making problem that is structured. For the unstructured portion, a DSS can improve the quality of the information on which the decision is based by providing, for example, not only a single solution but also a range of alternative solutions along with their potential impacts. These capabilities help managers to better understand the nature of problems and thus to make better decisions.

7.

What are some of the drivers and benefits of computerized decision support?

Drivers: • Companies work in an unstable or rapidly changing economy.

• There are difficulties in tracking the numerous business operations.

• Competition has increased especially global competition.

• Electronic commerce is changing the ways business is done.

• Existing information systems do not fully support decision making.

• The information systems department is too busy to address all of management’s inquiries.

• Special analysis of profitability and efficiency is needed.

• Accurate information is needed.

• Computerized support is viewed as an organizational winner.

• New information is needed.

• Management mandates computerized decision support.

• Higher decision quality is needed.

Benefits: • Improved communication • Improved customer and employee satisfaction is derived.

• Timely information is provided.

• Cost reduction is achieved.

• Employees’ productivity has been improved.

(Since the question reads “some,” a correct answer need not list all of these.)

Section 1.6 Review Questions 1.

Provide two definitions of DSS. The two definitions of DSS given in the text are: • Interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems.

• [They] couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semistructured problems.

(Other correct definitions are possible. Students who provide different definitions, unless they wrote those definitions themselves, should be expected to cite their sources.)

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

2.

11

Describe DSS as an umbrella term.

Used in this way, DSS describes any computerized system that supports decision making in an organization. An organization may have (for example) a knowledge management system to guide all its personnel in their problem solving, it may have separate support systems for marketing, finance, and accounting, a supply chain management (SCM) system for production, and several expert systems for product repair diagnostics and help desks. The term DSS encompasses them all.

3.

Describe the architecture of DSS. The DSS architecture consists of four components, plus optionally a fifth.

The four required components are: data: to drive the system; models: to manipulate the data; users: to control the system; and a user interface: through which they control it.

DSS may also optionally have a knowledge (or intelligence) component.

4.

How is the term DSS used in the academic world?

The term DSS is used primarily in the academic world today. It is commonly used to describe the area of research related to computer support for decision making, is the name of a leading journal in the field, and is a standard course title. The industry tends to use the term business intelligence. However, the two are not always seen as identical; see Review Question 4 of Section 1.7.

Section 1.7 Review Questions 1.

Define BI. Business Intelligence (BI) is an umbrella term that combines architectures, tools, databases, applications, and methodologies. Its major objective is to enable interactive access (sometimes in real time) to data, enable manipulation of these data, and to provide business managers and analysts the ability to conduct appropriate analysis.

2.

List and describe the major components of BI. BI systems have four major components: the data warehouse (analogous to the data in the DSS architecture), business analytics and business performance management (together, analogous to models in the DSS architecture), and the user interface (which corresponds to the component of the same name in the DSS architecture). One could also list the user as a component here, as was done there.

3.

List and describe the major tangible and intangible benefits of BI. According to one survey, the major benefits of BI are faster and more accurate reporting, improved decision making, improved customer service and increased revenue. The first three are intangible; that is, they have no directly associated financial value. The last is tangible.

12

4.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

What are the major similarities and differences of DSS and BI? • • • • •

5.

BI uses a data warehouse, whereas DSS can use any data source (including a data warehouse).

Most DSS are built to support decision making directly, whereas most BI systems are built to provide information that it is believed will lead to improved decision making.

BI has a strategy/executive orientation whereas DSS are usually oriented toward analysts.

BI systems tend to be developed with commercially available tools, whereas DSS tend to use more custom programming to deal with problems that may be unstructured.

DSS methodologies and tools originated largely in academia, whereas BI arose largely from the software industry. Many BI tools, such as data mining and predictive analysis, have come to be considered DSS tools as well.

Define MSS. The term MSS, or management support system, refers to a broad concept of using technology to support managerial tasks in general and decision making in particular.

Section 1.8 Review Questions 1.

What is Alter’s definition of decision support?

Decision Support: “The use of any plausible computerized or non-computerized means for improving decision making in a particular repetitive or non-repetitive business situation, in a particular organization.”

2.

Define work system.

Work System: “A system in which human participants and/or machines perform a business process using information, technology, and other resources to produce products and/or services for internal or external customers.”

3.

List the nine elements of a work system.

The nine elements of a work system are: business process, participants, information, technology, products/services, customers, infrastructure, environment, and strategy.

4.

Explain how decision making can be improved by changing an element of a work system.

One could, for example, change the information element of the work system. This could be done through better information quality, availability, and/or presentation. Since better information leads to better decisions, this change to the work system can improve decision making.

Other correct answers are possible, as the student can choose to discuss any of the nine elements of a work system. A correct answer must (a) reflect the content of the text as regards possible improvements in the work system element, and (b) relate improvement in the work system element to better decision making.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

13

Section 1.9 Review Questions.

1.

List the nine major categories of decision support tools.

The nine major categories are: data management, reporting status tracking, visualization, business analytics, strategy and performance management, communication and collaboration, knowledge management, intelligent systems, and enterprise systems.

2.

In what ways can the Web facilitate the use of these tools?

The Web facilitates the use of these tools by providing access. This includes (a) a common, familiar way to access a variety of software located anywhere and (b) a standard way for DSS to obtain data from a variety of sources.

3.

What is a hybrid system? What are its benefits?

A hybrid system combines several tools and techniques to solve a problem. Its benefits are that it takes advantage of the strengths of all the tools used, rather than forcing the system to use a single tool, which may be strong in some areas but weak in others.

Section 1.10 Review Questions 1.

Describe the issues involved in acquiring or building an MSS application.

The underlying issue companies face in acquiring or building an MSS application is whether to purchase, lease, or build it. Depending on the path chosen, they then face additional issues. If they build the application, they can program it from scratch, use a decision support programming language, or use a DSS development generator, in all cases possibly in conjunction with tools for report generation, querying, presentation, and summarization. If they purchase or lease it, product selection is an issue.

2.

Why it is difficult to justify decision support applications?

It is difficult to justify because many of the benefits of improved decision making are intangible.

3.

What type of integration is needed in implementing MSS? MSS systems need to be integrated with one or more of (a) databases, (b) legacy systems, (c) enterprise systems such as ERP and CRM, (d) e-commerce on both buy and sell sides, (e) the Internet, (f) information systems of business partners, and (g) each other.

4.

Describe the MSS–Web connection.

The MSS–Web connection goes two ways. In one direction, the Web impacts DSS/BI. It does so by providing improved DSS/BI access to data, improved decision maker access to DSS/BI, and a standard, familiar interface (the Web browser) to which new DSS/BI systems can be designed and which decision makers already know how to use. All of these facilitate the use of DSS/BI. In the other direction, DSS/BI systems impact the Web as well. Early GUI concepts originally developed for executive information systems have had a strong

14

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

influence on the way many Web-based applications work today. DSS/BI can help create more effective Web sites, improve the technology behind Web sites (e.g., improved network designs, load balancing across multiple servers), facilitate collaboration in Web development, and make expertise available to solve a variety of Web-related problems.

5.

Relate information portals to decision support.

The essence of decision making is having information: about the decision to be made, about the available choices, and the likely consequences of each. An information portal provides structured access to information through the familiar Web browser interface. It is thus an ideal vehicle to give decision makers the information they need. DSS/BI systems can be designed to present information via an information portal-like interface even if they do not actually use the Web to access it.

Section 1.11 Review Questions (This section has no review questions.)

Section 1.12 Review Questions (This section has no review questions.)

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Give examples for the content of each cell in Figure 1.2.

•

Structured/Operational: Ordering materials to maintain an appropriate inventory level • Semistructured/Operational: Bond trading • Unstructured/Operational: Selecting a location for the company picnic • Structured/Managerial: Selecting the optimum mix of financial investments • Semistructured/Managerial: Planning a consumer marketing campaign • Unstructured/Managerial: How should offices be arranged in a new building?

• Structured/Strategic: Deciding how to finance a take-over offer • Semistructured/Strategic: Which applicant should be chosen as the new CFO? • Unstructured/Strategic: Writing a corporate mission statement (Other correct examples are possible.)

2.

Survey the literature from the past six months to find one application of each of DSS, BI, and intelligent systems. Summarize the applications on one page and submit it with the exact sources.

There is no single correct answer to this question. Answers will depend on when the question is posed and on the student’s online or library search strategy. The correctness of an answer can be evaluated by examining the sources cited in it or submitted with it.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

3.

15

Observe an organization with which you are familiar. List three decisions it makes in each of the following categories: strategic planning, management control (tactical planning), and operational planning and control.

There is no single correct answer to this question. Answers will depend on the organization which the student observes. The correctness of an answer can be evaluated by comparing characteristics of the nine decisions with the criteria in Section 1.5 for the levels in which they are placed.

4.

Compare and contrast MS with ADS. Both the MS approach and the ADS approach are intended to select an action from a set of alternatives. The way they can be used in an organization is therefore similar.

However, their internal behavior is based on different principles. The MS approach is based on mathematical analysis derived from the principles of the problem being addressed. The ADS approach uses rules derived from the way in which people would approach the problem. If these rules are appropriate, the ADS approach can reach recommendations more quickly and efficiently, making them more suitable for decisions (such as pricing) that involve vast amounts of data. ADS also require less mathematical expertise on the part of the system developer. However, it can be harder to show that ADS recommendations are correct. In some situations they may make errors that go undetected.

5.

Describe how hybrid systems might help a manager in decision making.

Making a decision involves several steps, including gathering information about the decision situation, coming up with alternative courses of action, deciding how to evaluate them, gathering information needed for the evaluation, performing the evaluation and finally making a choice. No one tool is best at all of these. Therefore, a system that uses only one tool will be deficient in its level of support for at least some aspects of the process. A hybrid system can include components designed specifically to assist at each stage. The cost is in integrating the components so they can work with each other.

6.

How is e-commerce related to decision support?

Many decisions are required for a successful e-commerce venture. These include business strategy decisions, marketing’s four P’s (Product, Price, Place, Promotion) as applied to e-commerce, supply and distribution chain decisions, and Web/database technology decisions. Decision support can help with all of these.

In the other direction, e-commerce provides firms with direct, instantaneous access to a wealth of information about products, suppliers, and customers. It can be of value in many business decisions.

7.

Why is the role of knowledge management so important for decision support? Discuss an example of how the two can be integrated.

Knowledge management is especially helpful in providing support for unstructured decisions. With those decisions, not enough is known about the underlying problem to develop system models or management science models to help solve it, but the

16

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

knowledge of experts who have solved similar problems in the past can help guide other people through the process now. As the knowledge management system guides problem solvers through the steps of the process, there will be individual steps in which a management science or other model can be helpful. Integrating them with knowledge management makes the process “seamless” and easy to follow.

8.

Which organizational responses can be considered reactive and which proactive?

The range of organizational responses listed in the text includes the following: • Employ strategic planning • Use of new and innovative business models • Restructure business processes • Participate in business alliances • Improve the corporate information systems • Improve partnership relationships • Encourage innovation and creativity • Improve customer service and relationships • Move to electronic commerce • Move to make-to-order production and on-demand manufacturing and services • Use new information technologies to improve communication, data access (discovery of information) and collaboration • Respond quickly to competitors’ actions such as in pricing, promotions, and new products and services • Automate many tasks of white-collar employees • Automate certain decision processes especially those dealing with customers • Improve decision making The only one of these that is inherently always reactive is “respond quickly to competitors’ actions.” The others can be either reactive or proactive. For example, a firm can employ strategic planning proactively, to improve its strategic position while its competitors are doing nothing. It may also employ strategic planning reactively, recognizing that its competitors are improving their positions via this process and that it must therefore do the same. The same is true of all the other items in the table (with the single exception noted earlier). These responses are generally more successful if employed proactively, but they are not necessarily employed that way.

9.

Discuss how a wireless system can improve decision making.

Wireless technology enables managers to access information at any time, in any place.

They can then analyze and interpret it, and again through wireless technology communicate with others involved to gather more information, discuss or transmit decisions. Since decisions do not limit their appearance to times when managers are at their desks, this facilitates decision making when the decision is needed rather than when the manager is in a particular location.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

10.

17

Discuss the importance of ADS. Automated Decision Systems approach a decision by following the rules a person would use to make that decision. That decision, since it is made by following specific rules rather than on the basis of a deep understanding of the issues, is usually restricted to a single industry (e.g., airlines, drug stores) and functional area (e.g., yield management, pricing). However, computer-based systems can process a mass of data far faster than a person could. This makes them suitable for situations where the same decision rules are applied to a large number of individual items (e.g., flights and ticket classes, items to be priced). In such situations they are the most effective and efficient way to reach a good decision. Since such situations arise often in many businesses, ADS are important.

11.

Distinguish BI from DSS. • • • • •

12.

BI uses a data warehouse, whereas DSS can use any data source (including a data warehouse).

Most DSS are built to support decision making directly, whereas most BI systems are built to provide information which it is believed will lead to improved decision making.

BI has a strategy/executive orientation whereas DSS are usually oriented toward analysts.

BI systems tend to be developed with commercially available tools, whereas DSS tend to use more custom programming to deal with problems that may be unstructured.

DSS methodologies and tools originated largely in academia, whereas BI arose largely from the software industry. Many BI tools, such as data mining and predictive analysis, have come to be considered DSS tools as well.

Discuss the difficulties in implementing the concept of work systems in decision support.

A work system is “a system in which human participants and/or machines perform a business process using information, technology, and other resources to produce products and/or services for internal or external customers.” This is a very broad definition, encompassing nearly the entire scope of enterprise activity. Each work system typically has nine elements, covering everything from its underlying strategy down through its infrastructure and technology. The result is an extremely broad concept which, while it unquestionably covers everything that could possibly be supported as regards decision making, has expanded the field of managerial support so widely that it is difficult to use in a concrete, operational way. More research is needed before the work systems concept can be used as a practical guide to managerial activity.

13.

Discuss the major issues involved in implementing MSS. Issues arise at every stage of implementing a management support system. The first issue is justification and cost-benefit analysis. If a system is not justified on a cost-benefit basis, it should not be built, and none of the other issues matters. Justifying MSS systems is difficult, however, because many benefits of improved decision support are intangible.

18

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Once it has been decided to build a system, one must decide if it is to be purchased, leased, or built. Following each possible choice there are additional issues related to that option. For example, if it is decided to build it, the developers must choose among conventional programming languages, specialized decision support languages, or DSS development generators. Also in the development area, issues arise in integrating MSS components with each other and with external data sources such as databases, enterprise systems, and even the information systems of business partners.

A specific issue that arises in this context is the role the Web is to play in the DSS. Browsers and search engines are often integrated into MSS. Last, but far from least, proper attention to privacy and security is mandatory.

These are concerns with any computerized system, but especially so when the data in a system are of strategic value.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

Identify the medical and administrative decisions that the IHH system supports.

The case refers to decisions several times: • “What treatment or tests the patient needs.”

• “Decisions … made constantly by physicians, nurses, administrators, and other employees,” not further detailed, many of them presumably falling under the previous item as well but also including administrative decisions such as room assignments and personnel scheduling • “Financial and operational decisions,” again not further detailed, but presumably including financial decisions ranging from the strategic (“How should we finance the new building?”) through managerial (“How much should we pay a new director of nursing?”) to operational (“Should we write off the cost of this charity case?”).

• “Joint decisions” made by doctors and other employees, whose speed is improved by the hospital’s communications and collaboration systems. Presumably the decisions themselves fall into the above categories.

2.

Explain how the IHH system is able to reduce the time needed to make medical decisions.

Much of the time reduction comes from the hospital’s communication and collaboration system. It can reduce the time needed to make medical decisions by reducing the time it takes for doctors to consult with each other on their individual specialized aspects of a case, often eliminating the need for them to find a common time when they can all be in the same place or even a common time when they can all be on a telephone call.

3.

In your opinion, what administrative decisions that are not cited in the case can the IHH system expedite?

This is an open-ended answer based on the student’s personal experiences. Possible answers include room assignments, staff scheduling, medication and supply ordering,

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

19

billing policy decisions such as the minimum amount the hospital will agree to bill an insurer, operational billing decisions such as adjusting or canceling a bill under specific circumstances, selection of interns from the applicant pool, setting salary scales. In some cases, the system would provide specific support to the decision. In others, such as intern selection, the system’s communication and collaboration components would facilitate a joint decision.

4.

Explain why this is a DSS application. Identify its components—the data, models, and user interface cited in the case.

One definition of DSS from the text (Section 1.6) is: Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semistructured problems.

This is a DSS application because it is exactly that. It uses the computer to provide immediate access to medical records to facilitate medical decision making. This same information is used for other types of decisions as well. The problems are semistructured: some things are known about medical problems and their treatment, but each case presents individualized aspects as well.

The components of a DSS are given in that section as data, models, knowledge, users and the user interface. In this system those are as follows: • Data: Primarily the content of the medical records. Secondarily, information about available resources such as drug inventories, room availability, and operating room schedules.

• Models: Financial planning (Mezzia from VFA). There are presumably also patient care models in the system, such as drug interaction models, but those are not given in the case.

• Knowledge: Knowledge management is said (Section 1.6) to be an optional DSS component. This DSS does not have one, at least as far as we can tell from the information in the case.

• Users: “Physicians, nurses, administrators, and other employees”—in other words, just about everyone in the hospital except the patients themselves.

• User interface: There are several user interfaces here, each designed for a different task or user community. Those mentioned include one to bring a new patient’s medical records “on a screen in 15 seconds;” one to add tests to a patient’s medical record; a wireless tablet interface for doctor data access, entry and communication; the bedside electronic chart with both manual (nurse, physician) and automatic inputs; and bar-code scanners to track medications. In addition to these patient care system interfaces, there are interfaces to the Mezzia financial planning and analysis package and to the communications and collaboration system.

5.

What strategic advantage can IHH derive from its digitization?

Michael Porter has identified five competitive forces that organizations can leverage to obtain a strategic advantage. Although those are not discussed in this chapter, students may have studied them in other courses. Even if they have not, the forces provide a

20

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

convenient framework for possible answers to this question. The forces, and corresponding potential IHH strategic advantages, are: • Rivalry among competitors: Doctors and patients who know that IHH has reduced its medical error rate by 75 percent may choose it over other hospitals. (However, this example contains a possible logical fallacy. If the medical error rate at IHH was previously higher than at other area hospitals, it could still be higher after this reduction. It would be unlikely to find such a wide discrepancy, it would have to have been over four times worse to start with to still be worse after the reduction, but this is not impossible in theory.)

• Bargaining power of customers. Patients whose medical records are already in IHH’s electronic system may choose to return there rather than start anew at a different hospital.

• Bargaining power of suppliers. The ability of this system to support electronic reordering of drugs and other medical supplies implied in the statement that it eliminates delays in the supply chain, makes IHH a more desirable customer as it reduces errors, emergency orders, and other supplier “hassle factors.” This makes suppliers more likely to provide it with favorable terms or treatment relative to their other hospital customers.

• Increased barriers to entry. To the extent that systems such as this become the norm in cardiac hospitals in the region, it will be more expensive to go into competition with IHH and others already in that field.

• Threat of substitute products. The only substitute for treatment at a specialized cardiac hospital is treatment at another type of medical facility. (Not treating a cardiac condition is usually not a viable option.) To the extent that this system allows the specialized facilities of IHH to be applied more effectively, such substitutes become less attractive.

Many other correct answers are possible. It is not necessary to use Porter’s framework to answer this question, though if you know your students have studied it in other courses (it, or something much like it, is a staple of most introductory MIS courses) asking them to use it here may help show the unity of the field.

CHAPTER

2

Decision Making, Systems, Modeling, and Support

Learning Objectives for Part I ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the conceptual foundations of decision making Understand Simon’s four phases of decision making: intelligence, design, choice, and implementation Understand the concept of rationality and its impact on decision making Understand the foundations, definitions, and capabilities of decision support systems (DSS) and business intelligence (BI) Describe DSS components and technology levels Describe the various types of DSS and explain their use Explain the importance of databases and database management Explain the importance of models and model management

Learning Objectives for Chapter 2 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the conceptual foundations of decision making Understand Simon’s four phases of decision making: intelligence, design, choice, and implementation Recognize the concepts of rationality and bounded rationality and how they relate to decision making Differentiate between the concepts of making a choice and establishing a principle of choice Learn how DSS support for decision making can be provided in practice Understand the systems approach (described in Online File W2.2)

21

22

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

CHAPTER OVERVIEW This chapter covers two fundamental topics: How people make decisions, and how information systems can support people in making decisions. Understanding both of these is essential if the student is to be able to develop information systems whose purpose is to help people make decisions.

CHAPTER OUTLINE 2.1

OPENING VIGNETTE: DECISION MAKING AT THE U.S. FEDERAL RESERVE  Questions for the Opening Vignette A. WHAT WE CAN LEARN FROM THIS VIGNETTE

2.2

DECISION MAKING: INTRODUCTION AND DEFINITIONS A. CHARACTERISTICS OF DECISION MAKING B. A WORKING DEFINITION OF DECISION MAKING C. DECISION MAKING AND PROBLEM SOLVING D. DECISION-MAKING DISCIPLINES E. DECISION STYLE AND DECISION MAKERS 1.

Decision Style 2.

Decision Makers  Section 2.2 Review Questions

2.3

MODELS A. ICONIC (SCALE) MODELS B. ANALOG MODELS C. MENTAL MODELS D. MATHEMATICAL (QUANTITATIVE) MODELS E. THE BENEFITS OF MODELS  Section 2.3 Review Questions

2.4

PHASES OF THE DECISION-MAKING PROCESS  Section 2.4 Review Questions

2.5

DECISION MAKING: THE INTELLIGENCE PHASE A. PROBLEM (OR OPPORTUNITY) IDENTIFICATION B. PROBLEM CLASSIFICATION C. PROBLEM DECOMPOSITION D. PROBLEM OWNERSHIP  Section 2.5 Review Questions

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

23

2.6

DECISION MAKING: THE DESIGN PHASE A. SELECTION OF A PRINCIPLE OF CHOICE B. NORMATIVE MODELS ⧫ Technology Insights 2.1: The Difference Between a Criterion and a Constraint ⧫ Technology Insights 2.2: Are Decision Makers Really Rational? ⧫ Application Case 2.3: Decision Making Between a Rock and a Hard Place; or What Can You Do When There are No Good or Even Feasible Alternatives?

C. SUBOPTIMIZATION D. DESCRIPTIVE MODELS E. GOOD ENOUGH OR SATISFICING ⧫ Application Case 2.4: Decision Making from the Gut: When Intuition Can Fail F. DEVELOPING (GENERATING) ALTERNATIVES ⧫ Technology Insights 2.5: Some Reasons for Bad Decision Making ⧫ Application Case 2.6: Too Many Alternatives Spoils the Broth G. MEASURING OUTCOMES H. RISK I. SCENARIOS J. POSSIBLE SCENARIOS K. ERRORS IN DECISION MAKING  Section 2.6 Review Questions

2.7

DECISION MAKING: THE CHOICE PHASE  Section 2.7 Review Questions

2.8

DECISION MAKING: THE IMPLEMENTATION PHASE  Section 2.8 Review Questions

2.9

HOW DECISIONS ARE SUPPORTED A. SUPPORT FOR THE INTELLIGENCE PHASE ⧫ Application Case 2.7: Union Pacific Railroad: If You’re Collecting Data, Use It Profitably!

B. SUPPORT FOR THE DESIGN PHASE ⧫ Decision Making in the Digital Age C. SUPPORT FOR THE CHOICE PHASE D. SUPPORT FOR THE IMPLEMENTATION PHASE E. NEW TECHNOLOGY SUPPORT FOR DECISION MAKING ⧫ Application Case 2.8: Advanced Technology for Museums: RFID Makes Art Come Alive  Section 2.9 Review Questions

2.10

RESOURCES, LINKS AND THE TERADATA UNIVERSITY NETWORK CONNECTION A. RESOURCES AND LINKS

24

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

B. C. D. E. F. G.

CASES MILLER’S MIS CASES VENDORS, PRODUCTS AND DEMOS PERIODICALS ADDITIONAL REFERENCES THE TERADATA UNIVERSITY CONNECTION

Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises ⧫ End of Chapter Application Case: Strategic Decision Making In the Pharmaceutical Industry: How Bayer Decides Whether or Not to Develop a New Drug  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

This chapter has two major themes: (a) how decisions are made, and (b) how the decisionmaking process (and hence the people who make them) can be supported.

Decision making is the subject of Section 2.2 and Sections 2.4 through 2.8. Support is covered in Sections 2.3 and 2.9. (Section 2.1, the introductory vignette, and Section 2.10, resources and links, apply equally to both.) Some instructors will find the order in the text best, since it covers the types of models used in DSS/BI in Section 2.3 before they come up in the sections that follow. Others will find the intuitive understanding most students have of models sufficient for Sections 2.4–2.8 and will prefer to cover decision making first, support for it second. Either approach can work well.

It is important to stress the relevance of decision-making methods to DSS/BI in covering this chapter. The key reason is in the word “support” in the term DSS. We are discussing systems that support people who make decisions, not systems that make decisions on their own. People who make business decisions are often high enough in the organization to have choices as to how they make their decisions, so it is important to support decision-making methods and styles that they are willing to use.

One way to view this area, which ties into the discussion of systems in Online File W2.2on systems, is to consider the decision-making system as a whole as consisting of human and automated subsystems. Inputs to the overall system are external and internal (as seen by the organization) data sources and decision requirements. Output is a decision. Internally, the two subsystems communicate in a manner determined by the designers and developers of the automated subsystem. If this is not a suitable interface for the human subsystem, the overall decision-making system will not work well. DSS/BI system designers must see themselves as subsystem designers, where they have limited control over the other major subsystem in the

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

25

system they are working on, and where they must not suboptimize the automated subsystem at the expense of the overall system. (Suboptimization is covered in this chapter too, in Section 2.6.)

Understanding the phases of decision making is important in developing automated support, as the kind of support needed depends on the decision phase. Teachers should recognize that this subject is taught in several places in the typical business school curriculum, not always from the same point of view. Some instructors draw a strong distinction between decision making and problem solving, whereas this book considers them nearly equivalent. Some instructors consider monitoring to be a fifth phase of the process, whereas this book considers it as the intelligence phase of the next decision. It is not necessary to be dogmatic about one version of the phases versus another. It can help, however, to determine where else these concepts are taught at your institution and how they are approached there.

In Section 2.3, this book groups simulation models and other types of mathematical models, such as linear programming, as “mathematical (quantitative) models.” Students may have been taught a distinction between the two in other courses such as operations management.

You may want to recognize this as being beyond the level of breakdown needed for the purpose of this chapter, but at the same time as valid when a finer classification of models is required.

Section 2.9, “How Decisions Are Supported,” gives a wide range of support possibilities for each phase. In teaching this section, which is critical to students’ careers because it tells them what to look for in specific situations, it can help to add perspective to the lists in each subsection by indicating which of the listed tools are more important in each phase, which are less so. For example, expert systems are listed as being able to support all four of the decision phases. Although this is correct, you can add perspective by pointing out (in this case) that they are most useful in the choice phase, secondarily in design and implementation, and relatively less useful in the intelligence stage of a decision.

Finally, students may have to be reminded (or told for the first time!) that “criterion” is a singular noun whose plural is “criteria.” Saying “The single most important decision criteria is …” is incorrect. Since managers may obtain subconscious clues to a junior staff member’s or job applicant’s competence from his or her ability to use business terms correctly, it’s important for them to learn correct usage when this term comes up in Section 2.4.

26

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 2.1 Review Questions 1.

What is the primary decision that Alan Greenspan makes?

His primary decision involves whether to raise or lower interest rates.

2.

Why are Greenspan’s decisions so important?

Because they affect the flow of billions of dollars between banks and countries, affect corporate profits in a major way, and impact millions of jobs.

3.

How does Greenspan actually make decisions? How does he utilize technology in making decisions? How does he involve others in the decision-making process?

Greenspan makes decisions by attempting to predict the effect of different interest rate choices on the U.S. economy and choosing the interest rate that will, he feels, lead to the best results. He utilizes technology to make these predictions for different scenarios. He involves others in three ways: to provide him with input (Federal Reserve Board governors and regional bank presidents, leaders of important sectors of the U.S. economy, etc.); to analyze the information; and (FRB governors) to vote on the decision.

4.

What information sources does Greenspan use in his decision making? Statistical indicators and raw data describing the U.S. economy • • • • • •

5.

Reports of Federal Reserve Board governors, presidents of Federal Reserve Banks and other major banking institutions Real-time information, including anecdotal insights from leaders of important sectors of the U.S. economy Economic analyses from 255 economists plus those at the twelve Federal Reserve districts (total of about 500 economists) Reports on gross domestic product, consumer prices and other economic indicators Personalized tales from key economic hubs Perhaps other information sources not mentioned in the case of which students may be aware

Why are so many analysts needed to sift through the data and create and run models of the economy for decision making?

This is due to the large number of data sources, the large number of models used to analyze the data, and the complexity of both the data and the models.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

6.

27

How can executives and managers at a national or multinational company (e.g., WalMart) utilize a procedure like that used at the U.S. Federal Reserve? Answer the question in the context of the kinds of decisions made by such firms. (If you like, refer to Duvall and Nash, 2005, directly.)

Although companies do not set interest rates, they make business moves based on expectations of future trends. They could use a similar procedure on a smaller scale to forecast economic activity in sectors that will affect their business—such as, in WalMart’s case, exchange rates between the U.S. dollar and the currencies of countries from which Wal-Mart obtains many of the products it sells.

Students who read the cited reference will find the following paragraph near the end. It leads to a similar, though not identical, and equally valid answer: Such a comprehensive and malleable model of the economy and what it means could, in theory, allow any company to finely tune how it deals with its customers, by creating more or fewer goods and services to provide them; its suppliers, by choking down or opening up orders for more materials; and its stockholders, by more accurately forecasting performance and reducing surprises on the bottom line that could damage the price of its shares.

Section 2.2 Review Questions 1.

What are the various aspects of decision making?

Aspects of decision making that are important to understand if we are to develop effective computer support include characteristics, phases and how they relate to problem solving; supporting disciplines, styles and how they relate to the personal characteristics of the decision maker, and the nature of group involvement in the decision (if any).

2.

Why is decision making so complex in today’s business environment?

In a nutshell, because today’s business environment is complex. Specific reasons for its complexity include the existence of groupthink, which can lead to bad decisions; decision makers’ interest in evaluating what-if scenarios; the fact that experimentation with the real system (trial-and-error) may result in failure, as well as being possible only for one set of conditions at a time and being potentially disastrous; the fact that changes in the decision-making environment may occur continuously, leading to invalidating assumptions about the situation; time pressure from these same changes in the decisionmaking environment may affect decision quality by imposing time pressure on the decision maker; the cost and expense of collecting information and analyzing a problem, with the difficulty of determining when to stop and make a decision; possible lack of sufficient information to make an intelligent decision; and conversely the possible availability of too much information (information overload).

3.

Identify similarities and differences between individual versus group decision making.

•

Individual decision makers need access to data and to experts who can provide advice, while groups additionally need collaboration tools.

28

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• •

•

There are often conflicting objectives in a group decision-making setting, but not in an individual setting.

Groups can be of variable size and may include people from different departments or from different organizations. Collaborating individuals may therefore have different cognitive styles, personality types, and decision styles. Some clash, whereas others are mutually enhancing.

Consensus can be a difficult political problem.

For these and similar reasons, group decision making can be more complicated than individual decision making.

4.

Compare decision making versus problem solving. Determine whether or not it makes sense to distinguish the two from one another.

It is a matter of definition. Some people consider decision making as the first three steps in problem solving. Others use the terms interchangeably. Those who distinguish between them consider decision making to be the process of making a recommendation, whereas problem solving includes the implementation of the recommendation (and perhaps monitoring its effects to determine whether or not the problem has been solved).

As experts on the subject disagree on whether or not it makes sense to distinguish between the two concepts, there is no single correct answer to the second part of this question.

5.

Define decision style and describe why it is important to consider in the decision-making process.

Decision style is the manner in which a decision maker thinks and reacts to problems. It is important to consider it because different decision styles require different types of support.

Section 2.3 Review Questions 1.

Describe the different categories of models.

Categories of models that can be useful in business include iconic (scale, physical) models, analog models, mental models, and mathematical (quantitative) models. Other types of models, such as fashion models or data models as used in system analysis and design, are not relevant to this context but share the underlying concept of representing some aspect of a real system, having advantages over it for a specific purpose and lacking features that would permit them to replace it.

(See also the note on types of models in the “Teaching Tips” section above.)

2.

How can mathematical models provide the benefits listed in the section?

The benefits listed in this section, with the applicability of mathematical models to each, are: • Model manipulation (changing decision variables or the environment) is much easier than manipulating the real system. Experimentation is easier and does not

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

•

•

• •

•

•

• •

3.

29

interfere with the daily operation of the organization. Mathematical models describe their parameters in the form of numbers on paper or data in a computer, which can be changed easily without affecting the operation of the real system.

Models enable the compression of time. Years of operations can be simulated in minutes or seconds of computer time. The second part of this statement is specifically about mathematical models. However, other types of models enable the compression of time as well. For example, a mental model of which says “if I eat this food, I’ll get an allergic reaction” leads to the decision to avoid it far more quickly, as well as less painfully, than tasting it would.

The cost of modeling analysis is much less than the cost of a similar experiment conducted on a real system. This is because a model is typically less expensive than a real system, the compression of time reduces time-related costs such as personnel, and the business does not run the risk of impaired operations while alternatives are investigated in the model.

The cost of making mistakes during a trial-and-error experiment is much less when models are used rather than real systems. This is because mistakes affect only the model, not the real system.

The business environment involves considerable uncertainty. With modeling, a manager can estimate the risks resulting from specific actions. Mathematical models allow a decision maker to vary parameters (such as inflation rates, oil prices or demand growth) over as wide a range as desired to reflect all future scenarios of interest.

Mathematical models enable the analysis of a very large, sometimes infinite, number of possible solutions. Even in simple problems, managers often have a large number of alternatives from which to choose. This benefit is explicitly about mathematical models.

Models enhance and reinforce learning and training. The operation of a mathematical model can be observed and reviewed, variables changed to see the effects of the change, and mistakes deliberately made to learn how to recover from them.

Models and solution methods are readily available over the Web. Most models available over the Web are mathematical models.

There are many Java applets (and other Web programs) that readily solve models.

This is true of mathematical models, but generally not of other types.

How can mental models be utilized in decision making involving many qualitative factors?

Mental models, which are typically used when a decision involves mostly qualitative factors, can help frame the decision-making situation and can work through scenarios to consider the risks and benefits of alternative decisions.

4.

How can modern IT tools help synthesize qualitative and quantitative factors in decision making? (Hint: See Online File 2.1.1) Modern information technology tools can present qualitative along with its analysis of quantitative factors, so decision makers can consider both together and use the qualitative

30

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

information to guide them to the most useful quantitative analyses. The MMS Rent-ACar case in the online files gives several examples of this.

Section 2.4 Review Questions 1.

List and briefly describe Simon’s four phases of decision making.

Simon’s four phases of decision making are intelligence, design, choice, and implementation.

Intelligence consists of gathering information by examining reality, then identifying and defining the problem. In this phase problem ownership should also be established.

Design consists of determining alternatives and evaluating them. If the evaluation will require construction of a model, that is done in this phase as well.

The choice phase consists of selecting a tentative solution and testing its validity.

Implementation of the decision consists of putting the selected solution into effect.

2.

Why is a fifth phase, evaluation, not necessary?

The authors of this book view monitoring as a manifestation of the intelligence phase, applied to the implementation of a decision. Like any other intelligence phase, it may lead to a future decision.

In evaluating responses to this question, graders should be aware that students may have learned to treat evaluation, or monitoring, as a separate phase in other courses or from other sources.

3.

What can cause a problem to exist in decision making?

A problem is a negative discrepancy between what is and what a decision maker feels should be. This can result from a change in the decision maker’s expectations or from a change in the situation. Expectations can be raised, creating such a discrepancy, by (for example) seeing how competitors have improved their operations. The situation can change in many ways: higher costs, lower market share, etc.

Section 2.5 Review Questions 1.

Explain the differences and similarities between problems and opportunities.

A problem is defined in the text as the result of dissatisfaction with the status quo. An opportunity is a recognition that the status quo, while satisfactory, can perhaps be improved upon. The difference is to some extent one of degree, to some extent one of urgency: all non-trivial problems must be dealt with, while one can afford to be selective in choosing which opportunities to pursue. (If a business regularly chooses not to pursue opportunities, it will soon find that its competitors have pursued them and its position has worsened. At that point, it has a problem.)

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

2.

31

What is the difference between a problem and its symptoms?

Problems arise out of dissatisfaction with the way things are going. It is the result of a difference between what we desire and what is (or is not) happening. A symptom is how a problem manifests itself.

A familiar personal example is a high temperature (symptom) and an illness (problem). It is necessary to diagnose and treat the underlying illness. Attempting to relieve the temperature works if the illness is one which the body’s defenses can cure, but can be disastrous in other situations.

A business example: high prices (problem) and high unsold inventory level (symptom). Another is quality variance in a product (symptom) and poorly calibrated or worn-out manufacturing equipment (problem).

3.

Why is it important to classify a problem?

Classifying a problem enables decision makers to use tools that have been developed to deal with problems in that category, perhaps even including a standard solution approach.

4.

What is meant by problem decomposition?

Problem decomposition means dividing a complex problem into subproblems. This helps solve the problem because the smaller subproblems may be simpler to solve; some of the smaller problems may be structured, facilitating their solution, even though the overall problem as a whole is not; solving the “easy” subproblems enables decision makers to focus their attention on the remaining “difficult” parts; and decomposition may facilitate communication among decision makers.

5.

Why is establishing problem ownership so important in the decision-making process?

Problem ownership means having the authority, and taking the responsibility, of solving it. Lack of problem ownership means either that someone is not doing his or her job, or that the problem at hand has yet to be identified as belonging to anyone. In either case, it cannot be solved until someone owns it.

Section 2.6 Review Questions 1.

Define optimization and contrast it with suboptimization.

Optimization refers to the “best.” (There is no such thing as “more” optimal!) To achieve it, all alternatives must be considered, and the optimal one must be the best.

Suboptimization is the optimization of a subsystem, without considering its impacts on other parts of the overall system. What is optimal for a part of a system (or organization) may not be for the entire system (or organization).

For example, a student spending all 24 final exam study hours on DSS may give him/her the best possible grade in that course, but his/her overall average could be better served by studying six hours for each of four exams. The drop from A+ to B in the DSS course would be more than offset by the improvement from Ds to Bs and Cs in the other three.

32

2.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Compare the normative and descriptive approaches to decision making.

Normative decision making uses models, or methods that have perhaps previously been derived from models that tell a decision maker what he or she should do. These prescriptive models are often developed by utilizing optimization methods. The technology of expert systems, which will be discussed later in the book but which students may have seen elsewhere, derives them from the “rules of thumb” used by recognized experts in the field of the decision.

Descriptive decision making uses models that tell a decision maker “what-if.” These are usually simulation models.

3.

Define rational decision making. What does it really mean to be a rational decision maker?

Rational decision making follows the economic assumptions of rationality. A rational decision maker exhibits certain assumed behaviors: (1) Humans are economic beings, whose objective is to maximize the attainment of goals; (2) for a decision-making situation, all viable alternative courses of action and their consequences, or at least the probability and the values of the consequences are known; and (3) decision makers have an order or preference that enables them to rank the desirability of all consequences of the analysis (best to worst).

Being a rational decision maker means making decisions according to these assumptions.

4.

Why do people exhibit bounded rationality when problem solving?

Humans in general have limitations that prevent us from being completely rational. We usually simplify things. Individuals’ evaluation scales for the costs and benefits of a decision may be nonlinear and may not follow those of the organization. (For example, a manager may see great personal benefit in not exceeding a budget by even $1, but very little benefit by coming under it by more than a minimal amount. The view from the executive suite would not have such a sharp cut-off. Economic utility theory addresses this area.) Also, individual characteristics may result in a restricted rationality.

5.

Define scenario. How is a scenario used in decision making?

A scenario is a statement of assumptions about the operating environment of a particular system in a given time. It describes the system's configurations. By changing scenarios and measuring the goal attainment level, it is possible to compare alternatives under different sets of conditions.

6.

Some “errors” in decision making can be attributed to the notion of decision making from the gut. Explain what is meant by this and how such errors can happen.

In general, people have a tendency to measure uncertainty and risk badly. They tend to be overconfident and have an illusion of control in decision making. As a result, decisions in which some part of the future is unknown, which is true of most business decisions, are often made more optimistically than they should be.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

33

In addition, those who decide “from the gut” often do not develop a clear picture of all the details and implications of a situation. This approach can save time if a decision maker’s intuition is attuned to a situation from extensive experience, but can lead to errors if it is applied to unfamiliar situations. An executive who has made successful “gut” decisions in the past may not even recognize that his or her experience does not apply to a new, but superficially similar, situation.

Section 2.7 Review Questions 1.

Explain the difference between a principle of choice and the actual choice phase of decision making.

A principle of choice is a criterion used to describe the acceptability of a solution approach. In other words, it is a basis for deciding whether one approach or another is superior. A principle of choice is general: that is, it applies to many possible decisionmaking situations.

The choice phase of decision making uses one or more principles of choice, chosen during this decision phase or prior to it, to select an alternative in a specific situation.

2.

Why do some people claim that the choice phase is the point in time when a decision is really made?

Because, in a sense, it is. The decision, choosing one of the available alternatives, is made during this stage. It is, therefore, easy to equate the two.

However, the choice phase as the term is usually used covers more than this single point in time. It also includes the comparisons that lead up to it and the assessment of robustness and possible adverse consequences that may lead a decision maker to choose an alternative that is less desirable under ideal conditions but also less likely to lead to disaster in other circumstances.

3.

How can sensitivity analysis help in the choice phase?

Sensitivity analysis determines how an alternative responds to small changes in the input parameters. An alternative that appears best for the nominal set of parameters might produce far worse results for small changes in them. Another alternative, which might not be as attractive if all parameters have their nominal values, might be more robust—that is, it might not degrade as much if they depart from those values. It might therefore be a better choice in practice, since some statistical variation in these values is only to be expected.

Section 2.8 Review Questions 1.

Define implementation.

Implementation is defined as the initiation of a new order of things, the introduction of change; putting a recommended solution to work.

34

2.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

How can a DSS support the implementation of a decision?

A DSS supports the implementation of a decision through communication, explanation, and justification. In a financial decision, for example, a DSS would include not only the detailed financial goals and cash needs for the near term, but would also provide the calculations, intermediate results, and statistics used to determine the aggregate figures. It also conveys to subordinates that the decision maker has thought through the assumptions behind as decision’s goals and is serious about their importance. Finally, it allows people to explain and justify their suggestions and opinions with graphical support.

Section 2.9 Review Questions 1.

Describe how DSS/BI technologies and tools can aid in each phase of decision making.

Intelligence phase: The primary requirement of decision support for the intelligence phase is the ability to scan external and internal information sources for opportunities and problems and to interpret what the scanning discovers. Web tools and sources are extremely useful for environmental scanning.

Decision support/business intelligence technologies can also help. (Automatic) data mining and (manual) online analytic processing support this phase by identifying relationships among activities and other factors. Geographic information systems (GIS) can be utilized either as stand alone or integrated with these systems, so that a decision maker can determine opportunities and problems in a spatial sense.

Another aspect of identifying internal problems and opportunities is monitoring operations. Business activity monitoring, business process management and product lifecycle management provide such capability. Routine and ad-hoc reports can also help: regular reports can be designed to assist in problem finding by comparing expectations with current and projected performance.

Design phase: This phase involves generating alternative courses of action, agreeing on choice criteria and their weights, and forecasting the consequences of various alternatives. Several of these activities can use standard models such as financial and forecasting models. Either standard or special models can generate alternatives for structured problems. OLAP and data mining software are useful in identifying relationships to use in such models. An expert system can assist with qualitative methods as well as with the expertise required in selecting quantitative analysis and forecasting models.

A knowledge management system, if available, can be consulted to determine whether such a problem has been encountered before, or if there are experts on hand to provide quick understanding and answers. CRM systems, revenue management systems, ERP, and SCM software are useful in providing models of business processes that can test assumptions and scenarios. If a problem requires brainstorming to help identify important issues and options, a group DSS may prove helpful.

Choice phase: In addition to providing models that rapidly identify a best or good enough alternative, a DSS can support the choice phase through what-if and goal-seeking analyses. Different scenarios can be tested for the selected option to reinforce the final decision. A knowledge management system helps identify similar past experiences;

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

35

CRM, ERP, and SCM systems can test the impact of each choice. If a group makes the decision, a group support system can provide support to lead to consensus.

Implementation phase: DSS can be used in implementation activities such as decision communication, explanation, and justification. Implementation phase DSS benefits are partly due to the vividness and detail of analyses and reports used for these purposes.

All phases of the decision-making process can be supported by improved communication by collaborative computing through GSS and KMS. Computerized systems can facilitate communication by helping people explain and justify their suggestions and opinions.

Decision implementation can also be supported by expert systems. An ES can be used as an advisory system regarding implementation problems (such as handling resistance to change). Finally, an ES can provide training that may smooth the course of implementation.

Impacts along the value chain are typically identified by BAM, BPM, SCM, and ERP systems. CRM systems report and update internal records based on the impacts of the implementation. These inputs are then used to identify new problems and opportunities—a return to the intelligence phase.

(Note to graders: The text provides additional examples.)

2.

Describe how new technologies can provide decision-making support.

With the development of mobile commerce (m-commerce), more and more personal devices (personal digital assistants, cell phones, tablet computers, laptop computers) can access information sources, and users can respond to systems with information updates, collaboration efforts, and decisions. This can help salespeople, for example, to be more effective by accessing their CRM while on the road. Constant access to corporate data, inventory and otherwise, can only help them in their work. Wireless devices are taking on greater importance in the enterprise, generally by accessing specialized Web servers that provide data and communication directly to the m-commerce device.

Section 2.10 Review Questions (This section has no review questions.)

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Why is intuition still an important aspect of decision making?

Intuition is a critical factor that decision makers use in solving unstructured and semistructured problems. The best decision makers recognize the tradeoff between the marginal cost of obtaining further information and analysis versus the benefit of making a better decision. (General Patton’s adage, “A good decision today is better than a perfect decision tomorrow,” reflects the application of this principle to his situation.) When decisions must be made quickly, the intuition of a seasoned, excellent decision maker is

36

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

called for ideally. When an inexperienced or ill-trained decision maker attempts to use intuition, however, disaster can strike.

(See the related answer to Review Question 6 of Section 2.6 above.)

2.

Define efficiency, define effectiveness, and compare and contrast the two.

Efficiency refers to the proper utilization of resources, doing things right.

Effectiveness refers to the attainment of goals, doing the right things.

Driving from New York City to Boston by way of Chicago in a hybrid compact that gets 50 miles per gallon (21 km/liter) may be efficient, but is not effective (in terms of reaching the goal, Boston, quickly).

Driving directly, but alone in a large vehicle that gets 12 mpg (5 km/l), may be effective but is not efficient as it uses four times the resources—in some sense, wasting 75 percent of them.

Ideally, one should be both effective and efficient, but if that is not possible a decision maker should focus on effectiveness.

3.

Why is it important to focus on the effectiveness of a decision, not necessarily the efficiency of making a decision?

The effectiveness of a decision affects an organization for as long as the decision matters, and as broadly as the decision scope. The efficiency of making the decision affects only the decision-making process itself. Since the resources involved in making a decision are far less than those affected by its implementation, it is more important to make sure the decision is correct than to save resources used in making it.

4.

What are some of the measures of effectiveness in a toy manufacturing plant, a restaurant, an educational institution, and the U.S. Congress?

Toy manufacturing plant: Cost per unit produced, percent of orders delivered on time, quality of finished product (perhaps measured by incidence of warranty claims).

Restaurant: Revenue per table, rate of return on investment, rating by professional reviewers, rating by local health inspection authorities.

Educational institution: Students’ performance on national tests, amount of funded research per faculty member, percentage of students successfully finishing and receiving degrees, success of athletic teams.

U.S. Congress: Number of bills passed, public image.

Since these are not the only measures for any of the examples, other correct answers are possible.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

5.

37

Even though implementation of a decision involves change, and change management is very difficult, explain how change management has not changed very much in thousands of years. Use specific examples throughout history.

Moses, while attempting to lead the children of Israel away from idol worship, found them rebellious and often desiring to return to slavery in Egypt rather than face the unknown of the desert. When he left them for a few days, they reverted to idolatry with the Golden Calf.

Many of those living in England’s North American colonies in the 1770s preferred the security and known economic benefits of that form of government to the possible benefits, but unknown risks, of independence.

Railroads of the 1950s and 1960s could not adapt to the changes required to see themselves as in the transportation business and therefore lost most of their passengers to airlines and buses.

U.S. automobile companies in the 1970s did not change their products from the large cars, built and bought with the expectation of being traded in frequently, that they were used to. As a result, they lost market share to foreign firms, whose cars offered higher quality and better fuel economy.

Of course, many other examples are possible.

6.

Despite the advances in ADM methods and tools, why should we still insist that a human being still be responsible for every decision?

Advances are not perfection. Decision-making tools have their limitations and often cannot detect when they have reached their limits or when a factor that was not programmed into them affects the answer. A combination of human intuition and automated support usually yields the best decisions.

Some fully structured decisions can be made without human intervention, such as a decision as to how much to withhold from a pay check for income taxes. These hardly qualify as “decisions,” however. Moreover, even in those cases, the overall system should include an opportunity for some type of human review at some point in the process to guard against software bugs or other errors. The need for such review increases with the criticality and finality of the decision.

7.

Why should all information system development projects focus on some aspect of decision making in practice?

Because information, if it is not used to make decisions, is not really useful. Focusing on how information can be used to make decisions helps any IS development project focus its resources on the most important areas.

(The decision for which information is needed may not be obvious. For example, a reporting system may have to conform to government financial accounting requirements. From the standpoint of the IS developer within a company, there is no decision here. However, while the requirements may be fixed in a practical sense to that person, they ultimately reflect someone’s potential need to make a decision on the basis of the reported information.)

38

8.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Your company is considering opening a branch in China. List typical activities in each phase of the decision (intelligence, design, choice, implementation) whether to open or not open.

Intelligence: Should we invest in China? What are the possible advantages, disadvantages, risks? What resources would have to be diverted from other activities?

When could we start?

Design: Select criteria for assessing the alternatives (e.g., the ROI, market share); create alternatives: invest now, invest later, do not invest; levels and timing of investment; information flow for decision making; prepare a model; how will the choice be made, by whom, and when?

Choice: Get information; final evaluation; sensitivity analysis; solve model.

Implementation: Prepare an implementation plan; deal with resistance to change and necessary approvals and authorizations; conduct training; transfer resources.

These are not the only possible activities for any of the phases; many other correct answers are possible.

9.

You are about to sell your car. What principles of choice are you most likely to use in deciding whether to offer or reject offers? Why? Satisficing. You cannot optimize. The sequential nature of the coming offers makes it a typical situation in which you set an aspiration level (say $3500). You should accept the first offer that meets your aspiration level. (Others suggest using the first offer to calibrate the market, then accepting the first one that exceeds it, or other approaches—but they are all satisficing.)

10.

You are about to buy a car. Using Simon’s four-phase model, describe your activities at each step.

Intelligence: You recognize that your needs could be better served by a different car, due to a change in your present car (decreasing reliability, growing repair bills) or your situation (changed transportation needs, more money).

Design: Determine parameters that describe the appropriate car to buy. Also determine the criteria (objective function) that indicates the value of certain features in a car. From the multiple criteria, a principle of choice should be developed. The alternatives must be identified as well. This is a multiple-criteria problem. In assessing alternatives, it may be necessary to visit dealers to drive candidate cars and determine actual selling prices.

Choice: Choose the car.

Implementation: Buy the car.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

11.

39

The use of scenarios is popular in computerized decision making. Why? For what types of decisions is this technique most appropriate?

Scenarios help decision makers experiment with different settings, which helps in determining the robustness of a decision in a variety of environments. This is appropriate for decision making under risk.

12.

Explain, through an example, the support given to decision makers by computers in each phase of the decision process.

Intelligence: A computer can scan a large amount of information to identify problems such as a decline in sales.

Design: To boost sales, several advertising proposals may be evaluated. A computerized model that shows the cost of an advertising program and its expected effect on sales can help a decision maker assess their potential.

Choice: Once alternatives are identified and their potential effect forecasted, a choice can be made. The computer can rank choices by cost/benefit ratio, net benefit or any other desired criterion.

Implementation: A detailed “what-if” analysis can convince management that the proposed alternative is indeed the best. Computer-generated graphics can ease the implementation process.

13.

Some experts believe that the major contribution of DSS is to the implementation of a decision. Why is this so?

Proposed solutions are no good unless they are implemented. Implementation in many cases is the most difficult phase of decision making. However, a specific DSS may be designed to help with any decision phase or phases, in which case the contribution of that DSS is to those phases.

14.

Most managers are capable of using the telephone without understanding or even considering the electrical and magnetic theories involved. Why is it necessary for managers to understand MSS tools to use them wisely?

It is important because the design of an MSS must be optimized for its use in the way a telephone does not. It must reflect specific user needs if it is to be useful. MSS developers, no matter how technically competent they are, cannot be expected to understand these as well as needed.

Also, the potential for disaster is high if a manager attempts to apply an inappropriate MSS technology to a problem (e.g., a neural network when an expert system is appropriate).

(By the way, (a) the folks who specify the features of a complex office telephone system have to know a good deal about them too, and (b) managers don’t need to understand the electrical and magnetic theories behind computers, either.)

40

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS

1.

   

Describe the different areas that must be considered when bringing a new drug to market.

These areas include: • The safety of the proposed drug • Its effectiveness • The cost of developing it, including obtaining regulatory approval • The feasibility of its production process, including packaging • The market for it, including potential competition Students may choose to “describe” these areas (as requested in the question) as they see fit.

2.

In this case, how was information about risk gathered and then used?

Information about risks was gathered through meeting with experts. It was used to adjust estimates of the drug’s profitability.

3.

Does it make sense to use net present value as a measure of success? Explain why or why not.

If the purpose of a corporation is seen as returning value to its shareholders, if financial return is therefore the only consideration, and if all the factors that go into evaluating the impact of a decision on net corporate ROI can be quantified, then this is the correct approach. (Risk, which is present in the typical business situation and was important in this one, can be factored into an ROI analysis either by adjusting the predicted ROI for risk factors—as was done here—or by using risk-based statistical distributions for costs and benefits, resulting in a statistical distribution of expected ROIs.) As a practical matter, when considering single projects in isolation and with nonfinancial factors being approximately equal, it provides a good basis of comparison.

Conceptually, however, the ROI approach is flawed primarily because it ignores factors that cannot be expressed in financial terms. For example, a proposed new drug may cure diseases that are highly correlated with low income due to factors such as poor nutrition or sanitation. The pharmaceutical firm’s assessment of the social contribution of developing such a drug, or even its assessment of the PR value of developing it, may outweigh a higher predicted ROI for a drug that will benefit primarily higher-income individuals.

4.

Why is it important in sequential decision making to reevaluate for a go/no go decision?

In sequential decision making the situation is reassessed at intermediate points to determine whether or not to proceed further. At that point costs that have been incurred in previous phases cannot be recovered and should not be considered. Future costs and expected benefits must be considered in order to decide whether or not to proceed. This can only be done if the project is reevaluated in light of current knowledge.

(DSS/BI students will typically have studied the standard SDLC (System, or Software, Development Life Cycle) in prior courses. It provides an example of sequential decision making with its successive steps of initial proposal evaluation, feasibility

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

41

analysis, systems analysis, etc. At the end of each stage the firm may decide to cancel the project or proceed to the next.)

5.

It is estimated that IT can have the most impact on Phase 3 of human testing. Investigate why this is the case. (Hint: Check the ComputerWorld article).

This is the case because Phase 3 is by far the longest of the phases, typically two to four years but sometimes as long as seven years. A given percentage time saving is therefore leveraged for the greatest impact on the overall schedule.

(The case does not give the URL of the ComputerWorld article. It is located at: http://www.computerworld.com/action/article.do?command=viewArticleBasic&articleId= 76907 . Students should be able to locate it using the information supplied in its citation.)

6.

Explain the similarities and differences between the decision that Bayer and other major pharmaceutical firms face in new drug development to those of a major motion picture studio considering developing a film.

Both involve a series of “go, no go” decisions about a major project that will not bear fruit (if at all) for several years and which will cost, relative to the firm’s resources, a great deal of money, so that it is impossible to undertake a large number of concurrent projects. Both depend on the arrival of a core idea that cannot be predicted: a pharmaceutical discovery on the one hand, a script or at least a concept on the other. Both decisions also involve (see Question 4) sequential decision making: at each stage of the project the decision is to proceed with the next stage, after which the situation will be reassessed and another decision made for the stage after that. And both decisions involve estimates of future conditions and competitive actions, the inherent imperfections of which add risk.

The differences arise less from the nature of the two decisions but more from the nature of the industries in question. The data used in both is entirely different. The motion picture studio decision is not regulated by a government agency that imposes requirements on the movie-making process. Making a movie, while it is expensive and takes a long time relative to many other business projects, costs much less than a billion dollars and takes much less time than twelve to fifteen years.

7.

How could the process that Bayer developed be provided to a smaller firm in the industry?

It could be used with few or no changes. Although there are relatively larger and smaller pharmaceutical firms, the human and physical resources required to develop any drug and the financial investment that must be made before it pays off mean that there are no “small” ones for which its resource requirements might pose a problem.

If it were desired to make a similar process available to truly small firms, a knowledge management system could capture much of the expertise provided here by individual human experts. An expert system could capture the experts’ knowledge of what decisions should be chosen in various situations.

CHAPTER

3

Decision Support Systems Concepts, Methodologies, and Technologies: An Overview

Learning Objectives for Chapter 3 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand possible decision support system (DSS) configurations Understand the key differences and similarities between DSS and business intelligence (BI) systems Describe DSS characteristics and capabilities Understand the essential definition of DSS Understand DSS components and how they integrate Describe the components and structure of each DSS component: the data management subsystem, the model management subsystem, the user-interface (dialog) subsystem, the knowledge-based management subsystem, and the user Explain Internet impacts on DSS and vice versa Explain the unique role of the user in DSS versus management information systems (MIS) Describe DSS hardware and software platforms Understand important DSS classifications Become familiar with some DSS application areas and applications Understand important, current DSS issues

CHAPTER OVERVIEW This chapter delves deeper into DSS. It is an intermediate step between the once-over-lightly of Chapter 1, which was designed to introduce the field and provide perspectives on it, and the more detailed coverage of the chapters that follow. As students study successive parts of the book, they will find themselves getting even deeper into the topic.

43

44

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

CHAPTER OUTLINE 3.1

OPENING VIGNETTE: DECISION SUPPORT SYSTEM CURES FOR HEALTH CARE A. DSS ASSISTS DIRECTLY IN PROVIDING PATIENT CARE AT RIVERSIDE HEALTH SYSTEM B. DSS MONITORS AND IMPROVES OKLAHOMA HEART HOSPITAL’S IT PERFORMANCE C. DSS IMPROVE HEALTH CARE OPTIONS EFFECTIVELY D. DSS GROWTH IN HEALTH CARE  Questions for the Opening Vignette E. WHAT WE CAN LEARN FROM THIS VIGNETTE

3.2

DECISION SUPPORT SYSTEM CONFIGURATIONS  Section 3.2 Review Questions

3.3

DECISION SUPPORT SYSTEM DESCRIPTION A. A DSS APPLICATION ⧫ Application Case 3.1: Web/GIS-Based DSS Aid in Disaster Relief and Identifying Food Stamp Fraud  Section 3.3 Review Questions

3.4

DECISION SUPPORT SYSTEM CHARACTERISTICS AND CAPABILITIES  Section 3.4 Review Questions

3.5

COMPONENTS OF DECISION SUPPORT SYSTEMS A. THE DATA MANAGEMENT SUBSYSTEM B. THE MODEL MANAGEMENT SUBSYSTEM C. THE USER INTERFACE SUBSYSTEM D. THE KNOWLEDGE-BASED MANAGEMENT SUBSYSTEM E. HOW THE DSS COMPONENTS INTEGRATE  Section 3.5 Review Questions

3.6

THE DATA MANAGEMENT SUBSYSTEM ⧫ Application Case 3.2: Roadway Drives Legacy Applications onto the Web A. THE DATABASE B. DATA ORGANIZATION C. DATA EXTRACTION D. THE DATABASE MANAGEMENT SYSTEM E. THE QUERY FACILITY ⧫ Technology Insights 3.3: The Capabilities of a Relational DBMS in a DSS F. THE DIRECTORY G. KEY DATABASE AND DATABASE MANAGEMENT SYSTEM ISSUES 1.

Data quality

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

2.

3.

4.

45

Data integration ⧫ Technology Insights 3.4: The 10 Essential Ingredients of Data (Information) Quality Management Scalability Data security  Section 3.6 Review Questions

3.7

THE MODEL MANAGEMENT SUBSYSTEM A. THE MODEL BASE B. MODEL BUILDING BLOCKS AND ROUTINES C. MODEL COMPONENTS FOR BUILDING DSS D. MODELING TOOLS ⧫ Application Case 3.5: Web-Based Cluster Analysis DSS Matches Up Movies and Customers E. THE MODEL BASE MANAGEMENT SYSTEM F. THE MODEL DIRECTORY G. MODEL EXECUTION, INTEGRATION AND COMMAND ⧫ Technology Insights 3.6: Major Functions of an MBMS  Section 3.7 Review Questions

3.8

THE USER INTERFACE (DIALOG) SUBSYSTEM A. MANAGEMENT OF THE USER INTERFACE SUBSYSTEM B. THE USER INTERFACE PROCESS C. DSS USER INTERFACES ⧫ Application Case 3.7: Clarissa: A Hands-Free Helper for Astronauts D. DSS DEVELOPMENTS ⧫ Technology Insights 3.8: Human Gestures for Computer Input  Section 3.8 Review Questions ⧫ Technology Insights 3.9: Blackboard: A DSS for E-Learning

3.9

THE KNOWLEDGE-BASED MANAGEMENT SUBSYSTEM ⧫ Application Case 3.10: IAP System’s Intelligent DSS Determines the Success of Overseas Assignments and Learns from the Experience  Section 3.9 Review Questions

3.10

THE DECISION SUPPORT SYSTEM USER  Section 3.10 Review Questions

3.11

DECISION SUPPORT SYSTEM HARDWARE  Section 3.11 Review Questions

3.12

DECISION SUPPORT SYSTEM CLASSIFICATIONS A. The AIS SIGDSS classification for DSS 1.

Communications-driven and group DSS (GSS)

46

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

2.

B. C. D.

E.

3.13

Data-driven DSS ⧫ Application Case 3.11: Database-Oriented DSS: Glaxo Wellcome Accesses Life-Saving Data 3.

Document-Driven DSS 4.

Knowledge-driven DSS, Data Mining, and Management Expert Systems Applications 5.

Model-Driven DSS 6.

Compound DSS HOLSAPPLE AND WHINSTON’S CLASSIFICATION ALTER’S OUTPUT CLASSIFICATION OTHER DSS CATEGORIES 1.

Institutional and Ad Hoc DSS 2.

Personal, Group, and Organizational Support 3.

Individual DSS Versus a Group Support System CUSTOM-MADE SYSTEMS VERSUS READY-MADE SYSTEMS  Section 3.12 Review Questions

RESOURCES, LINKS, AND THE TERADATA UNIVERSITY NETWORK CONNECTION A. RESOURCES AND LINKS B. CASES C. MILLER’S MIS CASES D. VENDORS, PRODUCTS AND DEMOS E. PERIODICALS F. ADDITIONAL REFERENCES G. THE TERADATA UNIVERSITY CONNECTION

Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role Playing Projects Internet Exercises ⧫ End of Chapter Application Case: FedEx Tracks Customers Along with Packages  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

This chapter stands between the high-level DSS overview of Chapter 1 and the greater detail in the chapters that follow. The Chapter 1 material that is amplified here is primarily in Section 1.6.

You may want to revisit that section with your students, especially Figure 1.5, when starting on this chapter.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

47

At first glance the relationship between the opening vignette and the chapter content may seem to be a bit loose. Its value is not in introducing the specific DSS component issues that will occupy much of the chapter, but in motivating them. The third and sixth items in the “What we can learn from this vignette” list at the end of Section 3.1 will be the focus here.

The sections of this chapter have a tremendous amount of dense content. Students may despair of mastering it. It can help to point out that most of these sections are condensed versions of chapters yet to come, so the material will be revisited in a (relatively) more leisurely fashion.

You may also find the Application Cases in this chapter especially useful, as they relate the sometimes abstract concepts of the text to practical situations the students can “get their hands on.”

Finally, depending on your students’ background, you may find that you have to define some MIS concepts to make this chapter comprehensible. In the Roadway case (Application Case 3.2) today’s students may well not know what is a “green screen”. Java is mentioned several times as a tool for developing DSS models, but students may not know what makes Java more useful in this context than any other programming language. This chapter is a natural place for terminology issues to come up because it is the first chapter in the book that goes into technology in any depth. As you read the chapter, you may want to note terms that you feel might confuse your students or leave them in the dark. In some cases (e.g., the list of knowledge components in the first paragraph of Section 3.9) you will be able to say, “Don’t worry about those now; we’ll cover them in Chapters 12 and 13.”

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 3.1 Review Questions 1.

The key issue in DSS in healthcare is decision making. What kinds of decisions are being made, and how is DSS helping?

The kinds of decisions being made include: • Patient care decisions: treatments, medications, dosages, procedures, priorities (e.g., for limited resources such as transplantable organs) • Internal hospital/clinic administrative decisions: staffing, scheduling, overall management • Billing decisions: whom and in what order, eligibility, amounts/discounts, when to forgive • Other financial decisions, such as determining sources and uses of capital DSS helps with these decisions just as it helps with any decisions: by providing decision makers with information they need (collecting it from multiple sources, organizing it, analyzing it, and presenting it), offering a range of possible decisions, and predicting the effect of a possible choice.

Instructors assigning this case may want to add the related application case at the end of Chapter 2 (Indiana Heart Hospital) for additional content, if that case was not assigned there.

48

2.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Explain why it is so important for a hospital (or a bank, an airline, etc.) to continually monitor and forecast the condition of its information technology platforms?

These platforms are critical to the ongoing operation of the organization. An IT failure would impact patient care directly—not just decisions which, in most cases, could be made equally well an hour later. A bank could not function effectively during an IT failure as it could not accept deposits, dispense cash, or transfer funds among accounts.

An airline could not accept reservations, issue boarding passes, or check in passengers.

Continuous monitoring of their IT platforms improves the ability of these organizations, as well as others, to accommodate usage growth through expansion before the need becomes critical and, often, to predict incipient failures before they occur.

3.

Look up the basic concepts of CRM and describe how they apply in determining benefits segments.

A CRM system is an information system that manages an organization’s information about its customers, organizing it and making it available as needed to those who interact with those customers or use information about them in some other way. CRM data can be used to segment customers. For example, a catalogue sales firm such as L.L. Bean could segment its customers on the basis of their purchases, sending each segment a catalogue containing items expected to be of interest to those people.

To a health care provider, patients are the customers. A CRM system that has information about patient characteristics and health care needs can correlate needs with characteristics. This, in turn, allows the organization to plan health care for a changing population, to assess the cost of providing health care to different populations (and perhaps to adjust its charges appropriately, subject to legal and ethical considerations) and to make other, similar decisions based on increased understanding of its patient population and their needs.

4.

Make a list of benefits that DSS can provide to health care systems. Justify each item.

DSS can benefit health care systems by improving decisions of the types listed in the answer to Question 1. The justification in each case is the value of a better decision in that case: improved treatment outcomes, improved internal management, improved financial results.

Specific student responses will vary.

5.

Search the Web for additional examples of patient or benefit segmentation in healthcare.

Report on your findings.

This is an open-ended question; the answer depends on the student’s Web search. An example of what they could find is the Consumer Segmentation section of “The Paradox of Increasing Drug Costs,” originally published in Health Affairs: http://www.medscape.com/viewarticle/409816_5 . That article discussed tiered benefits, in which consumers who accept restrictions on their choice of drugs (using a “preferred” list, generics when available) can save money whereas those who want greater choice must pay for the privilege.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

49

(Segmentation in this case is driven by patient self-selection, not by the health care provider. The question does not restrict answers to segmentation by providers.)

Section 3.2 Review Questions 1.

List and describe the three major components of DSS. The three major components are: data, models, and user interface.

Data refers to the information needed to make a decision, typically stored in a database, and to how these data are organized and managed by a DBMS. Models refer to the models used to analyze the data and predict the results of a decision, as well as to the software used to manage the use of the models in a DSS. User interface refers to the way a manager or knowledge worker can use the system to support his or her decision making needs without having to become an expert in its technology.

2.

Explain how subjective data can be utilized in a DSS in light of the situations described in the opening vignette examples.

The opening vignette examples are all about DSS in health care. Some patient evaluation items are objective: a body temperature of 99 °F (37.2 °C), a triglyceride count of 100 mg/dL. Others are subjective: “severe” pain, “long” time to recover from anesthesia.

Both must be used in determining the correct course of treatment.

Data used in making other types of health care decisions are likewise often subjective.

3.

Models play a key role in DSS. Why? Explain how models exist in spreadsheet packages like Excel.

Spreadsheet packages such as Excel include built-in models for common situations, primarily financial. For example, Excel’s model of mortgage payments can tell a potential home buyer what the monthly payments will be for a mortgage of a given principal, term, and interest rate. If this figure is too high the buyer can then find a less expensive home or a larger down payment, cut other expenses to afford it, consider extending payments over a longer term, or perhaps explore other options.

Excel formulas also make it possible for users to create a wide variety of models.

For example, a formula might relate a firm’s revenues in year n to marketing expenditures in year n-1 and R&D expenditures in years n-1 and n-2. Using such a financial model could temper management’s inclination to deal with a revenue downturn by cutting these excessively.

4.

How can knowledge be automatically utilized by a DSS?

50

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Section 3.3 Review Questions 1.

Provide two definitions of DSS. What do they have in common? What features differentiate them?

The early definition of DSS was “a system intended to support managerial decision makers in semistructured and unstructured decision situations.”

A definition of DSS we can use today is: An approach (or methodology) for supporting decision making. It uses an interactive, flexible, adaptable computer-based information system (CBIS) especially developed for supporting the solution to a specific nonstructured management problem. It uses data, provides an easy user interface, and can incorporate the decision maker’s own insights. In addition, a DSS includes models and is developed (possibly by end users) through an interactive and iterative process. It supports all phases of decision making and may include a knowledge component. Finally, a DSS can be used by a single user on a PC or can be Web based for use by many people at several locations.

Their common features include a focus on managerial decision making in nonstructured (i.e., both semistructured and unstructured) decisions, and on supporting a human decision maker rather than automating the decision.

They differ in that the second definition is much more specific as to what a system must be in order to be considered a DSS. For example, the second definition requires a DSS to address a specific decision or problem, to use data, to incorporate models, and to be developed through an interactive, iterative process. In addition, the first definition requires the DSS to be an information system, while the second allows it to be an approach or a methodology—though it is not clear how, or if, the other DSS aspects it calls for could be incorporated into anything that is not an information system.

According to the second definition, a system that is not developed through an iterative process, does not support all phases of decision making, etc., does not qualify as a DSS. Some authors take a less restrictive position on this point.

2.

Why do people attempt to narrow the definition of DSS? Early, broad definitions such as the first one above were open to a variety of interpretations, leading to disagreements as to what actually is a DSS. Another reason is that narrower definitions enable greater focus on common characteristics of the smaller population of objects, much in the same way that biologists first define “insects” and then define a subset of insects as “beetles.” If one knows one is dealing with a beetle, one has more information about it than one would have if one only knows that a creature is an insect.

3.

Give your own definition of DSS. Compare it to the definitions in question 1.

My definition would be “An information system whose primary purpose is to support knowledge workers making nonstructured decisions in a specific area.” Student definitions will vary.

This definition is closer to the first one, but requires a DSS to have a specific application area. It does not require decisions to be managerial or for problems to be

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

51

management problems. (One can use these terms more generally than their literal reading suggests. In that case, this difference goes away.)

It allows for a wider range of technologies than the second definition, but would perhaps include systems that those who wrote it deliberately wanted to exclude.

Finally, it explicitly excludes systems that provide some decision support as a by-product of their primary purpose.

4.

Explain how the systems described in Application Case 3.1 are DSS. Use the definitions from questions 1 and 3.

(The case does not say how the systems in it were constructed. While it is reasonable to assume that they were developed “through an interactive and iterative process,” since most systems of this type would be, we do not know this. What follows will thus not address this aspect of the second definition.)

The case mentions these five specific systems: • Riverside Health System: This system provides necessary information to health care providers for decisions that are not specifically stated. Some of these decisions are surely nonstructured, a requirement of all three definitions. Although some health care decisions are made by managers, others are not, so in this regard the system fits the third definition but not the first two (or requires the term “manager” to be interpreted broadly).

We are told that the system incorporates both models and data, so it meets the restrictive requirements of the second definition on those points.

Finally, we do not know if it is used on a PC or accessed over the Web. If it is used (for example) via terminals connected directly to a hospital server, it might not meet the second definition in that regard.

• Oklahoma Heart Hospital, medical records: The single sentence about this system does not provide enough information for a meaningful answer to this question.

• Oklahoma Heart Hospital, BMC Patrol Analytics: This system handles event monitoring and alarms for OHH’s IT infrastructure. These are used as the basis of capacity planning and system maintenance decisions, some of which are made by managers. The data is that of specific alarms and other measurements. The models are used to determine which alarms are real, which are not. Access to the system is via the Web. This system conforms to all three definitions.

• International Truck/Pitney-Bowes: The system was used to support managerial decisions to reduce copayments for asthma and diabetes medications. It employed data about the incidence of these diseases overall and within specific employee populations. It also employed a knowledge component and statistical models to go through these data and find patterns that are associated with large claims. The case does not have information as to whether this system is accessed via a PC, over the Web or in some other way, so we cannot determine its conformance to the second definition in that regard.

52

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

•

Humana: The case lists several specific types of models in this system. It requires data to drive them. The segmentation is used to support decisions about which employee groups will receive each type of benefit package— decisions that would normally be made at a managerial level. With the possible exception of how users access it, about which the case is silent, the system conforms to the second definition. It clearly meets the first and third as well.

Section 3.4 Review Questions 1.

List the key characteristics and capabilities of DSS. These are shown in Figure 3.2, and amplified in the text below: • Support for (mainly) semistructured and unstructured situations, combining human judgment with computerized information.

• Support for all managerial levels, from top executives to line managers.

• Support for individuals and groups.

• Support for interdependent and/or sequential decisions.

• Support all four decision-making phases: intelligence, design, choice, and implementation.

• Support variety of decision-making processes and styles.

• Users can add, delete, combine, change, or rearrange basic elements, or modify them to solve other, similar problems.

• User-friendly interface. (Most new DSS applications use Web-based interfaces.)

• Improves effectiveness of decision making (accuracy, timeliness, quality) rather than its efficiency.

• Decision maker controls all steps of the decision-making process in solving a problem.

• End users can develop and modify simple DSS by themselves; larger DSS require IS specialists.

• Models are generally utilized to enable experimenting with different strategies.

• Access to a variety of data sources, formats, and types, including GIS, multimedia, and object oriented.

• Used by an individual decision maker or distributed throughout one or more organizations

2.

Describe how providing support to a workgroup is different from providing support to group work. Explain why it is important to differentiate these concepts.

A workgroup often works on separate problems within its area of responsibility. For example, a claim processing workgroup in an insurance company may consist of several people who work on their assigned claims by themselves.

In group work, multiple people cooperate to solve the same problem. Insurance claims processing could be group work if handling a claim required the cooperation of policy analysts, medical experts, vehicle dynamics experts, legal experts, and others.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

3.

53

What kinds of DSS can end users develop in spreadsheets?

Simple ones—this reflects both the limitations of the spreadsheet as a DSS development tool and the limitations of the typical end user in developing complex information systems.

4.

Why is it so important to include a model in a DSS? The modeling capability of a DSS, which is what differentiates DSS from other MSS, is needed to enable experimenting with different decisions under different configurations and assumptions.

Section 3.5 Review Questions 1.

List the major components of DSS and briefly define each of them.

The major components are: data management, model management, user interface and knowledge base.

• • • •

2.

Data management includes a database management system and one or more databases.

Model management includes models and their management system.

User interface includes bi-directional human-computer communication and its management.

Knowledge base includes artificial intelligence enhancements to the other components.

Briefly explain how the Web is utilized in each major component of DSS. Table 3.1 lists these at length in its middle column. To summarize briefly: • Database management system (DBMS): Data are usually stored on a server accessed via the Web. • Model base management system (MBMS): Models may be stored externally and accessed via the Web. Models may also be developed to run on application servers, which are also accessed via the Web. • User interface dialog system: Web browsers provide a familiar, consistent graphical user interface (GUI) structure.

• Knowledge-based management system (KBMS): Many AI methods have been implemented in Web-based systems, making them easy to integrate with other DSS components.

3.

How can a knowledge-based component help each of the other DSS components?

A knowledge-based component can help each of the other three components in the ways listed in the right column of the lower part of Figure 3.4. These are:

54

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• • • •

4.

Artificial intelligence techniques can assist users in using the other components.

AI methods can assist directly in decision making, for example via symbolic reasoning.

AI methods can also enable automated decision making for sufficiently structured problems.

Tools such as expert systems and neural networks can be more accurate than other methods.

Explain the basic structure of DSS and its components.

As shown in Figure 3.3: • The user interface (UI) connects the user to the other system components.

• The knowledge-based subsystem, in addition to being connected to the user via the UI, may also connect to the database management system to obtain the data it needs, to external models, and to an organizational knowledge base.

• The model management subsystem links data management to the models.

• Data management, in addition to the connections to model management and the knowledge-based subsystem already noted, connects to internal and external data sources.

• Models are controlled by the model management subsystem, may control or be controlled by the knowledge-based subsystem, and communicate with the user via the UI. • The entire DSS can communicate with other systems and networks if necessary.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

55

Section 3.6 Review Questions 1.

Why does a DSS generally include its own database?

Large DSS generally have their own databases for three reasons: • The DSS may have to integrate data from multiple sources, each of which is a separate database and which cannot be combined directly.

• The best database structure for static data used for analysis, with infrequent but complex queries, may not be suitable for dynamic data used for transaction processing with frequent but simple queries and updates, and vice versa.

• Sharing one database for both uses may lead to performance problems at peak load times. Splitting off part of its user community may be more economical than the hardware upgrades that would otherwise be required.

2.

Describe the similarities and differences among internal, external, and private data.

Similarities: All these types can be in any form—structured record/field files, text files, or multimedia of various types. All can be objective or subjective. All are, or can be, managed by a DBMS subject to appropriate access controls. All can be used to support a wide variety of decisions. And all can be accessed via a Web-like interface, even if they are not accessed over the Web. Differences: Internal data come from the organization’s own transaction processing systems, whereas external data come from outside it. Internal data are typically accessed over an intranet whereas external data are accessed over the Internet, though the tool(s) used and the user interface may be similar or identical. (Private data is a subset of internal data, subject to access restrictions that go beyond normal corporate data security.)

3.

Describe the components of a DBMS: the query facility, the directory, and the data.

• •

• 4.

Query facility: Accepts requests for data from other DSS components, determines how those requests can be filled, formulates database accesses to fill them, and returns results to the issuer.

Directory: The main purpose of the directory, and the data definitions it contains, is to answer questions about the availability of data items, their source, and their meaning. It also contains access rules that control users’ access to certain columns or rows of the database tables.

Data: These1 are the actual content of the database.

What are the major functions (capabilities) of DBMS? As listed and discussed in this section, its three basic functions are storage, retrieval, and control. The DBMS manages the database to organize, extract/access, modify, delete, and catalogue data.

While the word data is technically a plural noun whose singular is datum, and is so used in this Instructor’s Manual, treating it as singular is increasingly acceptable.

1

56

5.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

What is extraction?

It is the process of capturing data (often from multiple sources), filtering them, summarizing, condensing and reorganizing the data to load into a DSS database such as a data warehouse.

Some authorities students may consult use the term extraction only for the first of these, referring to the entire process as Extraction, Transformation and Load (ETL). This usage is noted on p. 99.

6.

What is the function of a query facility?

In building and using DSS, it is often necessary to access, manipulate, and query data.

The query facility performs these tasks. It accepts requests for data from other DSS components, determines how the request can be filled (consulting the data directory if necessary), formulates the detailed requests, and returns the results to the issuer of the request.

7.

What is the function of a directory?

It is a catalog of all data in the database. It includes data definitions and other information needed to facilitate and control access to data via the DMBS.

Section 3.7 Review Questions 1.

Models are classified as strategic, tactical, or operational, and analytical. What is the purpose of such a classification? Give an example of each type of model.

The purpose of this classification is to show the importance of the decision and indicate what managerial level is responsible for its execution. Knowing the type of a model also enables people to draw conclusions about its probable structure and user community without knowing more about it.

Strategic models support top management's strategic planning. For example, examination of acquisitions, diversifications, and mergers.

Tactical models support mainly middle management in resource allocation and in control. For example, make-or-buy decisions or devising a major promotion plan.

Operational models support middle managers and supervisors in very short-term decisions. For example, assessing the probable impact of alternative daily and weekly work schedules.

Analytical models are used to perform analysis on data. They include statistical models, management science models, data mining algorithms, financial models, and more.

2.

List some of the major functions of an MBMS. See Boxed Text: Technological Insights 3.6 in Section 3.7, for list. It parallels the database management system in function by providing comparable capabilities to model management.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

3.

57

Compare the features and structure of the MBMS to those of the DBMS. The DBMS includes databases that contain relevant data and is managed by database management software through a data dictionary, supported by query and reporting facilities. Information is taken from both internal and external data sources.

Correspondingly, the MBMS includes statistical, management science, or other quantitative models that provide the system’s analytical capabilities, and model management to coordinate their use. It contains a model directory that parallels the data directory of the DBMS. Its model execution, integration, and command processing facility corresponds to the query and reporting facilities of the DBMS in that it allows users to access whatever models their requirements call for.

4.

Why is model selection for DSS difficult?

Model selection is difficult because it requires expertise to understand the available models, their areas of applicability and their strengths and weaknesses.

5.

How can a knowledge component assist in model selection?

A knowledge component can assist in model selection by capturing the knowledge of human experts as to the applicability of different models in different situations, making it available to people having less expertise in this area.

Section 3.8 Review Questions 1.

What is the major purpose of a user interface system?

A user interface covers all aspects of the communications between a user and the DSS (or any MSS). Its fundamental purpose is to make it as easy as possible for the user to obtain decision assistance from the DSS.

2.

Describe the user interface process.

The term “process” in this context refers to the flow of information (a) from the user to the system and (b) from the system to the user. It is handled by the User Interface Management System (UIMS). The UIMS processes user commands, issued in whatever action language it requires, and passes them on to the data and model management subsystems. In the reverse direction, it presents information from those subsystems to the user. Increasingly, the action language is based on Web or operating system GUI concepts. It may also incorporate natural language processing capabilities.

3.

What are the major functions of a UIMS? See the answer to the above question. Since the major functions of a UIMS must be discussed in describing the user interface process, it would be redundant to assign both.

58

4.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Describe why Web tools are typically used for DSS interfaces.

Web tools are typically used because they have become the standard means for users to connect to many types of software, defining user expectations in this area. They allow users to access both internal (intranet) and external (Internet, extranet) sites, are available for a variety of devices ranging in size from computers down to mobile phones, are platform-independent at the laptop/desktop level (Windows, Mac OS, Linux) and are supported by a wide variety of problem-solving tools.

5.

List four new developments in user interfaces.

Voice recognition, gesture interpretation, automated input (e.g., RFID chips, sensor networks), voice output, virtual reality, and others could be listed here. Students may also answer this question with developments that they have read about elsewhere or studied in other courses.

6.

List four new developments in DSS, other than user interface developments.

Improved Web tools, parallel processing to improve scalability, better search engines as one manifestation of improved AI, ready-made DSS solutions for specific market segments, increased integration with diverse components such as GIS, and others could be listed here.

Section 3.9 Review Questions 1.

List the various knowledge-based tools that can comprise a knowledge-based management system.

Such a system can include expert systems, neural networks, intelligent agents, fuzzy logic, case-based reasoning, and so on.

Some instructors may wish to distinguish between the first three items in the list and the last two. Expert systems, neural networks and intelligent agents are software tools. Fuzzy logic and case-based reasoning, by contrast, can be considered useful concepts, which can be incorporated into tools of any type, rather than tools in their own right.

The issue is really one of interpretation of the word tool. If it is taken to mean a software tool, it is appropriate to exclude the last two items. If it is understood more generally, they should be included.

2.

What capabilities does the knowledge-based management system provide to DSS either in total or to each component?

The knowledge-based component can either supply required expertise for solving some aspects of the decision problem or provide knowledge to enhance the operation of other DSS components. As an example of the first benefit, an expert system could incorporate rules to help choose the best routing for an aircraft so it arrives at a maintenance base just before its 400-hour service is due, eliminating the need for an expensive, unproductive ferry flight. As an example of the second benefit, an expert system could figure out the best order in which to process database accesses for a complex query.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

59

Section 3.10 Review Questions 1.

List and describe the two broad classes of DSS users.

Managers (or knowledge workers as well), who are charged by the organization with solving the problem the DSS is designed to assist with, and staff specialists, who help them use aspects of a DSS that are beyond their technical capabilities or would require too much of their (more valuable) time.

2.

List and describe the four types of DSS intermediaries.

• • •

•

3.

Staff assistants have specialized knowledge about management problems and some experience with decision support technology.

Expert tool users are skilled in the application of one or more types of specialized problem-solving tools. An expert tool user performs tasks that the problem solver does not have the skill or training to perform.

Business (system) analysts have a general knowledge of the application area, a formal business administration education (not in computer science), and considerable skill in using DSS construction tools. They typically perform tasks that a manager could, in principle, perform himself or herself, freeing up his or her time for more valuable tasks.

Facilitators (in a GSS) control and coordinate the use of software to support the work of people working in groups. The facilitator is also responsible for the conduct of workgroup sessions.

Why are most users typically very hands-on in their DSS usage patterns?

Much of the value of a DSS is in its ability to try out various combinations of parameters and pursue interesting lines of inquiry in detail. This is inherently an interactive process that works better under the direct control of the user. (Instructors should note that this question is not answered directly in the text, so a wide range of answers should be expected. An instructor who assigns it might want to point out that it calls for thinking more than reading.)

Section 3.11 Review Questions 1.

Why are DSS hardware and software choices often based on a firm’s existing systems?

Most MSS, including DSS, use standard hardware. They usually do not justify the acquisition of new systems optimized for their needs. New acquisitions are limited to DSS-specific hardware and software, such as storage for a data warehouse or a forecasting software package.

2.

List potential PCs and mobile devices that decision makers could use to connect to a DSS. Any PC, whether it runs Windows, Mac OS or Linux, supports Web browsers and can therefore connect to a Web-based DSS. Mobile devices include laptops using any of the

60

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

above OS, PDAs (typically running either Windows CE or Palm OS today) and mobile phones.

3.

List the potential hardware options for DSS implementation.

The DSS itself can run on the client system, on a server (general-purpose or specialized application server) or on a mainframe. Complex models are typically run on fast hardware, which excludes most PCs and Web servers, but lightly loaded Web servers can also be used.

4.

List the reasons why the Web is utilized for DSS development and deployment.

It is familiar to users, supports the well-known browser interface, is supported by a wide range of software tools, facilitates communication among parts of a DSS housed on different computers, and can be accessed from wherever a user happens to be.

Section 3.12 Review Questions 1.

List the DSS classifications of the AIS SIGDSS. • • • • • •

2.

Communications-driven and group DSS (GSS) Data-driven DSS Document-driven DSS Knowledge-driven DSS, data mining, and management ES applications Model-driven DSS Compound DSS, hybrids that combine two or more of these categories

Define document-driven DSS. A document-driven DSS relies on knowledge coding, analysis, search, and retrieval for decision support. This includes all text-based DSS and most KMS. Document-driven DSS have minimal emphasis on mathematical models.

3.

List and compare the Holsapple and Whinston classifications to those of the AIS SIGDSS. The H&W classifications map readily into those of the AIS SIGDSS, as follows: • H&W text-oriented DSS are the same as AIS SIGDSS document-driven DSS. • H&W database-oriented DSS are AIS SIGDSS data-driven DSS. • H&W spreadsheet-oriented DSS are generally another form of AIS model-driven DSS, in which spreadsheet facilities are used to create and manage models.

Because packages such as Excel can include a rudimentary DBMS or can readily interface with one, they can handle some properties of an AIS SIGDSS databaseoriented DSS, especially manipulation of descriptive knowledge.

• H&W solver-oriented DSS map directly into AIS model-driven DSS. • H&W rule-oriented DSS include most AIS SIGDSS knowledge-driven DSS, data mining, and management ES applications.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

•

4.

61

H&W compound DSS integrates two or more of those cited above and corresponds to the same AIS SIGDSS concept.

List the capabilities of institutional DSS and ad hoc DSS. An institutional DSS is planned and developed to handle a recurring decision. It must have the flexibility to deal with that decision in different manifestations, with different data, over time. Such DSS tend to be used at the managerial control and operational levels.

An ad hoc DSS is developed to handle a one-time problem. Such problems typically appear at the strategic and management control levels. Such a DSS need not have the same degree of flexibility as an institutional DSS to deal with variations in the problem. However, problems that were not expected to recur still often do so, or it turns out that the DSS is applicable to other problems as well.

5.

Define the term ready-made DSS. A ready-made DSS is a DSS software product designed to be used, with minimal modifications, by several organizations that have comparable decision making needs.

Such DSS are often designed for a specific industry (e.g., hospitals) or functional area (e.g., finance).

ANSWERS TO END OF SECTION REVIEW QUESTIONS 1.

       

Review the major characteristics and capabilities of DSS. How do each of them relate to the major components of DSS? The characteristics and capabilities of DSS are listed in Figure 3.2 as follows. Their relationship to the major components of DSS is noted after each one.

• Semistructured and unstructured problems: The nature of all DSS components reflects this.

• Support managers at all levels: A given DSS may be oriented more toward one level than others. This will be reflected in its user interface.

• Support individuals and groups: DSS designed to support groups must include communication features in their user interface component.

• Interdependent or sequential decisions: This is controlled by the decision maker.

• Support all four decision phases: intelligence, design, choice, and implementation.

The DSS must incorporate models, and optionally knowledge-based capabilities, appropriate to each phase. The data and user interface will in most cases be common to all phases.

• Support variety of decision processes and styles: This relates primarily to the user interface. It also relates to the models (and knowledge-based system, if present), because decision makers with different styles will want different levels of analytical support.

• Adaptable and flexible: Primarily a function of the user interface, secondarily reflected in the variety of models and data sources included

62

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• • • • • • •

2.

Interactive ease of use: Relates to the user interface Effectiveness, not efficiency: This relates largely to the way all DSS components were developed and the priorities used in the development process.

Humans control the process: Relates to the user interface Ease of development by end users: Relates to the tools used to build models and to the end-user query facility of the database.

Modeling and analysis: Describes the models in the DSS Data access: Describes the database management capabilities of the DSS Stand-alone, integrated and/or Web-based: Reflects the way all components were developed

List some internal data and external data that could be found in a DSS for a university’s admissions office.

Internal data: Capacities of various majors for new students, historical success rates of applicants with specific characteristics, amount of money available for different forms of financial aid, team needs for specific types of athletes, agreed cutoff criteria for factors such as SAT scores.

External data: SAT scores and high school academic records of individual applicants, average grades given at various secondary schools.

3.

Explain how poor data quality can lead to bad decisions.

A decision is based on an assessment of what will happen in the future if a particular course of action is chosen. This forecast is based on data. Poor quality data will lead to an erroneous forecast, which in turn can lead to incorrect relative evaluations of the costs and benefits of alternatives.

For example, consider a company planning to lease a truck for one year. One option is to lease a truck that will use 2,000 gallons of fuel during the year for $12,000.

Another is a less economical truck that will use 3,000 gallons for $9,500. If fuel is expected to cost $3 per gallon during that year, the more economical truck is the wiser choice. However, if poor quality data resulted in the model using a figure of $2 per gallon for fuel, and this was not caught in later review, the company would make the mistake of leasing the less economical truck.

4.

Why is it so important for an organization to make data quality a key organizational issue?

It is important because no single person can ensure quality data. It must be a joint effort on the part of all those whose work contributes to the corporate data resource.

5.

Why is it so important to have a single version of the truth when it comes to data integration for decision making? Give or find an example of when not having a single version of the truth in believed data integration led to problems for an organization.

Different input data can often lead to different model outputs. When a model finds one choice superior to another, it can be impossible to determine if the apparent superiority is

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

63

real or due simply to different input data. For example, suppose two alternative manufacturing processes are being considered. One evaluation uses lower raw material costs than the other. It may appear to yield higher profits, but it can be impossible to know if the process is better or if the lower cost of raw materials used in evaluating it made it look better. The only way to compare them objectively is to use the same raw material costs for both.

6.

Why is scalability an important database issue?

As databases grow, performance can deteriorate. It can often deteriorate more than linearly, in a phenomenon sometimes known as “falling off a cliff:” when a certain size range is reached, performance deteriorates dramatically with a small increase in database size. Scalability refers to the ability of a database to grow without unacceptable performance degradation. This is especially important with DSS databases, such as data warehouses, reaching two-figure terabyte size.

7.

Why is data security an important database issue?

Much data is either private to individuals or would be of competitive value to a firm’s competitors. Such data must be protected from unauthorized access.

Specifically in the DSS context, DSS data are often summarized and aggregated to make them more useful in strategic and management analyses. Such data can be even more valuable to competitors than the raw data on which they are based.

8.

Provide a list of possible strategic, tactical, and operational models for a university, a restaurant, and a chemical plant.

Models can be developed to analyze the effects of different choices in all the following situations, among others: University Strategic: - major campus expansion - affiliation with other universities - development of a new school or college Tactical: - development of a new course - opening a new department - marketing plans for the year Operational: - course scheduling for the semester - admission decisions on MBA applicants Restaurant Strategic: - franchise or not - broad product line vs. limited menu - expand to new markets

64

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Tactical: - labor planning - budget preparation - promotion policy Operational: - weekly schedule - menu composition for a month Chemical Plant Strategic: - develop new process/technology - major expansion Tactical: - target sales, revenues - determining the least expensive mix of ingredients - determining the best mix of products Operational: - short-term schedule - weekly distribution plan 9.

Review the models described in the situations described in the opening vignette, the End of Chapter Application Case, and the Application Cases for this chapter. Compare and contrast what they are and how they are used. Explain how this illustrates the difficulty in model management as compared with that of database management.

A wide variety of models are used in the cases in this chapter. The difficulty of model management that emerges from this picture is that their range makes it more difficult to come up with common management approaches that apply to all of them. Data, on the other hand, tends to be more standardized in how it is structured. The same type of data dictionary can describe a medical or a package delivery database, because at bottom they are both based on similar data structuring concepts.

10.

Show the similarities between DBMS and MBMS. What is common to both and why?

What are the differences and why?

Both have similar functions. For example, change data (by DBMS) and change models (by MBMS), both provide for: store, control, update, retrieve, track usage, integrate and provide a directory. As a matter of fact, the term MBMS was coined to resemble DBMS.

11.

Why was DSS the first MIS ever defined as requiring a computer?

Prior to DSS systems, MIS consisted primarily of printed reports. While these reports were generated by computers, thus reducing costs and improving accuracy, there was no reason in principle why they had to be. A DSS, by definition, cannot be manual.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

12.

65

Why does a DSS need a DBMS, a model-management system, and a user interface, but not necessarily a knowledge-base management system?

The database management system is required to store and retrieve the variety of data required by a DSS. The model-management system is required to provide the capabilities to analyze the data. The user interface provides a mechanism for the user to be able to interact with the DSS. All these capabilities are necessary; none can be dispensed with.

The knowledge-based management system, by contrast, provides knowledge or expertise that might also be provided by an experienced user or, possibly trading off decision quality for system cost and complexity, dispensed with entirely.

13.

Why might the user be considered a component of the DSS? The user is unquestionably a component of the overall decision making system. Whether or not the user is a component of the DSS depends on where one draws the boundary of the DSS. If the user is the final decision maker who is supported by the DSS, it would usually be more appropriate to put the user outside the DSS. If the user is an intermediary, such as a staff assistant who brings findings to a senior manager who will make the final decision, the DSS can be said to include the user since the user is part of the overall system that provides decision support.

(This question can be used as a basis for class discussion of the concept of a “system,” especially if an instructor chooses to assign Online File W2.2, the tutorial on systems.)

14.

Why do managers use intermediaries? Will they continue to use them in the future? Why or why not? Would you? Why or why not?

The text discusses four types of intermediaries: staff assistants, expert tool users, business (system) analysts, and facilitators. The reasons managers use intermediaries vary from one type to another. Their use of expert tool users and business analysts may drop as DSS become easier to use and their knowledge-based components are better able to provide decision guidance.

However, it is unlikely that use of staff assistants or facilitators will decrease. The use of staff assistants reflects the cost and value of a top executive’s time. The use of facilitators reflects the need for professional leadership in many group decision making situations. Neither technology advances nor further developments in DSS will affect either of these human issues.

Answers to the last part of the question, whether the student would use an intermediary and why, will vary with the student’s background. At the MBA level, many students may have such jobs now.

15.

Why are DSS typically developed on an organization’s existing hardware and software?

See Section 3.11, Review Question 1. The two questions are essentially identical.

66

16.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Why are DSS typically developed and deployed with Web-based technology? Discuss the potential benefits that a DSS application can derive from the Web in terms of both developers and users.

DSS are typically developed and deployed with Web-based technology because it provides a familiar and powerful user interface, an easy way to access data from many sources, and offers a wide variety of pre-developed software tools. The range of available tools benefits primarily DSS developers. The user interface is primarily of benefit to users, though developers may find it easier to develop to a known interface than to define a new one from scratch. Data access capability benefits both.

17.

What are the benefits and the limitations of the AIS SIGDSS classification for DSS? The benefits of any classification are that, by grouping items that share common characteristics, one can benefit by knowing the common characteristics of each type rather than having to learn the characteristics of each item individually. The AIS SIGDSS DSS classification provides these benefits in a concise way by focusing on the key factor that drives each type of DSS. The limitations of this classification are that any broad grouping of items must of necessity ignore or gloss over differences among items within each category. Thus, this classification ignores issues such as whether a DSS is custom-built or ready-made, whether it is institutional or ad hoc, and other differences that can be important in many situations.

18.

Compare an individual DSS to a group DSS. How are they similar? How are they different?

A group DSS (or Group Support System, GSS) is a system that supports the process of decision making by a group. It must reflect steps of the group decision-making process.

An individual DSS is built for a problem faced by one decision maker, or by a group whose joint decision-making process does not require automated support.

They are similar in that, other than specific support for the group process, they include similar models, databases, user interfaces, etc.

19.

How has the Web impacted the components of DSS, and vice versa?

The Web provides a standard means of access to vast amounts of information that can be used in a DSS, thus impacting its data management component. The Web browser interface, well understood by all users, has impacted by user interface component of DSS by providing a model for developers to follow. Web tools also provide powerful pre-built software tools that impact all DSS components in a positive way.

20.

Compare a custom-made DSS with a ready-made DSS. Describe and discuss the advantages and disadvantages of each.

The comparison resembles that of any custom software system with a ready-made package. The custom DSS will take longer to develop, will cost more, and will require maintenance efforts over time, but will meet the needs of its using organization precisely.

The ready-made package will be available immediately for a lower cost and with

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

67

developer maintenance, but will be designed to meet the generic needs of many organizations in its area.

An organization should choose a ready-made DSS, assuming one is available, if its needs don’t differ much from those of other organizations; if it is small so developing a custom DSS would cost more than it can afford; or if the value of optimizing the DSS to its specific needs is small. Otherwise, developing a custom DSS is the wiser choice.

21.

Search for a ready-made DSS. What type of industry is its market? Explain why it is a ready-made DSS. The answer to this question will depend on the ready-made DSS that each student finds.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

Describe the benefits of the FedEx system. What other benefits might FedEx obtain with other features?

The benefits question can be answered at several levels: Low-level, specific: The system integrates internal FedEx operational data with external data.

Results-focused: Analysts can get up-to-the minute drop site usage data from their desktops, can track and even anticipate customer trends.

Business value: FedEx becomes provides better customer service by having drop boxes in the right places and achieves lower operating costs by removing under-utilized drop boxes. (The real saving in the latter is, of course, eliminating the ongoing cost of servicing them.)

Instructors who assign this question might want to specify the viewpoint they want students to take in answering it to avoid this ambiguity.

The benefits FedEx might obtain with other features are open ended, since the nature of those features is not specified. This question calls for creative thinking. Student answers will vary.

2.

Why is it important for a company like FedEx to manage its drop locations effectively?

If drop box locations are not convenient to current and potential customers, these customers will tend to use more convenient drops from competing firms. In addition, its drivers will waste time driving to empty or nearly-empty (i.e., unprofitable) drop boxes.

3.

Describe the benefits of switching from FOCUS to WebFOCUS. Do you think this was the right approach? Why or why not?

The major benefit of the switch was obtaining a Web-based DSS where they had previously had a mainframe-based DSS. They could have used any of several software packages to obtain this benefit, but they already had programmers who were familiar with the FOCUS family of products. Assuming the software choices were otherwise

68

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

comparable, there was no strong business or technical reason to use a different package.

This suggests they took the right approach.

4.

How can the FedEx approach taken in this case be applied to other industries?

The essence of what FedEx did here was to provide operational and customer data to managers and business analysts. Viewed in this light, what they did is not specific to the package delivery industry. The same approach, using the same or similar tools, could be used in any industry, though the specific data items managers need would change.

CHAPTER

4

Modeling and Analysis

Learning Objectives for Chapter 4 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the basic concepts of management support system (MSS) modeling Describe how MSS models interact with data and the user Understand some different, well-known model classes Understand how to structure decision making with a few alternatives Describe how spreadsheets can be used for MSS modeling and solution Explain the basic concepts of optimization, simulation, and heuristics, and when to use them Describe how to structure a linear programming model Understand how search methods are used to solve MSS models Explain the differences among algorithms, blind search, and heuristics Describe how to handle multiple goals Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking Describe the key issues of model management

CHAPTER OVERVIEW This is the first of the chapters that go into specific DSS technologies in detail. It covers models, an essential part of most DSS. (The exceptions are a few that make data available without analyzing it beyond an elementary level. These are suited only to the least structured problems.)

Future chapters will continue this approach by discussing data warehouses, business analytics, and so on.

The purpose of this chapter is to provide an understanding of the concepts of modeling so students will understand how models can be used with real world problems. It does so by going through a wide variety of model types, from the familiar to some students have probably never even heard of. By the time they finish, students should have a good idea of the range of modeling methods that exist and where each one might be useful.

CHAPTER OUTLINE 4.1

OPENING VIGNETTE: WINNING ISN’T EVERYTHING … BUT LOSING ISN’T ANYTHING: PROFESSIONAL SPORTS MODELING FOR DECISION MAKING A. THE BOSTON RED SOX AND OAKLAND ATHLETICS BASEBALL TEAMS B. THE SAN JOSE SHARKS HOCKEY TEAM C. HOOPMASTERS.COM BASKETBALL D. THE GREEN BAY PACKERS FOOTBALL TEAM  Questions for the Opening Vignette E. WHAT WE CAN LEARN FROM THIS VIGNETTE

4.2.

MANAGEMENT SUPPORT SYSTEM MODELING A. LESSONS FROM MODELING AT DUPONT B. LESSONS FROM MODELING AT PROCTER & GAMBLE C. LESSONS FROM ADDITIONAL MODELING APPLICATIONS ⧫ Application Case 4.1: United Airlines Model-Based DSS Flies the Friendly Skies D. CURRENT MODELING ISSUES 1.

Identification of the Problem and Environmental Analysis 2.

Variable Identification 3.

Forecasting (Predictive Analytics) ⧫ Application Case 4.2: Forecasting/Predictive Analytics Boosts Sales for Cox Communications 4.

Multiple Models 5.

Model Categories 6.

Model Management 7.

Knowledge-Based Modeling 8.

Current Trends in Modeling  Section 4.2 Review Questions

4.3.

STATIC AND DYNAMIC MODELS A. STATIC ANALYSIS B. DYNAMIC ANALYSIS  Section 4.3 Review Questions

4.4

CERTAINTY, UNCERTAINTY AND RISK A. DECISION MAKING UNDER CERTAINTY B. DECISION MAKING UNDER UNCERTAINTY C. DECISION MAKING UNDER RISK (RISK ANALYSIS)  Section 4.4 Review Questions

4.5.

MANAGEMENT SUPPORT SYSTEM MODELING WITH SPREADSHEETS  Section 4.5 Review Questions

4.6.

DECISION ANALYSIS WITH DECISION TABLES AND DECISION TREES A. DECISION TABLES 1.

Treating Uncertainty 2.

Treating Risk B. DECISION TREES ⧫ Application Case 4.3: Johnson & Johnson Decides About New Pharmaceuticals by Using Trees  Section 4.6 Review Questions

4.7.

THE STRUCTURE OF MATHEMATICAL MODELS FOR DECISION SUPPORT A. THE COMPONENTS OF DECISION SUPPORT MATHEMATICAL MODELS 1.

Result (Outcome) Variables 2.

Decision Variables 3.

Uncontrollable Variables or Parameters 4.

Intermediate Result Variables B. THE STRUCTURE OF MSS MATHEMATICAL MODELS  Section 4.7 Review Questions

4.8.

MATHEMATICAL PROGRAMMING OPTIMIZATION A. MATHEMATICAL PROGRAMMING ⧫ Application Case 4.4: Complex Teacher Selection Is a Breeze in Flanders B. LINEAR PROGRAMMING C. THE LP PRODUCT-MIX MODEL FORMULATION ⧫ Technology Insights 4.5: Linear Programming D. MODELING IN LP: AN EXAMPLE  Section 4.8 Review Questions

4.9.

MULTIPLE GOALS, SENSITIVITY ANALYSIS, WHAT-IF ANALYSIS, & GOAL SEEKING A. MULTIPLE GOALS B. SENSITIVITY ANALYSIS 1.

Automatic Sensitivity Analysis 2.

Trial-and-Error Sensitivity Analysis C. WHAT-IF ANALYSIS D. GOAL SEEKING 1.

Computing a Break-Even Point by Using Goal Seeking  Section 4.9 Review Questions

4.10. PROBLEM-SOLVING SEARCH METHODS A. ANALYTICAL TECHNIQUES B. ALGORITHMS C. BLIND SEARCHING D. HEURISTIC SEARCHING ⧫ Application Case 4.6: Heuristic-Based DSS Moves Milk in New Zealand



Section 4.10 Review Questions

4.11. SIMULATION ⧫ A. B. C. D. E.

F. G.

Application Case 4.7: Pratt & Whitney Canada Gets Real Savings Through Virtual Manufacturing MAJOR CHARACTERISTICS OF SIMULATION ADVANTAGES OF SIMULATION DISADVANTAGES OF SIMULATION THE METHODOLOGY OF SIMULATION SIMULATION TYPES 1.

Probabilistic Simulation 2.

Time-Dependent Versus Time-Independent Simulation 3.

Object-Oriented Simulation 4.

Visual Simulation SIMULATION SOFTWARE SIMULATION EXAMPLES ⧫ Application Case 4.8: Simulation Applications  Section 4.11 Review Questions

4.12. VISUAL INTERACTIVE SIMULATION A. CONVENTIONAL SIMULATION INADEQUACIES B. VISUAL INTERACTIVE SIMULATION C. VISUAL INTERACTIVE MODELS AND DSS  Section 4.12 Review Questions 4.13. QUANTITATIVE SOFTWARE PACKAGES AND MODEL BASE MANAGEMENT A. MODEL BASE MANAGEMENT  Section 4.13 Review Questions 4.14. RESOURCES, LINKS AND THE TERADATA UNIVERSITY NETWORK CONNECTION A. RESOURCES AND LINKS B. CASES C. MILLER’S MIS CASES D. VENDORS, PRODUCTS, AND DEMOS E. PERIODICALS F. ADDITIONAL REFERENCES G. THE TERADATA UNIVERSITY NETWORK (TUN) CONNECTION Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises

⧫ 

End of Chapter Application Case: Major League Baseball Scheduling: Computerized Mathematical Models Take Us Out to the Ballgame Questions for the Case

References

TEACHING TIPS/ADDITIONAL INFORMATION

         

The purpose of this chapter is to teach the concepts of modeling so students will begin to understand how models can be used with real world problems. It does so by going through a wide variety of model types, from the familiar (most DSS students will have encountered at least one financial model on a spreadsheet) through some that may have been seen in other courses (linear programming is often taught in operations management courses) to many that students will not have seen before. This can be a daunting amount of information to cover in the time you can allocate to a single chapter.

For this reason, you may want to select parts of this chapter to go into more deeply than others. Your choice of topics to stress will depend on your own perspective as to what is important, what isn’t. It’s still a good idea to at least touch on all the sections, though. Each one discusses a method that has been used in some DSS and that students may therefore encounter on the job. Your professional experience may tell you that some are less important than others, but that doesn’t mean any are totally unimportant.

Modeling is an applied discipline, best learned by reinforcement, through exercises and problems. Encourage the students to understand it through trial-and-error. The chapter mentions several software tools. You may wish to download, install, and use some of them. You can also work through a simple simulation by hand—for example, modeling waiting times and customers turned away at a barber shop with two barbers, two more chairs in the waiting area, and any distribution of arrival and service times you choose. Such a simulation can easily be done using a standard six-sided die to generate random numbers or by any of several other “low-tech” methods. If you have access to and are familiar with a simulation package, showing students how this simulation is programmed for that package will bring home the lessons of what simulation is really about.

You may also wish to read at least some of the articles cited in the text as examples. The text, for reasons of space, provides only a brief summary of how modeling was used in most of the situations. (see Application Case 4.8.) Most of the full articles contain additional information to flesh out the situation and add specifics that make the example come alive. This is less necessary in some of the other chapters where the topic, at least at the conceptual level, is easier for students to grasp. (The case studies suggested under “Cases” in Section 4.14 can serve this purpose, but cases in the text have the advantage of already being available.)

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 4.1 Review Questions 1.

Why is it more important to measure a player’s ability to create scoring situations than

whether he or she scores? How can this concept be transferred to industry?

A team is more interested in whether a player helps the team’s succeed than in his or her individual statistics. (“There are no prizes for plowing a straight furrow if the crops don’t come up.”) A player’s ability to create scoring situations is a better indicator of his or her contribution to team success than his or her individual scoring statistics, though less visible and perhaps harder to measure.

This concept can be transferred to industry by determining the key factors that lead to corporate success. These may not be the visible or easily measured ones. It can be used for personnel evaluation by rewarding employees for contributions to work group and project success rather than evaluating their individual work. A practical difficulty would be lack of sufficient objective data in most work situations. This obstacle must be overcome if a company wants to adopt this approach.

2.

How could college basketball and professional football benefit from analytical models for analyzing potential team members? Refer to the baseball and hockey situations described here.

The concept of assessing a player’s contribution to team wins applies to any team sport.

The specific factors that are considered will vary from sport to sport and from position to position within a sport. Hockey, for example, tabulates “assists” along with goals scored.

Other sports may have their own objective measures of non-scoring contributions to success.

3.

Consider the case of a law firm. How can the concepts illustrated here justify hiring good, multiskilled attorneys instead of superstars in one narrow area?

The law firm must determine the factors that make an attorney contribute to its success. If it turns out that the good, multiskilled attorneys make a greater contribution than the superstars, despite their receiving less publicity, the data could justify hiring them. This would require specific knowledge of that law firm. One with a focused practice, for example, might do better going for the superstar.

4.

How can the concepts described here be applied to industry? Use specific examples.

This is similar to the second part of Question 1. An example might be a software firm identifying a programmer who for some reason always works on projects that come in on time and under budget, despite not being seen as very productive. It might turn out that he or she spends a lot of time mentoring project team members, showing them coding techniques and suggesting algorithms that make the project successful overall, but taking away from his or her own apparent productivity. Such people should be rewarded and encouraged, not penalized for turning out too few lines of code per week.

5.

How can the concepts in described here be applied in government services? Use specific examples.

All government agencies have, or should have, performance metrics just as profit-making companies do. The concepts described here can help them evaluate employees, to see

who contributes the most to accomplishing their objectives per these metrics and showing the others how they can contribute more.

Section 4.2 Review Questions 1.

List three lessons learned from modeling.

This question refers to lessons found in the examples in this section, not to lessons learned from modeling in general. Many examples could be cited, among them these four: • DuPont learned how different approaches to rail transportation would work out.

• The University of Virginia Health Science Center (the institution studied in the cited Rossetti and Selandari paper) learned how human couriers and mobile robots compare in making deliveries within a hospital.

• Procter & Gamble learned the best shipping options from product sources to distribution centers.

• American Airlines learned the optimum ascent and descent profiles for its aircraft.

2.

List and describe the major issues in modeling.

Major modeling issues include: • problem identification and environmental analysis: scanning the environment to figure out what problems exist and can be solved via a model • variable identification: identifying the critical factors in a model and their relationships • forecasting: predicting the future • use of multiple models: combining them to solve many parts of a complex problem • model categories: selecting the right type of model for the problem or subproblem • model management: coordinating a firm’s models and their use • knowledge-based modeling: how to take advantage of human knowledge in modeling

3.

What are the major types of models used in DSS? The major types from Table 4.1 follow.

• Optimization with few alternatives • Optimization via an algorithm • Optimization via an analytical formula • Simulation • Heuristics (“rules of thumb”) • Predictive models • Other models

4.

Describe why it is important to establish an objective before developing a model.

The objective of any activity must be understood if the activity is to help reach it.

Modeling is no exception. Developing a model without an objective may lead to one that leaves out key factors for the decision, oversimplifies the system, or (conversely) wastes time and money on an unnecessary level of detail.

5.

Why are models not used in industry as frequently as they should or could be?

There are several reasons. The most important is that useful models are often complex, requiring major investments of time and money as well as scarce, expensive expertise.

Section 4.3 Review Questions 1.

Distinguish between a static model and a dynamic model. Give an example of each.

A static model describes relationships among parts of a system at a point in time. An example is an annual income statement. This year’s revenues, costs, etc., do not depend on those of prior years. (From a business standpoint revenue may depend on, say, R&D or marketing expenditures in prior years, managers usually budget for marketing and R&D as though it does, but that is not reflected in accounting methods.)

A dynamic model describes relationships among parts of a system as it moves through time, with its state at one instant influencing (together with its inputs) its state at the next.

An example is an inventory model, in which today’s inventory can be calculated as yesterday’s plus receipts and less shipments. One cannot calculate inventory in one period without knowing what it was in the prior period. Models of physical systems, where the position of an object at one instant depends on its position and velocity at the prior instant and any forces that are applied to it, are dynamic models.

2.

Explain how a static model can be modified to create a dynamic model.

A static model reflects relationships that exist at a point in time. These relationships must also hold in a dynamic model of the same system. To modify a static model into a dynamic model, it is necessary to add relationships between time periods so that the static model in one time period depends, in an appropriate way, on the values of system variables in the prior time period rather than standing on its own. This often increases the model’s complexity dramatically and can make it harder, if not impossible, to solve.

Section 4.4 Review Questions 1.

Define what it means to perform decision making under assumed certainty, risk, and uncertainty.

Decision making under assumed certainty: the values of all variables affecting the decision, including future values, are known or can be assumed to be known.

Decision making under risk: the exact value of decision variables is not known, but their statistical probability distributions are known (or can be assumed to be).

Decision making under uncertainty: even these distributions are not known.

2.

How can decision-making problems under assumed certainty be handled?

Decision making problems under assumed certainty can be handled by mathematical models that will yield an optimum solution.

3.

How can decision-making problems under assumed uncertainty be handled?

Decision making problems under uncertainty can be handled, first, by trying to get more information. If this is not possible or if the uncertainty remains after this has been done, it is necessary to evaluate the outcomes of many possible values of the decision variables.

The choice then depends on the decision maker’s attitude toward risk. For example, one solution may have a higher expected value than another, but also a higher likelihood of a negative outcome. A manager with an inclination to gamble might choose that solution, whereas a more conservative and risk-averse manager would choose a solution that offers a lower probable return but also minimizes the likelihood of loss.

4.

How can decision-making problems under assumed risk be handled?

Decision making problems under assumed risk can be handled by taking the probability distributions of the decision variables and working through the model with those distributions. The result will be a distribution of outcomes.

Note that the mean of the distribution of outcomes is often not the outcome that would be obtained under assumed certainty if each variable had its mean value. This is because, in a complex model, decision variables interact in complex ways and effect is not always immediately obvious.

Another approach to problems under assumed risk is to select a random value from each decision variable’s probability distribution and to run the model, under assumed certainty, with those values. This is repeated until a statistical distribution of results is obtained. This approach is used when a model is too complex to work through with statistical distributions directly.

Section 4.5 Review Questions 1.

What is a spreadsheet?

“A program designed to perform general computational tasks using spatial relationships rather than sequential execution as their primary organizing principle.” (Wikipedia) “A computer program in which figures arranged in the rows and columns of a grid can be manipulated and used in calculations.” (Oxford American Dictionary) This section does not define the concept, relying on student’s experience to tell them what a spreadsheet is. Given the audience of this book, that should normally be appropriate. Instructors who assign this question with no further explanation should expect some students to search for definitions (finding one or more like the above two)

whereas others come up with their own and still others use descriptive phrases from the text. If a specific approach is wanted, that should be stated in advance.

2.

What is a spreadsheet add-in? How can add-ins help in DSS creation and use?

Spreadsheet add-ins are small programs designed to extend the capabilities of a spreadsheet package. They help in DSS creation and use because many add-ins are designed specifically for that purpose, often by structuring and solving specific types of models.

3.

Explain why a spreadsheet is so conducive to the development of DSS. A spreadsheet is conducive to DSS development because it provides an easily understood metaphor for the computation and typically incorporates many powerful modeling functions.

Section 4.6 Review Questions 1.

What is a decision table?

A decision table is a way to organize information in a systematic way to prepare it for analysis. Typically, decisions are shown along one axis, states of nature on the other. The range of outcomes that can result from any decision, given the possible states of nature, can then be easily seen in one column (or row).

2.

What is a decision tree?

A decision tree is an alternative representation of a decision situation, in which choices and states of nature are shown as alternating nodes along the branches of a tree. The range of outcomes that can result from any decision, given the possible states of nature, can then be easily seen by following all branches from that decision to their ends.

3.

How can a decision tree be used in decision making?

By showing the decision maker the possible outcomes that could result from a given choice, the tree gives the decision maker information by which to compare choices.

4.

Describe what it means to have multiple goals.

Having multiple goals means that a decision maker hopes to obtain the best possible combination of several factors, all of which depend on the decision to be made. For example, a student may want to find an instructor who is entertaining, has a good reputation for teaching, grades easily, assigns little homework, and whose section meets at convenient times. It will be difficult, if not impossible, to achieve all these at the same time. Multiple goals require willingness to compromise.

(You may want to defer this question until students have read Section 4.9, or to refer them forward to it at this point.)

Section 4.7 Review Questions 1.

What is a decision variable?

A decision variable is a data element controlled by the decision maker, whose possible values describe alternative courses of action.

2.

List and briefly discuss the three major components of linear programming.

Of the four components of any decision support mathematical model, linear programming uses result (outcome) variables, decision variables and uncontrollable variables (parameters). Linear programming models do not use the fourth component, intermediate result variables.

There are other possible answers that do not tie in to the concepts of this section.

For example, one could say that the three major components of a linear programming problem are its objective function, its decision variables, and its constraints. You might wish to connect this question to Section 4.8.

3.

Explain the role of intermediate result variables.

An intermediate result variable is the result of one part of a model, which in turn is input to another part of the model. Its role is to simplify the modeling process by breaking a complex problem down into multiple simpler subproblems, to present data of management interest that might not otherwise be visible between system inputs and final outputs, and to aid in debugging a model by helping a model developer zero in on the cause of any difficulties.

Section 4.8 Review Questions 1.

List and explain the assumptions involved in LP. The LP approach involves both economic and technical assumptions.

Economic assumptions are: • Returns from different allocations can be measured by a common unit (e.g., dollars, utility).

• The return from any allocation is independent of other allocations.

• The total return is the sum of the returns yielded by the different activities.

• All data are known with certainty.

• Resources are to be used in the most economical manner.

Technical assumptions are: • • •

The objective function (that is to be maximized) is a linear combination of the outputs.

Each output (decision variable) uses a linear combination of the inputs.

Constraints on inputs are linear (or constant, which is a special case of linear) inequalities.

The requested explanations should show that the student understands each of the concepts, and is not simply parroting items from a list.

2.

List and explain the characteristics of LP. Linear programming is an optimization method used to solve problems in which the objective function and the constraints are all linear. It rests on the assumptions listed in the previous answer. LP problems can be solved by a wide variety of software for all popular computers.

3.

Describe an allocation problem.

An allocation problem is a linear programming problem in which limited resources can be allocated among several possible uses, each of which yields a known return per unit and are subject to constraints.

4.

Define the product-mix problem.

The product-mix problem is a linear programming problem in which a variety of different products are made from common resources. Each product requires a known resource mix and has a known profitability. Some resources are limited. Total profitability is to be maximized.

A product-mix problem can be viewed as an allocation problem. The difference is that a person formulating the problem as a product-mix problem is typically interested in the quantities of each product to be produced, while a person formulating the same problem as an allocation problem is usually interested in the quantities of each resource to be used. The solutions are identical, and each approach can produce both answers.

5.

Define the blending problem.

The blending problem is a linear programming problem in which resources can be used in different ways to create a desired end product. The ways in which the resources combine to create the characteristics of the end product are known. Total cost of the end product is to be minimized.

6.

List several common optimization models.

Several common optimization models are listed in the shaded box just before these review questions. These include the assignment problem, dynamic programming, goal programming, investment, linear and integer programming, network models for planning and scheduling, replacement (capital budgeting), simple inventory models (e.g.,

economic order quantity) and transportation (minimizing cost of shipments, route optimization). A motivated student will be able to find others through a Web search.

Section 4.9 Review Questions 1.

List some difficulties that may arise when analyzing multiple goals.

• • • • • •

2.

It is usually difficult to obtain an explicit statement of the organization’s goals.

The importance of specific goals may change over time or in different situations.

Goals and subgoals are viewed and weighted differently by different people and in different parts of the organization. (Composite of multiple items from the list in the text.)

Goals change in response to changes in the organization and its environment.

The relationship between alternatives and their role in determining goals may be difficult to quantify.

Participants access the importance (priorities) of the various goals differently;

List the reasons for performing sensitivity analysis.

Sensitivity analysis attempts to assess the impact of a change in input data or parameters on the result variable(s). Reasons for using it listed in this section include: • Revising models to eliminate too-large sensitivities • Adding details about sensitive variables or scenarios • Obtaining better estimates of sensitive external variables • Altering a real-world system to reduce actual sensitivities • Accepting and using the sensitive (and hence vulnerable) real world, leading to the continuous and close monitoring of actual results Another reason for using sensitivity analysis is to understand the sensitivity of different decision choices to variations in external conditions. One alternative may promise excellent results under nominal conditions but performs poorly if conditions vary from the nominal. Another may not turn out quite as well if all goes according to plan but will perform better if conditions vary from it. For example, one possible supplier may offer low prices for deliveries according to a pre-planned schedule but impose large penalties if the schedule is changed; another might have a higher base price but lower penalties for changing the delivery schedule.

3.

Explain why a manager might perform what-if analysis.

A manager might perform what-if analysis to find out what will happen if a particular action is taken. A manager might also perform what-if analysis to try out several alternatives, choosing the one that works out best.

4.

Explain why a manager might use goal seeking.

A manager might use goal seeking to find values of decision variables that enable him or her to meet a predetermined criterion. For example, a manager may need to find an advertising budget that will reach X households at a cost of not over $Y. A drawback is that goal seeking can encourage “satisficing,” discussed in Chapter 2. A manager who finds an advertising program that meets the above objectives may not be motivated to find one that reaches more households for the given cost, or the required number at less cost.

Section 4.10 Review Questions 1.

What is a search approach?

A search approach is a general method, or a category of closely related methods, used to find the best solution to a problem based on some criterion. The criterion usually involves maximizing or minimizing a numeric value.

2.

List the different problem-solving search methods.

Analytical techniques, algorithms, blind searching, and heuristic searching

3.

What are the practical limits to blind searching?

Practical limits are the time and computer resources available for the search.

4.

How are algorithms and heuristic search methods similar? How are they different?

The similarity is that they are both search approaches by the above definition (see Q. 1 above).

The difference is that algorithms always find a solution, if one exists within the constraints of the method, and that solution can always be proven to be mathematically optimal. Heuristic search methods may or may not find one. If one is found, it may or may not be “best” by the stated criteria. Their advantages over algorithms are that they can be used for some problems that lack applicable algorithms, and they are often faster.

(This answer uses the term “algorithm” within the context of solving business problems. The term has a more general meaning: “an explicit numerical step-by-step procedure that produces a solution to a known problem.” Some algorithms do not involve optimization, as an algorithm to calculate square roots. Others apply to areas where no mathematical optimum exists, for example, Google’s search algorithm mentioned in the text. There is no way to know if the pages it finds are best for a particular purpose, since people will disagree on what “best” means in this context. A person searching for “diamonds,” wanting information on gemstones, might find its answers useful, but the same results would be less useful to someone in need of information on baseball parks, geometric shapes, or the suit in a deck of cards.)

Section 4.11 Review Questions 1.

List the characteristics of simulation.

• • • • •

2.

An imitation of reality rather than a representation of it A technique for conducting experiments Fewer simplifications than with most other methods Descriptive rather than normative; users can use simulation results to search if desired Usually used for problems too complex for numerical optimization methods

List the advantages and disadvantages of simulation.

Advantages: • The theory is fairly straightforward.

• Time can be compressed a great deal, quickly giving a manager some feel as to the long-term effects of many policies.

• Simulation is descriptive rather than normative. This allows a manager to pose what-if questions. Managers can use trial-and-error quickly, at little expense, accurately, and with low risk.

• A manager can experiment to determine which decision variables and which parts of the environment are really important, and with different alternatives.

• An accurate simulation model requires intimate knowledge of the problem, thus forcing its builder to interact with the manager. This leads to better understanding of the problem and the potential decisions available.

• The model is built from the manager’s perspective.

• No generalized understanding is required of the manager; as every component in the model corresponds to part of the real system.

• Simulation can handle a wide variety of problem types, such as inventory and staffing, as well as higher-level managerial functions, such as long-range planning.

• Simulation can include most real complexities of problems; simplifications are not needed. For example, it can use real probability distributions rather than approximate theoretical ones.

• Simulation automatically produces many important performance measures.

• Simulation is often the only DSS modeling method that can readily handle relatively unstructured problems.

• Simulation generally can include the real complexities of problems; simplifications are not necessary.

• Simulation automatically produces many important performance measures.

• Simulation is often the only DSS modeling method that can readily handle relatively unstructured problems.

• There are some relatively easy-to-use simulation packages. These include add-in spreadsheet packages, influence diagram software, Java-based (and other Web development) packages, and visual interactive simulation systems.

Disadvantages: • An optimal solution cannot be guaranteed, though relatively good ones are generally found.

• Simulation model construction can be a slow and costly process, although newer modeling systems are easier to use than ever.

• Solutions and inferences from a simulation study are usually not transferable to other problems because the model incorporates unique problem factors. It doesn’t generalize.

• Simulation is sometimes so easy to explain to managers that analytic methods are often overlooked. (This is not a disadvantage of the method, but a practical caution about using it.)

• Simulation software sometimes requires special skills because of its complexity.

Some simulation packages require the model developer to be, in effect, a programmer.

3.

List and describe the steps in the methodology of simulation.

The steps are shown in Figure 4.11. They are: • Define the problem • Construct the simulation model • Test and validate the model • Design the experiments • Conduct the experiments • Evaluate the results • Implement the results

4.

List and describe the types of simulation.

Important types of simulation include probabilistic simulation, time-dependent and timeindependent simulation, object-oriented simulation, and visual simulation.

Probabilistic simulation is simulation where one or more of the independent variables is described by a statistical distribution.

Time-dependent and time-independent simulations are distinguished by whether or not it is important, in order to model the system correctly, to know exactly when events occur.

Object-oriented simulation refers to simulation models built using object-oriented programming methods. This is more a function of the tools used than of the simulation model itself. (If your students are interested in programming, you can point out here that much pioneering work in object-oriented programming was done in developing simulation languages such as Simula.)

Visual simulation is discussed in the next section and defined in the answer to Review Question 1.

Section 4.12 Review Questions 1.

Define visual simulation and compare it to conventional simulation.

Visual simulation uses graphical representation to show the situation to the end user. It does everything a conventional simulation does, which is any technique for conducting experiments (such as what-if analyses) with a digital computer on a model of a management system, but does it using visually pleasing and informative representation.

Visual interactive simulation is a type of visual simulation in which input is provided directly to the visual representation of the system, with results visible immediately or nearly so.

2.

Describe the features of VIS (i.e., VIM) that make it attractive for decision makers.

The main attraction of VIS (VIM) is the graphical display. This type of interaction can help managers learn about the decision making situation, though some people respond to graphical displays better than others. Non-technical managers, who may not understand a situation from data in tables or graphs, can grasp what goes on in a visual presentation easily.

3.

How can VIS be used in operations management?

According to the text, VIS (VIM) “has been used in several operations management decisions.” Examples given there include plant operations and waiting lines. The two are related, as waiting line buildup indicates an unbalanced plant. With VIS, a manager can see work in progress piling up at one stage of a process—which is far less expensive than watching it pile up in the real factory!

4.

How is an animated film like a VIS application?

An animated film is (with modern animation techniques; this question is not about handdrawn animations of a few decades ago) a computer-generated representation of a physical system, as is a VIS application. While some animated films use computers simply to generate final artwork and to fill in the gaps between key frames created manually, others use complex algorithms to simulate realistic motion of animated people or animals, calculate trajectories of physical objects and determine what happens when two objects interact (e.g., a vehicle hitting a wall). These are true VIS applications, even if their purpose is not to support management decision making.

Section 4.13 Review Questions 1.

Explain how a spreadsheet system such as Excel can be used in DSS modeling.

Section 4.5, which students will have read by now, is devoted to this topic. This section points out that Excel includes many built-in models. It can thus produce the needed results in many situations as long as the required data can be entered or imported into a spreadsheet.

2.

Explain why OLAP is a kind of modeling system.

OLAP is a flexible approach to data analysis. OLAP packages have evolved from a wide range of origins. Since analyzing data often requires models, OLAP software vendors have added modeling capabilities to their systems, making OLAP a modeling system in addition to its other features.

3.

Identify three classes of models and list two kinds of problems each can solve.

Three classes of models mentioned in this chapter are quantitative software packages, the Excel spreadsheet system, and OLAP systems.

Quantitative software packages can solve general optimization problems as well as problems that arise in specific applications. Section 4.7 (The Structure of Mathematical Models for Decision Support), 4.8 (Mathematical Programming Optimization, which includes linear programming) and 4.10 (Problem-Solving Search Methods) contain many examples.

The kinds of problems Excel can solve are discussed at length in Section 4.5.

OLAP systems can address (though perhaps not solve) any problem whose answer lies in a large data warehouse. These tend to be problems where historical data can serve as a guide to the future. Two examples are (a) airline yield management, where historical data indicate what fraction of the seats that will ultimately be sold on a given flight will be sold X days before it leaves; and (b) retail demand analysis, where it can indicate how sales break down by any set of factors and what the trends are in this.

4.

List the reasons model management is difficult.

The development of generic model base management systems is difficult because there are so many different model classes that can be used. It is hard to know the ones to include and the ones that may need to be modified for use. Further, the selection of an appropriate model requires expertise because several models may be applicable to any one situation. Finally, each organization uses models differently, unlike database management systems, where most organizations’ databases follow the row-and-column relational model.

Section 4.14 Review Questions Section 4.14 has no review questions.

ANSWERS TO END OF SECTION REVIEW QUESTIONS 1.

       

What is the relationship between environmental analysis and problem identification?

Environmental analysis is “the monitoring, scanning and interpretation of collected information.” Its purpose is to detect problem situations, after which they can be identified more precisely.

2.

Explain the differences between static and dynamic models. How can one evolve into the other?

A static model describes relationships among parts of a system at a point in time. A dynamic model describes relationships among parts of a system as it moves through time, with its state at one instant influencing (together with its inputs) its state at the next.

The relationships that hold in a static model at a point in time must also hold in a dynamic model of the same system at every time. To modify a static model into a dynamic model, it is necessary to add relationships between time periods so that the static model in one time period depends, in an appropriate way, on the values of system variables in the prior time period rather than standing on its own. This may increase the model’s complexity dramatically and make it harder, if not impossible, to solve.

3.

What is the difference between an optimistic approach and a pessimistic approach to decision making under assumed uncertainty?

An optimist makes the choice that has the best best-case outcome, even if it gives worse results under other conditions. The pessimist makes the choice as the best worst-case outcome, even if it gives worse results under other conditions.

For example: Suppose one choice has equally probable results of +$80 and +$30.

Another has equally probable results of +$120 and –$10. (The expected value of both is +$55, so a rational decision maker with a linear utility function would call it a tie.) An optimist will choose the second because, in the best case, it will yield +$120: better than the best-case result of +$80 for the other option. A pessimist will choose the first because, at worst, it will yield +$30: better than the worst-case result of –$10 for the other choice.

4.

Explain why solving problems under uncertainty sometimes involves assuming that the problem is to be solved under conditions of risk.

Solving problems under uncertainty means the decision maker has no idea of likelihood of one outcome or another. However, decision makers usually have some idea of the relative probability of certain influencing factors. By assuming approximate probabilities (changing the situation to one of risk even if the probabilities are not really known) the decision maker can predict the results to be expected from a course of action. Sensitivity analysis can then be used, if desired, to determine how much the prediction depends on the accuracy of the estimates.

5.

Excel is probably the most popular spreadsheet software for PCs. Why? What can we do with this package that makes it so attractive for modeling efforts?

The reasons why Excel is the most popular spreadsheet program for PCs have more to do with industry dynamics and Lotus’s slowness in porting 1-2-3 to Windows than with its technical merits. Although undoubtedly a good program, it achieved dominance in the mid-1990s when it was not markedly superior to competitors. Its superiority over most competing products today is due to the limited development resources available for products with far smaller market shares and came about only after its dominance was already well established. A full discussion of how and why Excel became the most popular PC spreadsheet program is beyond the scope of a DSS book or course.

Once a program such as Excel achieves a dominant market share, factors such as size and inertia of its user community, the infrastructure that builds up around it (add-ins, books, etc.) and (in its case) bundling into Microsoft Office combine to help it maintain that position.

Excel’s attractiveness for modeling efforts is due to five factors: the intuitiveness of the spreadsheet metaphor, its ubiquity (which makes Excel models easy to share), user familiarity with it, its flexibility, and its power. You can do many things with Excel. Its ease of use facilitates creating ad-hoc DSS. Macros allow complex procedures to be automated, with VBA available when macros fall short. Built-in goal-seeking and scenario analysis capabilities enable a model, once developed, to become part of a larger problem-solving process.

In short: (nearly) everyone has Excel, and Excel has what (nearly) everyone needs.

(This question is related to Review Question 1 of Section 4.13.)

6.

Explain how OLAP provides access to powerful models in a spreadsheet structure.

OLAP applies the familiar spreadsheet row-and-column grid metaphor to data extracted from a data warehouse, giving modelers the best of both worlds. Consistently with their purpose, OLAP products incorporate an extensive library of statistical models and other data analysis tools. These provide it with much of its power.

In addition, the Pivot Table feature of Excel (and of some other spreadsheet programs) is well suited to OLAP applications. It may not be the best OLAP tool available, but nearly everyone has it, and it gets the job done most of the time.

7.

Explain how decision trees work. How can a complex problem be solved by using a decision tree?

This question is essentially the same as Review Questions 2 and 3 of Section 4.6, combined.

8.

Explain how LP can solve allocation problems.

LP solves allocation problems by using mathematical relationships (inputs required per unit of output) under known constraints (resource availability, etc.) to find the allocation

of finite resources that produces the greatest value (highest value of the objective function).

9.

What are the advantages of using a spreadsheet package to create and solve LP models?

What are the disadvantages?

Advantages: The spreadsheet framework is well understood by many modern managers, linear programming models fit well into the tabular format, solutions can be stored directly in the spreadsheet, and it is possible to explain linear programming using what-if analysis directly in a spreadsheet for small problems. Spreadsheet models are not bound by the theoretical limitations of the linear programming approach, so they can (for example) work with nonlinear constraints.

Disadvantages: Large problems are difficult to create, debug, modify, and interpret. The fact that spreadsheets are a general-purpose tool, not a specialized one, makes them slower than LP software, and special structures, such as for network optimization problems, cannot be exploited for efficient solution.

10.

What are the advantages of using an LP package to create and solve LP models? What are the disadvantages?

Advantages: The software was designed to do that one thing, it can be quite fast, the best packages can interface with large-scale databases, and some systems even feature modeling languages to enter problems.

Disadvantages: The output can be cryptic, it may require an analyst to operate and even develop the model, the software can be cumbersome to operate, and limitations of a particular package can make it difficult to solve large problems.

11.

What is the difference between decision analysis with a single goal and decision analysis with multiple goals (i.e., criteria)? Explain in detail the difficulties that may arise when analyzing multiple goals.

In a single goal we compare only one result (e.g., profit). In multiple goals we compare several results. Since in the latter case we rarely find an alternative who is superior along all the criteria (we all would like to find a partner who is the richest, sexiest, nicest, smartest, etc.), it is necessary to prioritize goals, weight them, or combine them in some other way in order to make a choice.

12.

Explain how multiple goals can arise in practice.

It is rare to have only one objective in making a business or personal decision. In choosing a flight, for example, people consider schedule, price, airline preference and more. When multiple stakeholders are involved, which is common in business decisions, there are even more goals to compromise among. (Consider, for example, three business travelers trying to pick a single flight. They might agree on cost, but have different schedule preferences and want frequent flyer credit on different airlines.)

13.

Compare and contrast what-if analysis and goal seeking.

What-if analysis begins with conditions and determines their result. Goal seeking begins with a desired result and determines the conditions that will produce it. That often calls for using what-if analysis in an iterative process: assume some conditions, examine the result, on that basis choose other conditions, examine their result, and continue (using an intelligent search process that considers how prior condition changes affected the result) until the desired result is reached.

14.

Describe the general process of simulation.

This question is essentially the same as Review Question 3 of Section 4.11. Some instructors may prefer the list format requested there, whereas others may prefer the text description implicitly requested here, but the content of the answers would be the same. It would be redundant to assign both.

15.

List some of the major advantages of simulation over optimization and vice versa.

Advantages: Simulation can deal with realistic, complex situations (it models risk), simulation theory is straightforward, time is compressed, there is no need for restrictive assumptions, and it fits how managers think (models are built from the manager’s perspective).

Disadvantages: An optimal solution is not guaranteed; construction can be slow; and simulation software is often not user-friendly, requiring programming.

16.

What are the advantages of using a spreadsheet package to perform simulation studies?

What are the disadvantages?

Advantages: Ease of use because most managers understand the structure of spreadsheets.

Disadvantages: (For complex simulations) slow running time and the fact that the spreadsheet metaphor does not incorporate facilities for many types of simulations, so the developer must work around the limitations of the tool to construct these. In those cases, using a simulation package would be more straightforward.

17.

Compare the methodology of simulation to Simon’s four-phase model of decision making. Does the methodology of simulation map directly into Simon’s model? Explain.

Both start off with a problem identification phase, called Intelligence in Simon’s model.

Simon’s design phase corresponds to Steps 2–4 of the simulation model: constructing it, testing and validating it, and designing the experiment. Conducting simulation experiments and evaluating the results would be Simon’s choice phase. Finally, both have an implementation phase.

18.

Many computer games can be considered visual simulation. Explain why.

Many computer games simulate a fictional (realistic or fantasy) environment and the action that takes place in it. They use visual presentation to show the progress of the simulation. This enhances the understanding of the environment and how the game is played, often providing immediate feedback as to the success or failure of a player action.

19.

Explain why VIS is particularly helpful in implementing recommendations derived by computers.

Implementation is a process of human change. People must be motivated to change. They are more likely to accept change if they see its benefits. VIS makes it possible to see those benefits.

20.

There are hundreds of DBMS packages on the market. Explain why there are no packages for MBMS. The answer to Review Question 4 of Section 4.13 discussed the wide variety of models that exist, in contrast to the single paradigm that underlies the database management systems used by nearly all organizations. This makes it harder to define a generic MBMS that will serve the needs of many organizations.

Also, since fewer organizations have multiple models to manage than have data to manage, the demand for model management is much lower.

Finally, model management requires expertise that is much scarcer than what is required for database management.

21.

Does Simon’s four-phase decision-making model fit into most of the modeling methodologies described? How or how not?

Yes. They have a problem identification phase, followed by design and choice phases and then an implementation phase. This is only to be expected, since Simon’s model is a general description of how decisions are made. The modeling methodologies described, as specific approaches to specific decisions, of necessity fall within the general framework.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

Describe the conflicting goals of the MLB schedule. Relate them to the stakeholders as you describe them.

The case mentions minimizing travel inconvenience, varying the schedule so the same teams don’t play each other in too many consecutive games, and scheduling attractive team rivalries for important dates. (These descriptions generalize the examples given.)

Other requirements are to comply with constraints on the length of travel periods and on schedule balance.

The major stakeholders here are the sports teams (who are interested in financial results) and the players (who also care about financial results, but are interested in limiting travel time as well). The media, especially broadcast media, and fans are also stakeholders; they care about having attractive games scheduled at convenient times—but the meaning of an “attractive” game and a “convenient” time will vary from one stakeholder to the next.

2.

Explain why the MLB scheduling situation results in such a complex problem and why computerized mathematical models are ideally suited to solving it.

MLB scheduling is a complex problem because of the number of variables (30 teams, 162 games each) and the constraints (number of games between each pair of teams, travel rules, etc.).

Computerized models are needed because analyzing a schedule of this complexity properly is beyond the human capability. Within the overall area of computer models, “mathematical models” is the name of the category that describes this modeling approach. (See Section 2.3.)

3.

Describe the kinds of benefits that can be obtained by using this type of scheduling modeling approach.

This approach can result in a schedule that is fair to all stakeholders. Beyond being objectively fair, it will also be perceived as fair and—especially if on occasion a schedule seems unfair to a given stakeholder—as having been arrived at by a fair process.

4.

Describe some non-sports areas in which these kinds of methods and models can be applied.

These approaches can be used for any scheduling problem with constraints, such as scheduling courses at a school or college to minimize student course conflicts while creating feasible faculty schedules, or matching rental car availability with customer requests while minimizing “ferry runs” needed solely to move an automobile from one location to another.

5.

Even though the client managers (i.e., decision makers), really did not understand the methodology used, they were able to use the results effectively. Explain why this is a reasonable thing for them to do.

The results were presented in a non-technical way that decision makers could use without understanding how they were derived. They knew the problem was too complex for simple solution methods, understood the basic concepts behind the methodology even though they did not understand its details, and based on inspecting the results (from the standpoint of years of experience) were willing to believe that the methodology worked as advertised.

Data Warehousing

CHAPTER

5

Learning Objectives for Chapter 5 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the basic definitions and concepts of data warehouses Understand data warehousing architectures Describe the processes used in developing and managing data warehouses Explain data warehousing operations Explain the role of data warehouses in decision support Explain data integration and the extraction, transformation, and load (ETL) processes Describe real-time (active) data warehousing Understand data warehouse administration and security issues

CHAPTER OVERVIEW This is the data warehousing chapter of the book. Previous chapters, especially Chapter 3, led up to it; later chapters will use it as they discuss DW applications such as business analytics (Chapter 6) and data mining (Chapter 7), but the guts of it are right here. If students don’t “get” the data warehouse idea here, they’ll be at a disadvantage later. This is different from some of the earlier chapters, which introduced topics at an overview level but where we knew at the time that details would come later. “Later” is now.

CHAPTER OUTLINE 5.1

OPENING VIGNETTE: CONTINENTAL AIRLINES FLIES HIGH WITH ITS REAL-TIME DATA WAREHOUSE A. BIG PROBLEMS B. SOLUTION C. BENEFITS  Questions for the Opening Vignette

95

96

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

D.

WHAT WE CAN LEARN FROM THIS VIGNETTE

5.2

DATA WAREHOUSING DEFINITIONS AND CONCEPTS A. WHAT IS A DATA WAREHOUSE? B. CHARACTERISTICS OF DATA WAREHOUSING C. DATA MARTS D. OPERATIONAL DATA STORES E. ENTERPRISE DATA WAREHOUSES (EDW) F. METADATA  Section 5.2 Review Questions

5.3

DATA WAREHOUSING PROCESS OVERVIEW ⧫ Application Case 5.1: Data Warehousing Supports First American Corporation’s Corporate Strategy  Section 5.3 Review Questions

5.4 DATA WAREHOUSING ARCHITECTURES A. ALTERNATIVE ARCHITECTURES  Section 5.4 Review Questions 5.5

DATA INTEGRATION AND THE EXTRACTION, TRANSFORMATION, AND LOAD (ETL) PROCESS A. DATA INTEGRATION ⧫ Application Case 5.2: Bank of America’s Award-Winning Integrated Data Warehouse B. EXTRACTION, TRANSFORMATION, AND LOAD  Section 5.5 Review Questions

5.6

DATA WAREHOUSE DEVELOPMENT ⧫ Application Case 5.3: Things Go Better with Coke’s Data Warehouse A. DATA WAREHOUSE VENDORS B. DATA WAREHOUSE DEVELOPMENT APPROACHES 1.

The Inmon Model: The EDW Approach 2.

The Kimball Model: The Data Mart Approach 3.

Which Model Is Best? ⧫ Application Case 5.4: HP Consolidates Hundreds of Data Marts into a Single EDW C. ADDITIONAL DATA WAREHOUSE DEVELOPMENT CONSIDERATIONS D. DATA WAREHOUSE STRUCTURE: THE STAR SCHEMA ⧫ Technology Insights 5.5: How About a Hosted Data Warehouse?

E. DATA WAREHOUSING IMPLEMENTATION ISSUES F. MASSIVE DATA WAREHOUSES AND SCALABILITY  Section 5.6 Review Questions

5.7

REAL-TIME DATA WAREHOUSING

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

⧫ ⧫  5.8

97

Application Case 5.6: Egg plc Fries the Competition in Near-RealTime Technology Insights 5.7: The Real-Time Realities of Active Data Warehousing.

Section 5.7 Review Questions

DATA WAREHOUSE ADMINISTRATION AND SECURITY ISSUES ⧫ Technology Insights 5.8: Ambeo Delivers Proven Data Access Auditing Solution  Section 5.8 Review Questions

5.9

RESOURCES, LINKS, AND THE TERADATA UNIVERSITY NETWORK CONNECTION A. RESOURCES AND LINKS B. CASES C. VENDORS, PRODUCTS AND DEMOS D. ADDITIONAL REFERENCES E. THE TERADATA UNIVERSITY NETWORK (TUN) CONNECTION Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises ⧫ End of Chapter Application Case: Real-Time Data Warehousing at Overstock.com  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

Expect to spend some time on this chapter, as understanding its content is crucial and many of the concepts are not intuitive to students whose major computer exposure has been at the personal level. You should prepare yourself with some technical examples of how things work, such as screen shots from some of the tools mentioned in the chapter, as most of the technical discussion here is at a more conceptual level. The business discussion is tangible, but the technical part is less so. It will be up to you to connect it to the students.

Section 5.2 defines several fundamental concepts. Students must understand that these are not definitions for the sake of definitions (every textbook has some of those, this one is no exception) but are important to anyone working in the field. The characteristics of a data warehouse in its extended definition, for example, is given not because some researcher thought they’d be theoretically nice, but, more specifically, because they’ve turned out to be important in practice. So, it’s important to really know what each of them is about. (The concept of an

98

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

operational data store should already be familiar to students, though perhaps not by that name.

They should know what metadata are from their use with general databases, too.)

Section 5.3 is brief. It can be covered quickly. It’s important to understand what each step does, but the details of things like ETL will be covered later in this chapter.

Section 5.4, by contrast, is eight pages long—though a lot of that is diagrams. (You might have to tell students that “MDB” in some of them stands for “Multidimensional Database.” The key point here is that there are a lot of options, the correct one can only be chosen when something is known about the characteristics of the application, and that in most cases more than one will work reasonably well.

Section 5.5 discusses ETL, a process that never shows up outside a data warehouse context and that students may therefore never have heard of before this course. (They will have heard of it before this chapter, though. Chapter 3 introduced it in Section 3.6.) The need for ETL arises because databases aren’t all in the same format; chances are that few, if any, of the source databases are in the format of the DW—and even if the formats match, most databases contain dirty data. This is a good place to show screen shots of ETL tools so students will really appreciate how they work.

Section 5.6, another long one, is on the development process. Students will have studied the development and implementation of information systems in general in their introductory MIS course and probably gone deeper into it in Systems Analysis and other courses. Here, it helps to focus on what’s different about data warehouses. For example, while training is needed for both conventional systems and data warehouses, the training needed for a DW is at a higher level: more information literacy than computer literacy. A systems analyst who is used to the correct type of training for ERP or SCM systems and who attempts to replicate that experience for a DW user community, is courting disaster.

This section covers more than the database development suggested by its title. It also discusses the internal structure of a data warehouse database. Its focus is on the star schema.

That’s as close as this chapter and this book get to how a data warehouse is actually organized.

Students who’ve studied database management—likely to be most of the class in a senior-level business school course for MIS concentrators—will want more tangible information here. You can provide this with examples showing how a simple set of historical sales data with several dimensions could be stored in an operational database and in a data warehouse organized by the star schema.

You may also wish to add a multidimensional database to this example. These were mentioned several times in Section 5.4 but are not mentioned here. While these aren’t promoted for data warehouses quite as much as they were a few years ago (the statement on p. 217, that most DWs are built with RDBMS, is true today) there are still good products that support them, applications to which they are well suited, and a lot are in regular use, so students may encounter them on the job. A conceptual understanding of how they differ from a relational database organized by the star schema can be good to have.

If you have provided your students with a good foundation up to this point, Section 5.7 should go quickly. The business case for real-time DWs is well presented, and the technology introduces no new conceptual difficulties.

Section 5.8 returns to issues that students will be familiar with from a general database management context. The need for “establishing effective corporate security policies and procedures,” for instance, is not unique to data warehouses. Therefore, you may, again, want to

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

99

focus on what’s different about them. Answers to Question for Discussion 5 at the end of the chapter can lead to a good class discussion of this.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 5.1 Review Questions 1.

Describe the benefits of implementing the Continental Go Forward strategy.

This strategy consisted of a number of interrelated, concurrent actions. The first version of the overall strategy had the benefit of restoring Continental (CO) to profitability and giving it first-place rankings by many airline industry metrics. The second phase of the strategy led to savings of $41 million and a reduction of $7 million in fraud in the first year alone. Its revenue increased by over $500 million in six years. A data warehouse played a critical role in the second phase.

2.

Explain why it is important for an airline to use a real-time data warehouse.

It’s important for an airline to use real-time data warehouse because many airline decisions cannot be made with week-old, or even day-old, data.

An example is frequent flyer award availability on a given flight. Airlines limit these so as not to give away too many seats that would otherwise be sold. Award seat allocation is usually automated. Travelers can check availability online. American Airlines (and probably others) offers expanded award availability to the 30,000 or so people who fly at least 100,000 miles per year with them. When one of these wants an award seat that is not available online, he or she can request it by phone. The agent must decide whether or not to make it available. If the flight is selling slowly, the traveler gets the seat, even if the computer hasn’t allocated it for award. If it is likely to sell out, the seat isn’t offered, even to this select group. To make this decision, telephone agents (and the Yield Management staff, which agents can consult) need up-to-the-minute, or at least up-to-the-hour, information.

3.

Examine the sample system output screen at teradata.com/t/page/139245/. Describe how it can assist the user in identifying problems and opportunities.

This screen appears to show the relationship among three independent variables and one dependent variable. The independent age are the age of a caller (column), the time the person tends to call (color of rectangle) and the number of complaints by that category of caller (height of rectangle). The dependent variable is profitability, shown by the width of the rectangle. The chart shows that weekend callers, while generating neither the highest nor the lowest number of complaints, are the airline’s most profitable customers. This information can be used to find out why some other groups have more complaints, to give higher priority to weekend callers, and for many other purposes.

4.

Identify the major differences between the traditional data warehouse and a real-time data warehouse, as was implemented at Continental.

100

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

A traditional data warehouse moves data from operational databases to the DW on a scheduled basis, typically daily or weekly. This provides consistent data for analyses performed during one update cycle, but does not make current information available for decisions that require it. A real-time DW, as was implemented at CO, moves data into the DW on an hourly or even more frequent basis.

5.

What strategic advantage can Continental derive from the real-time system as opposed to a traditional information system?

By having real-time data available through its data warehouse, CO can make decisions using up-to-date information. While data warehousing applications which focus on longterm decisions aren’t affected much by the last hour’s, day’s or even week’s data, lowerlevel short-term decisions are. As the use of the DW is extended to these decisions and down in the organization, current data become necessary. By having real-time (or nearreal-time) data in the system, CO obtains a strategic advantage by making better decisions. These decisions can affect any and all of Porter’s five competitive forces referred to in Section S.5 (and probably familiar to your students from other courses as well.)

Section 5.2 Review Questions 1.

What is a data warehouse?

A data warehouse is defined in this section as “a pool of data produced to support decision making.” This focuses on the essentials, leaving out characteristics that may vary from one DW to another but are not essential to the basic concept.

The same paragraph gives another definition: “a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management’s decision-making process.” This definition adds more specifics, but in every case appropriately: it is hard, if not impossible, to conceive of a data warehouse that would not be subject-oriented, integrated, etc.

2.

How is a data warehouse different from a database?

Technically a data warehouse is a database, albeit with certain characteristics to facilitate its role in decision support. Specifically, however, it is (see previous question) an “integrated, time-variant, nonvolatile, subject-oriented repository of detail and summary data used for decision support and business analytics within an organization.” These characteristics, which are discussed further in the section just after the definition, are not necessarily true of databases in general—though each could apply individually to a given one.

As a practical matter most databases are highly normalized, in part to avoid update anomalies. Data warehouses are highly denormalized for performance reasons.

This is acceptable because their content is never updated, just added to. Historical data are static.

3.

What is an ODS?

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

101

Operational Data Store is the database from which a business operates on an on-going basis.

4.

Differentiate among a data mart, an ODS, and an EDW. An ODS (Operational Data Store) is the database from which a business operates on an ongoing basis.

Both an EDW and a data mart are data warehouses. An EDW (Enterprise Data Warehouse) is an all-encompassing DW that covers all subject areas of interest to the entire organization. A data mart is a smaller DW designed around one problem, organizational function, topic, or other suitable focus area.

5.

Explain the importance of metadata.

Metadata, “data about data,” are the means through which applications and users access the content of a data warehouse, through which its security is managed, and through which organizational management manages, in the true sense of the word, its information assets. Most database management systems would be unable to function without at least some metadata. Indeed, the use of metadata, which enable data access through names and logical relationships rather than physical locations, is fundamental to the very concept of a DBMS. Metadata are essential to any database, not just a data warehouse. (See answer to Review Question 2 of this section above.)

Section 5.3 Review Questions 1.

Describe the data warehousing process.

The data warehousing process consists of the following steps: 1.

Data are imported from various internal and external sources 2.

Data are cleansed and organized consistently with the organization’s needs 3. a.

Data are loaded into the enterprise data warehouse , or b.

Data are loaded into data marts.

4. a.

If desired, data marts are created as subsets of the EDW, or b.

The data marts are consolidated into the EDW 5.

Analyses are performed as needed

2.

Describe the major components of a data warehouse.

• • • •

Data sources. Data are sourced from operational systems and possibly from external data sources.

Data extraction. Data are extracted using custom-written or commercial software called ETL. Data loading. Data are loaded into a staging area, where they are transformed and cleansed. The data are then ready to load into the data warehouse.

Comprehensive database. This is the EDW that supports decision analysis by providing relevant summarized and detailed information.

102

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• •

3.

Metadata. Metadata are maintained for access by IT personnel and users.

Metadata include rules for organizing data summaries that are easy to index and search.

Middleware tools. Middleware tools enable access to the data warehouse from a variety of front-end applications.

Identify the role of middleware tools.

Middleware tools enable access to the data warehouse. Power users such as analysts may write their own SQL queries. Others may access data through a managed query environment. There are many front-end applications that business users can use to interact with data stored in the data repositories, including data mining, OLAP, reporting tools, and data visualization tools. All these have their own data access requirements.

Those may not match with how a given data warehouse must be accessed. Middleware translates between the two. (There will be more on middleware in Chapter 15, Section 15.8.)

Section 5.4 Review Questions 1.

What are the key similarities and differences between a two-tiered and a three-tiered architecture?

Both provide the same user visibility through a client system that accesses a DSS/BI application remotely. The difference is behind the scenes and is invisible to the user: in a two-tiered architecture, the application and data warehouse reside on the same machine; in a three-tiered architecture, they are on separate machines.

2.

How has the Web influenced data warehouse design?

Primarily by making Web-based data warehousing possible

3.

List the alternative data warehousing architectures discussed in this section.

• • • • •

4.

Enterprise Data Warehousing Architecture Data Mart Architecture Hub-and-Spoke Data Mart Architecture Enterprise Warehouse and Operational Data Store Distributed Data Warehouse Architecture

What issues should be considered when deciding which architecture developing a data warehouse? List the 10 most important factors.

1.

2.

3.

4.

5.

Information interdependence between organizational units Upper management’s information needs Urgency of need for a data warehouse Nature of end-user tasks Constraints on resources

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

6.

7.

8.

9.

10.

103

Strategic view of the data warehouse prior to implementation Compatibility with existing systems Perceived ability of the in-house IT staff Technical issues Social/political factors

(This list from the text, while clearly intended by the authors as the answer to this review question, does not explicitly say that these are the ten most important factors. Students may choose others.)

Section 5.5 Review Questions 1.

Describe data integration.

Data integration is an umbrella term that covers three processes that combine to move data from multiple sources into a data warehouse: accessing the data, combining different views of the data and capturing changes to the data.

2.

Describe the three steps of the ETL process.

Extraction: selecting data from one or more sources and reading the selected data.

Transformation: converting data from their original form to whatever form the DW needs. This step often also includes cleansing of the data to remove as many errors as possible.

Loading: putting the converted (transformed) data into the DW.

3.

Why is the ETL process so important for data warehousing efforts?

Since ETL is the process through which data are loaded into a data warehouse, a DW could not exist without it. The ETL process also contributes to the quality of the data in a DW.

Section 5.6 Review Questions 1.

List the benefits of data warehouses.

Direct benefits include: • Allowing end users to perform extensive analysis in numerous ways.

• A consolidated view of corporate data (i.e., a single version of the truth).

• Better and more timely information. A data warehouse permits information processing to be offloaded from costly operational systems onto low-cost servers; therefore, end-user information requests can be processed more quickly.

• Enhanced system performance. A data warehouse frees production processing because some operational system reporting requirements are moved to DSS. • Simplification of data access.

• Indirect benefits arise when end users take advantage of these direct benefits.

104

2.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

List several criteria for selecting a data warehouse vendor and describe why they are important.

Six criteria listed in the text are: financial strength, ERP linkages, qualified consultants, market share, industry experience, and established partnerships. These are important to indicate that a vendor is likely to be in business for the long term, to have the support capabilities its customers need, and to provide products that interoperate with other products the potential user has or may obtain.

One could add others, such as product functionality (Does it do what we need?), vendor strategic vision (Does their direction makes sense for our future plans and/or is consistent with industry trends?) and quality of customer references (What do their existing customers think of them?). These may be so obvious that the authors (or the author of the cited reference from which this list is taken) did not feel they needed to be mentioned, but they are still valid answers to this question.

There is more on vendor selection in Chapter 15, Section 15.11. Since students do not need to have learned the intervening material on data warehouse applications and intelligent systems in order to understand Chapter 15 (on implementation), you might want to suggest reading that section before answering this question.

3.

Does a bottom-up data warehouse development approach use an enterprise data model?

It need not. Kimball’s bottom-up data mart approach usually uses dimensional data modeling, starting with tables. However, if an enterprise data model exists, its relevant parts can be used as a starting point for the tables rather than embarking on a from-theground-up data modeling project.

4.

Describe the major similarities and differences between the Inmon and Kimball data warehouse development approaches.

Similarities: Both methods can produce an enterprise data warehouse and subset data marts.

Differences: Inmon’s approach starts with an enterprise data warehouse, creating data marts as subsets of that EDW if appropriate. The focus is on proven, traditional methods and technologies. Kimball’s starts with data marts, consolidating them into an EDW later if appropriate. It focuses in creating a useful end-user capability quickly.

5.

List the different types of data warehouse architectures.

This is essentially the same as Section 5.4, Review Question 3.

Section 5.7 Review Questions 1.

What is an RDW? A real-time data warehouse, in which decision making data are updated on an ongoing basis as business transactions occur; same as an active data warehouse (ADW).

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

2.

105

List the benefits of an RDW. The RDW extends the benefits of data warehousing, in general, down into tactical, and perhaps operational, decision making. It empowers people who interact directly with customers and suppliers by providing them with information to make decisions. It can then be extended to customers and suppliers themselves, thus affecting almost all aspects of customer service, SCM, logistics and beyond. It can also facilitate e-business activities, as when sales outlets such as overstock.com (cited in the text), woot.com and steepandcheap.com use historical data1 to price new close-outs.

3.

What are the major differences between a traditional data warehouse and an RDW? 1.

2.

3.

4.

5.

6.

7.

4.

List some of the drivers for RDW. • • • •

1

A traditional data warehouse (TDW) is used for strategic decisions (and sometimes tactical); an RDW for strategic and tactical (sometimes operational) ones.

The results of using a TDW can be hard to measure; results of using an RDW are measured by operational data.

Acceptable TDW refresh rates range from daily to monthly; RDW data must be up to the minute.

TDW summaries are often appropriate; RDWs must supply detailed data.

Small user community at upper organizational levels means a TDW supports few concurrent users; an RDW must support many, perhaps over a thousand.

TDWs typically use restrictive reporting to confirm or check patterns, often predefined summary tables; RDWs need flexible, ad hoc reporting.

TDW user community generally consists of power users, knowledge workers, managers, other internal users; RDWs are used by operational staff, call centers, perhaps external users.

A business often cannot afford to wait a whole day for its operational data to load into the data warehouse for analysis.

Traditional data warehouses have captured snapshots of an organization’s fixed states instead of incremental real-time data showing every state change and almost analogous patterns over time.

With a traditional hub-and-spoke architecture, retaining the metadata in sync is difficult. It is also costly to develop, maintain, and secure many systems as opposed to one huge data warehouse so that data are centralized for BI/BA tools.

In cases of huge nightly batch loads, the necessary ETL setup and processing power for large nightly data warehouse loading might be very high, and the processes might take too long. An EAI with real-time data collection can reduce or eliminate the nightly batch processes.

An attempt by amazon.com in 2000 to study price sensitivity of demand, by varying the price of the same movie to different customers and tracking the statistical effect on sales, was criticized as unethical and withdrawn. They subsequently offered refunds to customers who paid more than the lowest price offered to anyone.

106

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Section 5.8 Review Questions 1.

What steps can an organization take to ensure the security and confidentiality of customer data in its data warehouse?

Effective security in a data warehouse should focus on four main areas: Step 1. Establishing effective corporate and security policies and procedures. An effective security policy should start at the top and be communicated to everyone in the organization.

Step 2. Implementing logical security procedures and techniques to restrict access. This includes user authentication, access controls, and encryption.

Step 3. Limiting physical access to the data center environment.

Step 4. Establishing an effective internal control review process for security and privacy.

2.

What skills should a DWA possess? Why? • • • •

Familiarity with high-performance hardware, software and networking technologies, since the data warehouse is based on those Solid business insight, to understand the purpose of the DW and its business justification Familiarity with business decision, making processes to understand how the DW will be used Excellent communication skills, to communicate with the rest of the organization

Section 5.9 Review Questions Section 5.9 has no review questions.

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Compare data integration and ETL. How are they related?

Data integration consists of three processes that integrate data from multiple sources into a data warehouse: accessing the data, combining different views of the data and capturing changes to the data. It makes data available to ETL tools and, through the three processes of ETL, to the analysis tools of the data warehousing environment.

2.

What is a data warehouse and what are its benefits? Why is Web accessibility important with a data warehouse?

A data warehouse can be defined (Section 5.2) as “a pool of data produced to support decision making.” This focuses on the essentials, leaving out characteristics that may vary from one DW to another but are not essential to the basic concept.

The same paragraph gives another definition: “a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management’s decision-making process.” This definition adds more specifics, but in every case appropriately: it is hard, if

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

107

not impossible, to conceive of a data warehouse that would not be subject-oriented, integrated, etc.

The benefits of a data warehouse are that it provides decision making information, organized in a way that facilitates the types of access required for that purpose and supported by a wide range of software designed to work with it.

Web accessibility of a data warehouse is important because many analysis applications are Web-based, because users often access data over the Web (or over an intranet using the same tools) and because data from the Web may feed the DW. (The first part of this question is essentially the same as Review Question 1 of Section 5.2. It would be redundant to assign that question if this one is to be answered as well.)

3.

A data mart can replace a data warehouse or complement it. Compare and discuss these options.

For a data mart to replace a data warehouse, it must make the DW unnecessary. This would mean that all the analyses for which the DW would be used can instead be satisfied by a DM (or perhaps a combination of several DMs). If this is so, it can be much less expensive, in terms of development and computer resources, to use multiple DMs (let alone one DM!) instead of an overall DW. In other situations, a data mart can be used for some analyses which would in its absence use the DW, but not all of them. For those, the smaller DM is more efficient— quite possibly, enough more efficient as to justify the cost of having a DM in addition to a DW. Here the DM complements the DW.

4.

Discuss the major drivers and benefits of data warehousing to end users.

Major drivers include: • Increased competition and pace of business, leading to increased need for good decisions quickly • Successful pioneering experiences with data warehouses, leading to their wider user acceptance • Decreasing hardware costs, making terabyte databases with masses of historical data economically feasible for more firms • Increased availability of software to manage a large data warehouse • Increased availability of analysis tools making DWs potentially more useful • Increased computer literacy of decision makers, making them more likely to use these tools (See Review Question 1, Section 5.6 for list of benefits.)

5.

List the differences and/or similarities between the roles of a database administrator and a data warehouse administrator.

Since a data warehouse is a specific type of database designed for a specific application area, a data warehouse administrator has all the roles of a database administrator—plus others. One new role is advising on decision support uses of the DW, for which a DWA needs to understand decision making processes. Beyond that, the issue is more a need for

108

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

additional skills in the same roles as a DBA—e.g., understanding high-performance hardware to deal with the large size of a DW—than it is one of additional roles. (See Review Question 2, Section 5.8, for list of skills.)

6.

Describe how data integration can lead to higher levels of data quality.

A question involving the word “higher” (or any other comparative, for that matter) requires asking “higher than what?” In this case, we can take it to mean “higher than we would have for the same data, but without a formal data integration process.”

Without a data integration process to combine data in a planned and structured manner, data might be combined incorrectly. That could lead to misunderstood data (a measurement in meters taken as being in feet) and to inconsistent data (data from one source applying to calendar months, data from another to four-week or five-week fiscal months). These are aspects of low-quality data which can be avoided, or at least reduced, by data integration.

7.

Compare the Kimball and Inmon approaches toward data warehouse development.

Identify when each one is most effective.

Inmon’s approach starts with an enterprise data warehouse, creating data marts as subsets if appropriate. It is most effective when there is a recognized need for an EDW, an executive “champion” of the project, and a willingness to invest in a data warehousing infrastructure before it will show results.

Kimball’s approach starts with data marts, consolidating them into an EDW later if appropriate. It is most effective when it is desired to provide a “proof of concept” implementation before embarking on a full-scale EDW project or when a well-defined area with the greatest benefits can be identified.

(This question is related to Section 5.6, Review Question 4. It would normally be redundant to assign both.)

8.

Discuss security concerns involved in building a data warehouse.

Security and privacy2 concerns are important in building a data warehouse: 1.

Laws and regulations, in the U.S. and elsewhere, require certain safeguards on databases that contain the type of information typically found in a DW. 2.

The large amount of valuable corporate data in a data warehouse can make it an attractive target.

3.

The need to allow a wide variety of unplanned queries in a DW makes it impractical to restrict end user access to specific carefully constrained screens, one way to limit potential violations.

(See Review Question 2 of Section 5.8 for steps to address these concerns.)

2

Privacy added to the answer. The two concerns are different, but are often grouped—as they are in the relevant portion of this text. Privacy is one reason to want security. Without security, one cannot ensure privacy.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

9.

109

Investigate current data warehouse development implementation through offshoring.

Write a report about it. In class, debate the issue in terms of the benefits and costs, as well as social factors.

Open-ended answer to the report; it is impossible to predict what the debate will bring.

A student’s position on this issue is related to his/her feelings on the relationship of national economies to the global economy. It can be argued that offshoring improves the global economy while potentially harming one or more of the national economies involved.—such as the student’s own. U.S. students may see primarily the damage they perceive it does to their national economy (and to their own career prospects), but students in India may take a different view. The economic, political and philosophical issues can be pursued well beyond what is practical in a DSS course.

If you feel students are too nationalistic on this issue, you can ask them if they feel the same way about a Massachusetts or California bank processing checks in Alabama to reduce labor costs. (This example uses U.S. territories, but similar issues exist in any country large enough to have regional economic differences.)

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

Describe how Overstock.com ran its campaigns before the Teradata system was developed. How has the real-time data warehouse helped Overstock.com improve the performance of the business?

The text does not provide information for the first part of this question. However, we can infer that decisions were made intuitively, without the benefit of a historical database or analyses based on its content. Therefore, prices may have been too high (so Overstock.com didn’t achieve its objective, liquidating inventory in a timely fashion) or too low (in which case it did not obtain maximum revenue for liquidating it). The benefits are a more informed first price at which to offer close-out merchandise.

The real-time data warehouse, introduced after the earlier EDW, allows the firm to adjust pricing and other factors on-the-fly based on real-time information as to how they are selling. The benefit here is greater ability to adjust the price of merchandise based on actual selling patterns. While the better first price that the original Teradata DW made possible is an improvement over conditions that existed before then, it is still not perfect; the RDS permits fine-tuning it further.

2.

Go to the Web sites of the vendors mentioned in the case and examine the current data warehousing features and capabilities of each. Describe in detail how Overstock.com could potentially use each one.

Vendors to look at include Teradata, GoldenGate Software and Sunopsis. The descriptions of how Overstock.com could use each one, which are open-ended, should be based on what the case says about how the firm uses them already.

(Teradata offers broad-based data warehousing capabilities that students should already be familiar with from previous examples. GoldenGate Software

110

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

(www.goldengate.com) supplies software that provides real-time feeds of transaction data to a DW. Sunopsis (www.sunopsis.com), via its Data Conductor product, provides a business-rules-driven approach to the ETL process that has advantages over the traditional approaches, which mix business rules with the implementation of the ETL process.)

CHAPTER

6

Business Analytics and Data Visualization

Learning Objectives for Chapter 6 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Describe business analytics (BA) and its importance to organizations List and briefly describe the major BA methods and tools Describe how online analytical processing (OLAP), data visualization, and multidimensionality can improve decision making Describe advanced analysis methods Describe geographical information systems (GIS) and their support to decision making Describe real-time BA Describe how business intelligence (BI) supports competitive intelligence Describe automated decision support (ADS) systems and their benefits Explain how the Web relates to BA Describe Web intelligence and Web analytics and their importance to organizations Describe implementation issues related to BA and success factors for BA

CHAPTER OVERVIEW This chapter goes along with the previous one to present, between them, the entire data warehousing picture for decision support. Chapter 5 covered how the data are stored for access; this one covers what happens when a user accesses them. It discusses a wide range of approaches. Students should finish the chapter with a good appreciation of all. That will require a good deal of work on their part, there’s a lot of content here, but it is important work that will be repaid by useful knowledge.

CHAPTER OUTLINE 6.1

OPENING VIGNETTE: LEXMARK INTERNATIONAL IMPROVES OPERATIONS WITH BI A. PROBLEM B. SOLUTION C. RESULTS  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

6.2

THE BUSINESS ANALYTICS (BA) FIELD: AN OVERVIEW A. THE ESSENTIALS OF BA ⧫ Application 6.1: Ben & Jerry’s Excels with BA B. THE TOOLS AND TECHNIQUES OF BA 1.

MicroStrategy’s Classification of BA Tools: The Five Styles of BI 2.

SAP’s Classification of Strategic Enterprise Management: Operational, Managerial, and Strategic C. EXECUTIVE INFORMATION AND SUPPORT SYSTEMS  Section 6.2 Review Questions

6.3

ONLINE ANALYTICAL PROCESSING (OLAP) A. OLAP VERSUS OLTP B. TYPES OF OLAP C. EXAMPLES OF SUCCESSFUL OLAP APPLICATIONS D. OLAP TOOLS AND VENDORS 1.

Characteristics of OLAP Tools 2.

Representative OLAP Tools  Section 6.3 Review Questions ⧫ Application 6.2: TCF Financial Corp.: Conducting OLAP, Reporting, and Data Mining

6.4

REPORTS AND QUERIES A. REPORTS 1.

Routine Reports 2.

Ad Hoc (or On-Demand) reports 3.

Multilingual Support 4.

Examples of Vendors’ Products for Reporting 5.

Representative Types of Reports 6.

Scorecards and Dashboards 7.

Report Delivery and Alerting B. QUERIES 1.

Ad Hoc Queries 2.

SQL for Querying C. ANALYSIS OF REPORTS’ RESULTS  Section 6.4 Review Questions

6.5

MULTIDIMENSIONALITY A. MULTIDIMENSIONAL PRESENTATION B. MULTIDIMENSIONAL DATA CUBES AND THEIR ANALYSIS 1.

Multidimensional Tools and Vendors C. LIMITATIONS OF DIMENSIONALITY  Section 6.5 Review Questions

6.6

ADVANCED BUSINESS ANALYTICS A. DATA MINING AND PREDICTIVE ANALYSIS 1.

Data Mining 2.

Predictive Analysis ⧫ Application Case 6.3: Predictive Analysis Can Help You Avoid Traffic Jams B. REPRESENTATIVE VENDORS’ TOOLS FOR ADVANCED ANALYTICS  Section 6.6 Review Questions

6.7

DATA VISUALIZATION A. VISUALIZATION SPREADSHEETS B. NEW DIRECTIONS IN DATA VISUALIZATION 1.

Dashboards and Scoreboards 2.

Visual Analysis 3.

Financial Data Visualization ⧫ Application Case 6.4: Financial Data Visualization at Merrill Lynch C. VENDORS AND PRODUCTS  Section 6.7 Review Questions

6.8

GEOGRAPHIC INFORMATION SYSTEMS (GIS) A. GIS APPLICATIONS B. GIS AND DECISION MAKING C. GIS COMBINED WITH GPS ⧫ Application Case 6.5: GIS and GPS Track Where You Are and Help You with What You Do D. GIS AND THE INTERNET/INTRANETS  Section 6.8 Review Questions

6.9

REAL-TIME BUSINESS INTELLIGENCE, AUTOMATED DECISION SUPPORT (ADS), AND COMPETITIVE INTELLIGENCE A. REAL-TIME BI 1.

Concerns About Real-Time Systems B. AUTOMATED DECISION SUPPORT (ADS) 1.

Business Rules 2.

Characteristics and Benefits of ADS 3.

ADS Applications 4.

Implementing ADS C. COMPETITIVE INTELLIGENCE

 6.10

Section 6.9 Review Questions

BUSINESS ANALYTICS AND THE WEB: WEB INTELLIGENCE AND WEB ANALYTICS A. USING THE WEB IN BA B. WEB ANALYTICS 1.

Clickstream Analyses C. VENDOR SUPPORT FOR WEB ANALYTICS ⧫ Application Case 6.6: Web Analytics Improves Performance for Online Merchants  Section 6.10 Review Questions

6.11

USAGE, BENEFITS, AND SUCCESS OF BUSINESS ANALYTICS A. USAGE OF BA B. SUCCESS AND USABILITY OF BA ⧫ Technology Insights 6.7: Are BI Systems Making Firms Smarter? ⧫ Application Case 6.8: Retailers Make Steady BI Progress 1.

Why BI/BA Projects Fail C. SYSTEM DEVELOPMENT AND THE NEED FOR INTEGRATION  Section 6.11 Review Questions Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises ⧫ End of Chapter Application Case: State Governments Share Geospatial Information  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

As noted above, this chapter complements Chapter 5 by discussing what the users see of a data warehouse. (That chapter discussed what the data, and those that handle the data directly, see chapter 5.) Some of your students, especially those who are into databases, will probably prefer that one. Others, especially the more business-oriented ones, will relate better to this one. Both are important parts of the whole picture.

It can help to revisit the Figure 3.5 briefly before starting in on this chapter. That figure positions the analytical side of DSS/BI in the context of the entire system. While the BA tools discussed in this chapter may not fall exactly into the mold of that picture—for example, they seldom have a separate, visible component that can be identified as a “model management system”—that perspective helps students see where they fit in the big picture.

Once embarked on the voyage through this chapter, students will see the wide variety of tools that the system developer has at his or her disposal today.

Section 6.1, the opening vignette, isn’t just about the subject of this chapter—but that’s a good thing. It deals with a situation where the company didn’t have the data they needed. The first step in solving the problem was to get a data warehouse up and running (Chapter 5). Then, and only then, could the tools and techniques of this chapter be brought to bear with the benefits seen in the vignette. This example also helps show students the connection between the previous chapter and this one.

Section 6.2 provides two different classifications of BI tools: that of MicroStrategy and that of SAS. The value is not in memorizing the categories, but in applying them to specific tools to see where each tool fits and learn from the common characteristics of tools that fit into the same category. Therefore, you might want to go through the categories relatively quickly at first, but relate the topics you’ll cover later in the chapter to them as you encounter each tool or type of tool.

OLAP, the topic of Section 6.3, is vitally important to most data warehousing applications. Students should already understand how decision support differs from transaction processing, thanks to the many references to OLTP data earlier in the book (especially in Chapter 5), but it can’t hurt to make sure.

Also in this section, Codd’s rules for OLAP have been controversial. They were written under contract to an OLAP vendor, they reflect the characteristics of that vendor’s product in the mid-1990s, and at the time no other vendor’s products satisfied all 12. Some argue that the rules were developed to promote that vendor’s products. In their defense, it can be argued that, despite their origin, they are vendor-independent and others have since developed qualifying products. A middle ground is to concede that some requirements (e.g., items 5, 7 and 8) are design choices, but the majority defines the essence of OLAP. It is not our purpose to take sides in the debate, but to make you aware of it so you can research it further if you wish and present a balanced view of these rules to your students.

Most of your students will be generally familiar with the concepts of queries and reports (Section 6.4) from other courses. One item worth pointing out is in the use of SQL for querying near the end of the chapter. It’s not hard to learn simple SQL, but the complexity of the language grows rapidly as one wants to do fancier things with it. The easy way to write some simple SQL queries can result in a query that will bog down the fastest servers on the planet for several hours. Students whose experience with queries is limited to tables with a few dozen, or even a few thousand, rows may not realize this. Teradata University Network has a few “industrial strength” databases students can use to get an appreciation for this factor, such as the UA_DILLARDS sales database with over 128 million rows of retail sales data. (It also has builtin safeguards to prevent a “rogue query” from hogging the system. In particular, your students must access this database via Teradata SQL Web Assistant.)

Section 6.7 on data visualization is good to link to Chapter 4 on models. It was mentioned there briefly in the context of simulation, where it’s a “natural.” This section mentions models, but only spreadsheet models. You may want to create a stronger connection between the two.

GIS (Section 6.8) are important for many types of decisions. Research by Unisys in the 1990s found that over 85 percent of all entities in databases have some location-based data, though the database often didn’t contain it. You can ask students to come up with examples: the location at which an insured car is garaged, the classroom in which a projector having a specific property tag number is installed, the home state of a college or university applicant … in fact, it

can be hard to find any meaningful data set where the underlying entity has no location-based component.

(If your students are technically oriented and you feel up to it, a brief digression from the focus of the book into the way geographic data are actually stored in databases via links and nodes can be tolerated here. Oracle’s model is well described, with much technical detail, on its Web site.)

Section 6.9 is on three topics that could have been in separate sections, except that the resulting sections would have been very short. There is little connection among the three. Realtime BI is an important direction for BI evolution today. You can spend a bit less time on the other two, though: the essence of ADS will come up again in Chapter 12 along with a much deeper discussion of its underlying concepts. (Technology Insights 12.11 in that chapter will connect its content to the ADS concept.) As for competitive intelligence: it’s important, companies do it, students should know about it, but it’s more a topic for marketing courses than for this one and can be given a slightly lower priority for that reason.

Web analytics—the main topic of Section 6.10—on the other hand, while also marketingrelated, make more sense to discuss here because MIS students are also involved in Web development issues. Clickstream analysis and the other applications of Web analytics are important in showing students how what they’re learning in this course is related to what they’ve learned in their other MIS courses.

Finally, Section 6.11 pulls it all together with some implementation suggestions. The reasons for BI/BA project failure can be instructive if compared with reasons for project failure that students may have learned in their introductory MIS course, a systems analysis course or perhaps a project management course. Are these the same as they learned there, or is there something different about BI/BA? As with the previous chapter, visiting vendors’ Web sites can be a helpful part of the learning experience. The Internet Exercises at the end of the chapter provide good suggestions.

The Team Assignments can also be used for this even if you don’t want to assign them to teams: scaled down, they make good individual assignments too. (Should you assign Team Assignment 7, you may not want to require students to use specific search engines. Allowing them to use any search engine of their choice works equally well. In late 2006 most will use Google—but this may change in the future, some have other preferences even now, and the question doesn’t depend on which one(s) they use.)

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 6.1 Review Questions 1.

Identify the challenges Lexmark faced regarding information flow.

The challenges Lexmark faced regarding information flow in the case involved information about sales volumes and inventory levels flowing between Lexmark and its sales force/business partners. Challenges with information flow to Lexmark are not further specified in the case, beyond stating that problems existed. The main challenges regarding information flow from Lexmark were:

• • • 2.

Overcoming speed and quality problems arising from the need to copy answers from spreadsheets and paste them into reports before sending the reports to partners.

Eliminating the need for salespeople to use SQL to query a complex mainframe system Providing accurate and up-to-date data for management reports

How were information flows provided before and after implementation of the system?

Before implementation of the system, information flows were provided by navigating the mainframe to find the desired data, which was sometimes inaccurate and/or out of date; writing SQL queries to retrieve the data; getting the data (the case does not state how) into spreadsheets, and copying results from those spreadsheets into reports. (This answer is a composite of the steps mentioned in the case, arranged into a logical order. It is possible that information flow in a given situation did not involve all these steps.)

After implementation of the system, information goes from the data warehouse directly to decision makers’ desks and mobile devices, where it is analyzed.

3.

Identify decisions supported by the new system.

Decisions mentioned in the case include when to place a stock replenishment order, strategic decisions (not further detailed), and decisions on pricing and promotions.

The case also mentions several queries, increased partner loyalty, elimination of inventory problems, analyzing sales and inventory data, understanding business trends better and other uses of the system, but those are not decisions.

4.

How can the new system improve customer service?

Lexmark’s customer is the retail store, not the ultimate purchaser. By providing decision makers at the store and at Lexmark with up-to-date information on sales and inventory, the system can make sure stores have the right products in stock at all times. By providing production planners with aggregated information, they can make sure Lexmark has the stock to ship to stores than need it. The system can also help planners develop price and promotion strategies that meet the stores’ needs as well as Lexmark’s.

5.

Go to microstrategy.com. Examine the capabilities of Retail BI System. Prepare a list of the capabilities.

Answers will vary.

6.

Go to sas.com and find the Retail Intelligence product; take the interactive tour. Compare it to MicroStrategy’s product. Also, compare it to Oracle Retail (see www.oracle.com/applications/retail.html).

Oracle offers a much broader solution for retail management, beginning with database management and continuing through financials, human resource management and all other aspects of corporate and in-store management. Oracle Active Retail Intelligence, the part which is directly comparable to SAS Retail Intelligence, is a small part of their

overall offering to retailers. It offers the advantages of being part of a total integrated solution, but does not have the same capabilities as the SAS offering. That is focused on BI and has to be good at it if it is to appeal to any customers at all. It is the user’s job to decide if the added capabilities of SAS justify dealing with the integration issues.

Section 6.2 Review Questions 1.

Define BA and explain its strategic value.

BA is “a broad category of applications and techniques for gathering, storing, analyzing, and providing access to data to help enterprise users make better business and strategic decisions.” Its strategic value is in providing the tools that are needed for decision makers to take advantage of the data in a data warehouse (or occasionally elsewhere).

2.

Relate BA to data warehousing.

Business analytics cannot exist without a source of data to analyze. This data source is usually a data warehouse.

3.

List and describe the three categories of tools shown in Figure 6.1.

The three categories of tools shown in the figure are: • Information and knowledge discovery: tools that help people take advantage of information in a data warehouse.

• Decision support and intelligent systems: the broader application that takes advantage of the analyses performed by tools in the first category.

• Visualization: tools that present the results of the other categories in a way that helps managers understand them and thereby make informed decisions.

4.

List MicroStrategy’s five styles of BI. MicroStrategy’s five styles of BI are enterprise reporting, cube analysis, ad hoc querying and analysis, statistical analysis and data mining, and report delivery and alerting.

5.

Describe SAP’s BI tool categorization.

SAP categorizes BI tools according to the level of decision they support: operational, managerial, and strategic.

6.

Define EIS and ESS. An EIS (Executive Information System) is, in a nutshell, a DSS that focuses on top executives. It serves their information needs by providing a user-friendly interface through which executives can access timely and relevant information, drill down into it, and access management reports.

An ESS (Executive Support System) adds capabilities such as intelligence and analysis support, communications, and office automation to an EIS to support the complete information processing needs of top management.

7.

List the major capabilities of EIS/ESS (refer to Table 6.1).

The major capabilities listed there (in the left column) are drill-down, critical success factors (CSF), key performance indicators (KPI), status reports, trend analysis, ad hoc analysis, exception reporting, and slicing and dicing. They are further described in the right column of the table.

Section 6.3 Review Questions 1.

Define OLAP and compare it to OLTP. OLAP, or Online Analytical Processing, is an umbrella term that refers to a variety of activities users perform with data in online systems. These activities all relate to decision support but have little else in common. The only thing OLAP shares with OLTP (Online Transaction Processing), besides the obvious points that both use computers online, is a similarity in name: the term “OLAP” was coined by E.F. Codd in 1993 by analogy to OLTP. OLAP is concerned with complex analyses that read, but do not modify, large parts of a database, and are performed relatively infrequently; OLTP is concerned with simple, repetitive activities that update small parts of a database and are often performed hundreds of times per minute or even more frequently. As a result, the best database organization for one may not be good for the other, and many organizations use different databases to support the two.

2.

Describe the major characteristics and benefits of OLAP. The major characteristics of OLAP are: • Examining many data items • Dealing with complex relationships • Looking for patterns, trends, exceptions • Allowing users flexibility in defining their questions Its major benefit is helping users who know what they want, at the conceptual or business level, use a database (usually, and more narrowly, a data warehouse) to find it.

3.

Describe the types of OLAP. The major types of OLAP are as follows. (Note that they are not mutually exclusive.)

• Multidimensional OLAP (MOLAP): OLAP in which data are stored in a multidimensional database (rather than relational or some other structure).

• Relational OLAP (ROLAP): OLAP in which data are stored in a relational database (rather than multidimensional or some other structure).

• Database OLAP (DOLAP): A relational DBMS designed to support OLAP. • Web OLAP (WOLAP): OLAP in which data are accessed via a Web browser.

• Desktop OLAP: OLAP in which data are stored, and analysis is performed, on a user’s desktop system.

4.

List and briefly describe the major OLAP tools.

The major OLAP tools fall into four categories: • Categorical analysis: Using static historical data.

• • •

Exegetical analysis: Using static historical data, but with drill-down capability to delve further into the origins of a data point.

Contemplative analysis: Adding the ability to change a single value to determine the impact of the change.

Formulaic analysis: Extending that ability to permit changes to multiple variables.

Section 6.4 Review Questions 1.

Define routine report and ad hoc report.

A routine report is one that is issued regularly without a specific request each time.

An ad hoc, or on-demand, report is created for a specific, often a one-time, need.

2.

List the major categories of reports (according to MicroStrategy).

According to their Web site: operational reports, business reports, invoices and statements (which they consider one category due to their common characteristics: they are repetitive and have precise formatting requirements).

According to the text: production and operational reports, invoices and statements, profit-and-loss reports and performance reports. The last two would fall under the “business reports” category on their Web site.

3.

What kind of reports are included in the major vendors’ kits?

One answer would be the area categories of Table 6.4: finance/accounting, human resources, sales forecasting and marketing, customer relationship management, product sales, operation and supply-chain management, and Web site analysis.

Other correct answers, based on nearby parts of the text or other sources, are possible.

4.

Explain why scorecards and dashboards are viewed as a special category of reporting.

They are viewed as a special way of reporting because all are ways of delivering information to users. Scorecards and dashboards are often a better way to present results than plain reports, since they bring out important issues more clearly.

5.

Define report delivery and alerting.

Report delivery encompasses a wide variety of ways to get content to those who could use it: e-mail, printers, portals and more. Reports can be delivered to those who subscribe to them, to those whom management decides should have them, or to a combination.

Delivery can take place on request, on a schedule or on an event. Reports can be standardized or personalized.

Alerting is the process of notifying a user that a report is available. This typically implies the use of e-mail for the alert. The report would typically be delivered by another medium, or is so large that e-mailing the alert instead generates significant savings in network and storage resource utilization.

6.

Define ad hoc query. How can an ad hoc query be generated?

An ad hoc query is one that cannot be predicted and therefore cannot be planned for.

Ad hoc queries can be written in a language such as SQL or by a query tool.

Query tools include menus, query-by-example and natural language interfaces. Some OLAP systems, such as BusinessObjects cited as an example in the text, provide users with guidance in formulating queries.

Section 6.5 Review Questions 1.

Define multidimensionality and describe its benefits.

Multidimensionality is a way to organize data efficiently for analysis and presentation.

This approach treats each characteristic of the underlying entity as a dimension of a cube.

(Technically, a cube must have exactly three dimensions. The corresponding structure with two dimensions is a square; with four or more, a hypercube. IS developers tend to ignore this nuance of mathematical terminology. A cube in an OLAP context can generally be two-dimensional, three-dimensional or higher, though students should expect some authors to use the correct term also.)

2.

Define multidimensional database and describe the limitations of these databases.

A multidimensional database is a database in which the data are organized by their dimensions. According to the Gartner report cited in the last part of this section, its limitations are: • The multidimensional database can take up significantly more computer storage room than a summarized relational database.

• Multidimensional products cost significantly more than standard relational products.

• Database loading consumes significant system resources and time, depending on data volume and the number of dimensions.

• Interfaces and maintenance are more complex in multidimensional databases than in relational databases.

3.

Define cube and data cube.

A data cube is a structure used to represent data along several measures of interest.

According to one definition in the text (p. 270), cube is a common short form of data cube. According to another (p. 271), a cube is a subset of interrelated data organized to allow users to combine attributes and metrics in any desired way.

4.

What is meant by slice-and-dice?

The term is not defined precisely in the text, though students should obtain an intuitive understanding of its meaning from the context in which it is used. They should also be able to find definitions such as these on the Web: From the glossary provided by Alta Plana, a business analytics consulting firm: “The user-initiated process of navigating by calling for page displays interactively, through the specification of slices via rotations and drill down/up.”

From PC Magazine’s encyclopedia of IT terminology: “Refers to rearranging data so that it can be viewed from different perspectives. The term is typically used with OLAP databases that present information to the user in the form of multidimensional cubes similar to a 3-D spreadsheet.”

5.

List the limitations of dimensionality.

See the second part of the answer to Review Question 2 of this section, above.

Section 6.6 Review Questions 1.

Why are advanced analytics tools necessary for businesses?

“Advanced analytics” refers to tools that are able to analyze data in more sophisticated ways than simple reports and queries. These are necessary because making good decisions can require more complex analysis than reports and queries can offer.

2.

List some tools of advanced BA. Tools of advanced BA include hypothesis testing, multiple regression and correlation, churn predictions and customer scoring models.

3.

Define data mining.

Data mining extracts hidden information from and finds patterns in databases, when a decision maker may not know what he or she is looking for.

4.

Define predictive analysis and describe its capabilities.

Predictive analysis helps determine the probable future outcome for an event or the likelihood of a situation occurring. Aside from prediction: “if interest rates rise by one percent, what will happen to the cost of our raw materials?” these tools can also identify relationships and patterns.

5.

List some tools of predictive analysis.

Tools of predictive analysis include clustering, decision trees, market-basket analysis, regression modeling, neural nets, genetic algorithms, text mining, hypothesis testing, decision analytics, and more.

Section 6.7 Review Questions 1.

Define data visualization and list its major advantages.

Data visualization refers to technologies that support visualization and sometimes interpretation of data and information. It includes digital images, GIS, graphical user interfaces, graphs, virtual reality, dimensional presentations, videos, and animation.

Visual tools can help identify relationships such as trends.

Advantages of data visualization over other methods of presenting data include the ability to identify trends quickly, to detect problems that are hard to see otherwise (perhaps having gone undetected for years for this reason), and supporting the use of Web browsers to look at vital data.

2.

Describe spreadsheet visualization.

Spreadsheet visualization is the application of visualization tools to data created in spreadsheets. (The graphing capabilities of most spreadsheet software, including Excel, are too limited to qualify as true visualization.)

3.

Describe financial data visualization.

The concept of financial data visualization covers the visualization of many types of financially-related data. In one example, a CFO may need to view risk, return and liquidity in combination. Investment managers can use it to view characteristics of potential investments or of a client’s portfolio.

Section 6.8 Review Questions 1.

Define GIS and describe its benefits.

A geographical information system (GIS) is a computer-based system for capturing, storing, modeling, retrieving, checking, integrating, manipulating, analyzing, and displaying geographically referenced data by using digitized maps. The most distinguishing characteristic of GIS is that every record or digital object has an identified geographic location.

The benefit of GIS is the ability to integrate geographic information with other types of information. This, in turn, enables decisions that are based in part on geographic (location-based) data and in part on other data.

2.

How can banks use GIS? The section has a list with several examples: • Determining branch and ATM locations

•

Analyzing customer demographics (e.g., residence, age, income level) for each of a bank’s products • Analyzing volume and traffic patterns of business activities • Analyzing the geographic area served by each branch • Determining the market potential for banking activities • Evaluating strengths and weaknesses against those of the competition • Evaluating branch performance.

It also mentions others in the text, such as Visa Plus’s letting Internet users find a nearby ATM. (While the Visa Plus consortium is not technically a bank, the application is still an example of how a bank could use GIS, since a bank could do the same for its own ATMs.) Students may come up with still other answers. Those would take this question out of the “review question” category, but can still be correct.

3.

Describe GIS/GPS applications.

This question can be taken two ways: as asking for a generic description of GIS/GPS applications as a group, or as asking for descriptions of some sample GIS/GPS applications. You may want to specify which of those interpretations you want if you will not accept either as equally valid.

A generic description of GIS/GPS applications might be “GIS/GPS applications are GIS applications in which some or all of the location information used by a GIS is provided via GPS receivers.”

Sample GPS applications are described in Application Case 6.5. Students who follow this option may find additional examples in other sources.

4.

Why is the combination of GIS and GPS so useful?

GIS use location information. GPS provide this information for a suitably equipped movable object. When that object—a truck, for example—is the subject of a GIS, GPS input is useful.

5.

How is GIS related to the Web/Internet?

Just as the Internet provides a common access path and the Web provides a common user interface to all types of DSS/BI systems and expands their availability, they can do the same for GIS. The Web’s graphical nature makes it especially well suited to displaying maps.

Section 6.9 Review Questions 1.

Define real-time BI. Real-time BI is BI that uses current, rather than historical, data.

2.

List the benefits of real-time BI. It enables people to use BI tools for decisions that require up-to-date information. This extends the usefulness of BI technology to many more decisions than it was previously

able to support. The benefit is making this new class of decisions better than they could be made without BI. 3.

What technologies are needed to provide real-time BI? The key technology is real-time data warehousing, often with the assistance of automated data acquisition. They permit the use of data warehousing and BI tools for real-time operational decisions.

4.

List some concerns about real-time systems.

The two concerns both relate to data updating. One is that one person’s results may not match another’s generated hours, or even minutes, later. A related one is that not all data need to be updated continuously, so money may be wasted on unnecessary updates.

5.

Define ADS system and describe the benefits of ADS. Automated Decision Support (ADS) Systems are “rule-based systems that provide solutions to repetitive managerial problems.” Their benefits include the ability to provide these capabilities: • Rapidly builds rules-based applications to automate or guide decision makers, and deploys the applications into almost any operating environment.

• Injects predictive analytics into rules-based applications, increasing their potency and value.

• Provides decisioning services to legacy systems, expanding their capabilities while minimizing technical risk.

• Combines business rules, predictive models, and optimization strategies flexibly into state-of-the-art decision-management applications.

• Accelerates the uptake of learning from decision criteria into strategy design, execution, and refinement.

6.

Describe yield management.

Yield management is the process of adjusting the price of a perishable product, which has limited availability, based on demand. Its most commonly cited application is in pricing airline seats.

7.

List the major categories of ADS. Categories of ADS listed in the text include: • Product or service configuration • Yield (price) optimization • Routing or segmentation decisions • Corporate and regulatory compliance • Fraud detection • Dynamic forecasting • Operational control

8.

Describe competitive intelligence.

Competitive intelligence is information acquired about competitors in the course of monitoring their activities. This information can drive business performance by increasing market knowledge, improving knowledge management, and raising the quality of strategic planning.

9.

How can the Internet facilitate competitive intelligence?

The Internet, especially the Web, is an outstanding source of competitive information.

Aside from information deliberately made public by competitors, it can be used to find information inadvertently made public, information made public by customers and suppliers, information required to be made public by regulatory and other government agencies, and information posted by those who are upset with the company or have some other axe to grind.

Section 6.10 Review Questions 1.

Describe the use of the Web to enhance BI activities.

BI activities—from data acquisition, through warehousing, BA, and mining—are mostly performed using Web tools. These tools enable users with browsers to log on to a system, make inquiries, get reports, and so on, in real time.

2.

Define Web analytics.

Web analytics is the application of BA activities to Web-based processes.

3.

Describe applications of Web analytics.

While most BA applications are related to the Web somehow, they are not Web analytics.

That term refers specifically to applying BA to the Web-based process itself. The category of Web analytics includes applications such as clickstream analysis, which is described further in the text. It also includes applications such as determining which landing pages or link origins are most correlated with purchases or other desirable activity, which ads are most effective, which access patterns are most correlated with fraud or attempted fraud (including “click fraud,” where a person, or computer imitating a person, clicks on an ad in an attempt to generate an improper charge per click), studying customer reaction to changes in a site, and more.

Students who wish to pursue this area in more depth may want to visit The Web Analytics Association (http://www.webanalyticsassociation.org) and read the Wikipedia article on the subject, as well as using their usual search tools to find other sources.

4.

Define clickstream analysis.

Clickstream analysis refers to the analysis of data that occur inside the Web environment.

These data, known as clickstream data, provide a trail of the user’s activities and show the user’s browsing patterns: which sites are visited, which pages are accessed, how long is spent at the site, and so on.

5.

How are clickstream data used?

By analyzing and interpreting clickstream data a firm can, for example, find the effectiveness of promotions and determine which products and ads attract the most attention.

Section 6.11 Review Questions 1.

List the major benefits of BA. This is an open-ended answer: many benefits of BA have been discussed throughout this chapter, and students will have found more on Web sites to which the text has directed them. All involve BI’s ability to analyze data in a data warehouse or elsewhere to support decision making.

2.

List three factors regarding the implementation of BI. Three key factors that affect the implementation of BI are culture, people and processes.

(These are important in the implementation of nearly any other kind of information system, too.)

3.

Identify some BI tools.

This question can be taken two ways: as asking for categories of BI tools, or as asking for names (and perhaps descriptions) of specific ones. You may want to specify which of those interpretations you want if you will not accept either one equally.

If the first interpretation is used, general categories of BI tools were identified in the answers to Review Questions 3 and 5 of Section 6.2. It would be redundant to assign those questions and this one.

The chapter contains dozens of examples of specific BI tools, any set of which can be a correct answer to this question. Students who follow this option may find additional examples in other sources.

(These answer notes equate BI and BA. This is based on the statement on p. 256, near the start of Section 6.2, that “BA is also known as … and just BI.”)

4.

List five reasons why BI projects fail.

The text offers this list of ten: 1.

Failure to recognize BI projects as cross-organizational business initiatives and to understand that, as such, they differ from typical standalone solutions 2.

Unengaged or weak business sponsors 3.

Unavailable or unwilling business representatives from the functional areas 4.

Lack of skilled (or available) staff, or suboptimal staff utilization 5.

No software release concept (i.e., no iterative development method) 6.

No work breakdown structure (i.e., no methodology) 7.

No business analysis or standardization activities 8.

No appreciation of the negative impact of “dirty data” on business profitability 9.

No understanding of the necessity for and the use of metadata 10.

Too much reliance on disparate methods and tools

A correct answer may include any five of these, as well as other possible answers students may find in other sources.

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Relate data warehousing to OLAP and data visualization.

OLAP and data visualization are among the ways that data in a data warehouse are brought out for decision making. Although there are ways to make these data useful that do not involve either OLAP or visualization—for example, a data mining application that generates numbers for a spreadsheet—some sort of application is required if the data are to be used at all. These are two of the major categories. They are often complementary, since OLAP results can be presented by data visualization software.

2.

Compare OLTP to OLAP. This is the same as Review Question 1 of Section 6.3.

3.

Explain the relationship between OLAP and data mining.

From Section 6.3: “Users … drive OLAP, whereas data mining looks for relationships, with only some direction from the analyst.”

From Section 6.6: “The easiest way to distinguish data mining from the various forms of OLAP is that OLAP can only answer questions you are certain to ask, whereas data mining answers questions you don’t necessarily know you should ask.”

In other words, OLAP is guided by people; data mining is driven by algorithms.

In some situations the two can produce different views of the same data, which can combine to provide greater insight than would be possible using either type of tool alone.

In other cases the nature of a requirement makes it is clear which is called for.

4.

Describe multidimensionality and explain its potential benefits for MSS. See answer to Review Question 1 of Section 6.5.

5.

Discuss the strategic benefits of BA. This is a similar question to Review Question 1 of Section 6.11, though it is more specific in asking only for strategic benefits. A correct answer to the section review question could in theory focus on managerial/tactical and/or operational benefits, but since as a practical matter BA has much of its value at the strategic level, this is unlikely.

6.

Describe the concepts underlying Web intelligence and Web analytics.

Web intelligence: This a similar question to Review Question 9 of Section 6.9.

Web analytics: Users’ behavior patterns at Web sites are important information for those who would like to design Web sites that motivate certain types of behavior.

7.

Why do vendors that offer ERP tools (e.g., SAP, Oracle) offer BA tools as well?

ERP and other transactional systems create masses of data. These data are of great potential value for decision making. BA tools make them useful for this purpose.

8.

Compare data mining and predictive analysis and discuss why some think that they are similar while others think the opposite.

The term predictive analysis covers a wide range of methods whose purpose is to use historical information to predict the future. For example, one might know that a large number of international calls to a particular country on a calling card, absent any prior history of such calls on that card, may indicate possible fraud and justify confirming that the calls were legitimate, or that heavy pressure on a car’s brakes often indicates a coming collision so the seat belts should be tightened in anticipation.

The purpose of data mining is to extract relationships from data. To the extent that these relationships are between historical data and future activity, data mining is a form of predictive analysis. One could conclude that they are similar since they overlap in this way. Finding activity patterns that predict fraud, for example, could be either or both.

However, data mining is good for more than predictive analysis. For example, data mining could be used to find what items tend to show up together in the same real or figurative shopping cart. There is no prediction here; what a decision maker does with that information is beyond the scope of data mining. Conversely, predictive analysis does not require data mining—the relationships that lead to the prediction could have been arrived at in a different way. Application Case 6.3 in this chapter is an example of the latter situation: it’s predictive analysis, but it didn’t involve data mining.

9.

Will BI replace the business analyst? Discuss. (Hint: See McKnight, 2005.)

The referenced article makes good arguments that it will not, concluding with these thoughts: …the great judgment found in the great business analysts we call our users will always be required for our success in BI. We cannot hope, nor should we strive for, any diminishment of that role with our BI deliverables. Many tie BI justifications to business headcount reduction, but that rarely occurs as a result of BI. Partnering with our analysts/ users/customers is a better approach.

The writer of this IM is not personally aware of any convincing arguments to the contrary, short of the eventual arrival of a thinking computer. McKnight states early in his article “The question is not whether or not BI systems will be able to truly think. Of course, they will not.” I do not accept this blanket statement as being necessarily true forever, but it will (in my opinion) remain true1 as long as any of the other material in this book remains relevant or today’s students still make decisions on the basis of what they will have learned from it.

1

Ignoring science-fiction scenarios based on the arrival on Earth of a computer-based intelligence from an extraterrestrial civilization more advanced than our own. That speculation is not within the scope of this book.

10.

Will ADS tools replace the business analyst?

This is not exactly the same question as the previous one: BI tools were designed to support business analysts in decision making, while ADS systems are designed to make decisions without human intervention.

However, the decisions made by ADS tools are not those that a business analyst would otherwise make. They are lower-level, operational decisions. How they are made is of little relevance to whether or not organizations need business analysts. (See Discussion Question 14 below.)

11.

Discuss the benefits of GIS as visualization support to decision making.

GIS deals with location-based information, typically presenting it in the form of a map.

Many people find it easiest to understand location-based information this way. Decision makers who understand the information they use are likely to make better decisions than they would if they didn’t.

12.

Differentiate predictive analysis from data mining. What do they have in common?

Both predictive analysis and data mining can find relationships in data that a decision maker didn’t know were there. However, data mining is concerned with finding relationships that existed in the past. The application of those relationships to the future, via decision making, is not within its sphere. Predictive analysis is future-oriented: asking what, based on the data available, will happen if a given future event occurs?

13.

Relate competitive analysis to BI. Competitive analysis is the process of obtaining and analyzing information about a firm’s competitors. While competitive analysis as a concept is not new—Joshua is recorded as having sent spies into Jericho, then analyzed their reports to choose a strategy—BI is being increasingly applied to the second of these phases, analyzing the information.

14.

Discuss how ADS can support frontline employees (e.g., those who provide customer service).

From Section 6.9: ADS are most suitable for decisions that must be made frequently and/or rapidly, using information that is available electronically. The knowledge and decision criteria, as well as the business rules that express them, must be highly structured, and the problem situation must be well understood. High-quality data and/or knowledge about the problem domain must be available.

Customer service decisions often meet these criteria. While exceptions will always occur, new situations will always arise in which front-line employees must use their intelligence rather than relying on a set of computer-based rules derived from past experience, such rules can still be developed to handle a large fraction of the situations that will arise— often the great majority. In those situations, ADS can help front-line employees provide the answer more quickly, give information to new employees who would otherwise not

know the answer, help train these employees and ensure consistency in what different customers are told.

15.

Why is real-time BA becoming critical?

This is related to Review Question 2 of Section 6.9, which asked about the benefits of real-time BA. The reason it is becoming critical is because these benefits are becoming increasingly important if a company is to maintain or improve its competitive position.

Another answer could be based on the part of Section 6.9 which states “Today, many customers demand current data. Therefore, more and more IT managers are facing the question of how to conduct analytic systems in real-time. … The demand for realtime applications continues to grow.” The underlying reason for the growth in demand for these applications, here, is customer demand for current data.

16.

Relate advanced analytics to ERP and SCM. Current ERP and SCM products incorporate tools for advanced analytics.

17.

Discuss the relationship between visualization and Excel.

Excel-based data can be exported to visualization tools, an approach known as “spreadsheet visualization.” Given the wide use of Excel as an analysis tool and the value of visualization in decision support, this synergy can lead to better decisions than using either on its own. (The graphing capabilities of most spreadsheet software, including Excel, are too limited to qualify as true visualization.)

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

Why is a data warehouse needed in the GeoStor system?

“A data warehouse (DW) is a pool of data produced to support decision making; it is also a repository of current and historical data of potential interest to managers throughout the organization.” (Section 5.2). Without a data warehouse, or a comparable store by another name, GeoStor would not have the variety of data that it needs in order to perform its wide range of functions.

2.

List the major benefits of GeoStor to Arkansas and to private-sector users.

To the state of Arkansas: GeoStore has as many benefits as there are state agencies that use it, or more since some agencies use it in more than one way. As one example, it reduced the time needed to search for some types of documents by a factor of over 15, from 23.5 hours per document to 1.5 hours. This translates to a tangible saving of $2 million in the first 18 months the system was in use. Other benefits result from improved access to educational resource information, up to date information about the state of highways that might be needed in an emergency, and more

To private-sector users: They can obtain easily-understood information about what Arkansas can offer them in, for example, deciding where to locate a new plant. (One could argue philosophically that this is not necessarily a benefit to them, since a more effective presentation of Arkansas’s offerings may lead a company to locate there when it would be better served by choosing a state that offered more but did not present its offerings as clearly or attractively. That is beyond the scope of this question. In any case, proper use of decision support tools by the company would cancel out this effect.

Arkansas wants to present its offerings as effectively as possible. It is hardly fair to blame Arkansas for a company’s failure to analyze different states’ offerings objectively.)

3.

Check Oracle’s MapViewer and comment about its data visualization capabilities.

Oracle’s MapViewer is the visualization component that supports Oracle Locator or Oracle Spatial software for managing location-based data. (Locator is part of Oracle’s basic database product; Spatial is a separately-priced add-on.) It can be used to build and display maps in a browser or a specific application. It does not manage the content of those maps. It is intended for use by Web application developers; it is not directly used by end users who need to look at location-based data.

As a development tool, Oracle MapViewer is able to display a wide variety of maps, in a wide variety of formats, using an equally wide variety of associated nonspatial data. As examples of its capabilities, it can display a map on a predefined base map, plus any other predefined visuals not part of the base map; it can customize a map’s coverage, background color, size, image format, and notations; it can select patterns or other visual themes based on data retrieved via an SQL query; it can display a map with features that the application has obtained from other sources; and it can present either the resulting binary image data or a URL to the generated map image to the calling application or Web page. In doing so, it hides the complexity of spatial data queries and cartographic rendering from application developers, who can integrate MapViewer (relatively!) easily into their applications.

(A full list of MapViewer’s capabilities would require a book the size of its reference manual: exactly 250 pages.)

4.

The state of Arkansas was the first to have such a system, so it has a competitive edge in attracting new business to the state. Explain why. Can this advantage be sustained when other states will use a similar system in the future?

Organizations (including U.S. territories and corresponding subdivisions of other countries) obtain a competitive advantage, among other ways, by offering their customers (however “customer” is defined) something that their competition is not perceived to offer. This can mean either that the competitor does not offer it, or that the competitor offers it but the customer doesn’t know it. A system such as GeoStor can make a state’s customers (here, businesses considering relocation) aware of its advantages. States that do not have a comparable system will have a more difficult task of trying to do that. In any such situation, a competitor that has a more difficult job of doing something will tend to be less successful at it.

This is a typical situation. Systems that provide a competitive advantage to their first users often become competitive necessities, required by every organization of a

given type, later. Examples are not hard to find in many industries—consider online package tracking, concert ticket buying or reserving a seat on an airplane.

5.

Can any GIS data be combined with GPS data? For what uses?

GIS data can often be combined usefully with GPS data. The text (Section 6.8) gives examples of where this was done and the benefits of doing it.

However, this question asks about “any GIS data.” The word any is key. Some GIS data, especially static GIS data, cannot be combined usefully with GPS data.

For example, an HMO has used a GIS to portray how its hospitals, physicians and other health care providers are located relative to where the employees of a company live.

This is a static picture, intended to help get the HMO a contract with the company by conveying the message “Our facilities are well positioned for where your employees live.” For this purpose, at least, GPS data is of little use. (One can imagine other applications for which some of the same data might be used with GPS data, such as looking up a physician near the site of an automobile accident, but that is beside the point of this counterexample.)

CHAPTER

7

Data, Text, and Web Mining

Learning Objectives for Chapter 7 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Describe data mining and list its objectives and benefits Understand different purposes and applications of data mining Understand different methods of data mining, especially clustering and decision tree models Build expertise in the use of some data mining software Learn the process of data mining projects Understand data mining pitfalls and myths Describe text mining and its objectives and benefits Appreciate the use of text mining in business applications Describe Web mining and its objectives and benefits

CHAPTER OVERVIEW This chapter is a “deep dive” into the data mining concepts students were introduced to (in one short paragraph) in Section 6.6 of the previous chapter. This chapter discusses general data mining concepts excluding neural networks; Chapter 8 will cover those.

Data mining is a process, a technology, a solution and (to software vendors) a market. It resembles the elephant in the fable about the blind men in that sense: one can easily see it from one perspective and lose sight of the overall picture. This chapter, which covers it from all points of view, can do a lot to avoid this form of myopia.

CHAPTER OUTLINE 7.1

OPENING VIGNETTE: HIGHMARK, INC., EMPLOYS DATA MINING TO MANAGE INSURANCE COSTS A. DATA IN MANAGED CARE ORGANIZATIONS

B. C.

THE NEED FOR DATA MINING  Questions for the Opening Vignette WHAT WE CAN LEARN FROM THIS VIGNETTE

7.2

DATA MINING, CONCEPTS AND APPLICATIONS A. DEFINITIONS, CHARACTERISTICS AND BENEFITS ⧫ Application Case 7.1: Data Help Foretell Customer Needs B. HOW DATA MINING WORKS 1.

Classification 2.

Clustering 3.

Association 4.

Sequence Discovery 5.

Visualization 6.

Regression 7.

Forecasting 8.

Data Mining Functions Summary ⧫ Application Case 7.2: Motor Vehicle Accidents and Driver Distractions ⧫ Application Case 7.3: Data Mining to Identify Customer Behavior C. DATA MINING APPLICATIONS ⧫ Application Case 7.4: Customizing Medicine ⧫ Application Case 7.5: A Mine on Terrorist Funding  Section 7.2 Review Questions

7.3

DATA MINING TECHNIQUES AND TOOLS A. CLASSIFICATION 1.

Decision Trees B. CLUSTER ANALYSIS FOR DATA MINING C. CLUSTER ANALYSIS METHODS D. CLUSTERING EXAMPLE E. HIERARCHICAL CLUSTERING METHOD AND EXAMPLE F. DATA MINING SOFTWARE 1.

Cluster Analysis Software  Section 7.3 Review Questions

7.4

DATA MINING PROJECT PROCESSES ⧫ Technology Insights 7.6: Data Mining Myths ⧫ Technology Insights 7.7: Data Mining Blunders ⧫ Application Case 7.8: DHS Data Mining Spinoffs and Advances in Law Enforcement  Section 7.4 Review Questions

7.5

TEXT MINING ⧫ Application Case 7.9: Flying Through Text A. HOW TO MINE TEXT  Section 7.5 Review Questions

7.6

WEB MINING ⧫ Application Case 7.10: Caught in a Web]  Section 7.6 Review Questions Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata Student Network (TSN) and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises ⧫ End of Chapter Application Case: Hewlett-Packard and Text Mining  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

Data mining was introduced briefly in Chapter 1, mentioned briefly in passing in Chapters 2 and 3, and defined again (again briefly) in Chapter 6. Students who read the book carefully have encountered the term more than a dozen times in all these places, but in all likelihood have only the most general idea of what it means. By the time they finish this chapter, that should no longer be the case.

The order of sections in this chapter follows a logical sequence that most instructors will find appropriate. After the opening vignette, Section 7.2 discusses the general concepts. Sections 7.3 and 7.4 are complimentary: 7.3 (the longest section in this chapter by far) covers the technical side, whereas 7.4 focuses on the management issues. If your students have a business orientation, you may want to skip the part of 7.3 that deals with the Gini index, but your may instead want to add specific examples to one or more of the general process models in 7.4.

Sections 7.5 and 7.6 cover emerging areas of data mining: you can leave them were they are, cover them along with the other technical topics in 7.3, or possibly leave them for students to read as enrichment topics if time is tight. (Personally, I don’t urge the last option; I think these areas are becoming important enough, and are different enough from traditional mining of structured data in a data warehouse, to be worth class time.)

Compared to most other chapters in this book, this one contains a large number of application cases: ten of them, plus the opening vignette (Section 7.1) on Highmark and the endof-chapter case on HP. This should help the material come alive for students. The Internet exercises at the end are also helpful, especially the ones that send students to vendor sites for case studies and white papers. It can be a good class or group project for students to go to several different vendor sites and compare what they find on each. (It’s not just about products and user case studies. For example, what does the prominence of data mining software products on each site say about that firm’s strategy and the place of data mining in it? What they find on sas.com will be quite different in that regard from what they find on fairisaac.com, and so on.)

Regarding the opening vignette: the link http://sas.com/success/highmark.html, listed under Sources on p. 304, is no longer live. This was discovered after the book went to press. Two other Highmark case studies, both of which involve data mining, can be found at sas.com: http://sas.com/success/highmark_reimbursement.html http://sas.com/success/highmark_fraud.html (Underscores after “highmark” in both the above URLs may be obscured by the underline.) In addition, the following article is related: http://www.kmworld.com/Articles/ReadArticle.aspx?ArticleID=18289 Finally, if you assign any end-of-chapter questions numbered from 9 up, please see the notes on “ANSWERS TO QUESTIONS FOR DISCUSSION”questions 7 and 8 in this manual.

The extra questions will in all likelihood be removed in the book’s second printing. Should that happen, numbers of questions beyond them will change. You may want to confirm the numbering in your students’ copies, as this may differ from that in your desk copy, so they don’t inadvertently answer the wrong question—or make sure they know the content, not just the number(s), of the question(s) you assign.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 7.1 Review Questions 1.

Why are companies such as Highmark using data mining applications?

Per the beginning of the case, for “analyzing patient data and unraveling mysteries that could lead to better managed care at lower costs.” More specifically, data mining can analyze information to find relationships and correlations. For example, relationships between medical conditions (such as diabetes, one example in the case) and other parameters can be found. This in turn lays the foundation for intervention programs, which can lead to improved treatment outcomes.

2.

Why were managed care organizations initially hesitant to use data mining applications?

Managed care organizations were initially hesitant to use data mining applications because of its cost and complexity. As regards complexity, they find their databases and database applications complex enough already. They do not want to increase this complexity by adding data mining applications.

3.

What are the potential threats that could arise due to data mining applications?

The authors intend this as an open-ended question, outside the vignette itself. There is therefore no single correct answer. Student answers may cover areas such as competitive espionage stealing the results of the data mining application, improper use of its results (such as making decisions on the basis of factors that act as surrogates for race/sex/age/etc.), damage to the business if the application is used incorrectly by someone who does not understand what it does, and others.

4.

What complexities arise when data mining is used in health care organizations?

“Managed care organizations are inundated with data, and some of [them] do not want to add to the complexity by adding data mining applications.”

“They may [be] unable to decide why and how to analyze their data.”

These and similar complexities are not unique to health care organizations.

5.

Assume that you are an employer and that your managed care organization raises your rate based on the results of data mining and predictive modeling software. Would you accept the organization’s software predictions?

This question refers to justifying a proposed increase at contract renewal time, not to raising the rate in midstream (which most contracts would not allow). So, it asks whether you would consider the managed care organization’s data mining predictions as adequate justification for the increase.

Student responses to this scenario will vary. As a minimum, they should insist on seeing the justification and analysis. Beyond that, they could reasonably ask for access to the data in order to perform their own analysis—which could conceivably lead to a request for a reduction. One might also want to include a provision that rates will be reduced in future years if a similar analysis points in that direction, even if it doesn’t now.

Section 7.2 Review Questions 1.

Define data mining.

Its early definition was “the process through which previously undiscovered patterns in data are identified.” However, its usage has been stretched well beyond this definition, since vendors feel that it can have sales value for almost any type of data analysis tool. A more current definition would be “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases.” This includes most types of automated data analysis. Data mining, then, is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models.

2.

What factors have increased the popularity of data mining recently?

The following six factors were listed in the text: 1.

General recognition of the untapped value in large databases 2.

Consolidation of database records, tending toward a single customer view 3.

Consolidation of databases, including the concept of an information warehouse 4.

Reduction in the cost of data storage and processing, providing for the ability to collect and accumulate data 5.

Intense competition for a customer’s attention in an increasingly saturated marketplace 6.

Business movement to convert information resources into nonphysical form (soft copy) One might add increased availability of data mining software to this list. Other answers, based on other sources and/or the student’s insight, are possible.

3.

Identify at least five applications of data mining.

Table 7.1 lists market basket analysis, target marketing, market segmentation, customer life cycle analysis and sales forecasting, among others.

Page 311, under the heading “Data Mining Applications” which may suggest to students that it answers this question, lists several application areas including marketing, banking, retailing and sales, manufacturing and production, brokerage and securities trading, and more.

This question is subject to interpretation as to what an “application” is. If you want students to identify individual applications, such as those listed in Application Cases 7.1–7.5 in this section, or if you have any other desired interpretation, you might want to be specific about your requirements.

4.

What are some major characteristics of data mining?

• • • • • • •

5.

Data are often buried deep within very large databases, which sometimes contain data from several years. In many cases, the data are cleansed and consolidated in a data warehouse.

The data mining environment is usually a client/server architecture or a Webbased architecture.

Sophisticated new tools, including advanced visualization tools, help to remove the information ore buried in corporate files or archival public records. Finding it involves massaging and synchronizing the data to get the right results.

The miner is often an end user, empowered by data drills and other power query tools to ask ad hoc questions and obtain answers quickly, with little or no programming skill.

Striking it rich often involves finding an unexpected result and requires end users to think creatively.

Data mining tools are readily combined with spreadsheets and other software development tools. Thus, the mined data can be analyzed and processed quickly and easily.

Because of the large amounts of data and massive search efforts, parallel processing can be useful for data mining.

What are some of the main categories of data mining technologies? Define at least three of them.

Several software technologies are discussed beginning on p. 307. These include: • Classification: analyzing the historical behavior of groups of entities with similar characteristics, to predict the future behavior of a new entity from its similarity to those groups • Clustering: finding groups of entities with similar characteristics • Association: establishing relationships among items that occur together • Sequence discovery: finding time-based associations • Visualization: presenting results obtained through one or more of the other methods

• •

Regression: a statistical estimation technique based on fitting a curve defined by a mathematical equation of known type but unknown parameters to existing data Forecasting: estimating a future data value based on past data values

Similarly to Question 3 of this section on applications, the term “technologies” in this question is subject to interpretation. If you have a specific sense in mind, you may want to tell your students.

Section 7.3 Review Questions 1.

Identify at least three of the main methods of data mining.

Seven methods are listed near the beginning of this section: statistical methods, decision trees, case-based reasoning, neural computing, intelligent agents, genetic algorithms and rule induction (in the “other tools” bullet item). A correct answer can list any three of these.

2.

Give examples of situations in which classification would be an appropriate data mining technique.

Several such situations are listed in the first paragraph of the Classification subsection of this section. These include credit approval, target marketing, fraud detection and telecommunications. (The last item can be more generally described as customer retention analysis. It is used in other industries as well.)

3.

List at least two classification techniques.

Six methods are listed in the second shaded area of p. 315, just above the heading “Decision Trees:” decision tree classification, statistical analysis, neural networks, Bayesian classification, genetic algorithms and the “rough set” approach. A correct answer can list any two or more of these.

4.

What are some of the criteria for comparing and selecting the best classification technique?

Five criteria are listed in the shaded area at the bottom of p. 314 and continuing onto p.

315: predictive accuracy, speed, robustness, scalability and interpretability.

5.

Briefly describe the general algorithm used in decision trees.

The general idea is to split the data set by one attribute, then split each resulting subset by an attribute (not necessarily the same attribute for each subset) until the desired groups are obtained. The algorithm is defined more precisely on p. 316 as the following series of steps: 1.

Create a root node and select a splitting attribute.

2.

Add a branch to the root node for each split candidate value and label.

3.

6.

Take the following iterative steps: a.

Classify data by applying the split value. b.

If a stopping point is reached, create a leaf node and label it. Otherwise, build another subtree.

Define Gini index. What does it measure?

The Gini index, in its original form, measures the diversity of a population: that is, the degree to which a certain attribute is distributed evenly or unevenly across it. A population in which everyone has the same income would have a Gini index of 1; one in which all the income belonged to a single person, with all the others having none, would have a Gini index of 0.

In a data mining context, the Gini index can be used to measure the degree to which a particular split of a data set divides it into two distinct groups. The lower the Gini index associated with a splitting point, the better the split.

7.

Give examples of situations in which cluster analysis would be an appropriate data mining technique.

Cluster analysis has been used extensively for fraud detection (both credit card and ecommerce) and market segmentation of customers (e.g., in CRM systems). More business applications continue to be developed as the capabilities of this method become better understood.

8.

What is the major difference between cluster analysis and classification?

Classification is a type of problem: sorting cases into groups so that members of the same group are strongly associated in some meaningful way (e.g., likelihood of a transaction being fraudulent) and members of different groups are loosely associated. Cluster analysis is one way to identify the groups that classification requires.

9.

What are some of the methods for cluster analysis?

There are five general methods: statistical (including both hierarchical and nonhierarchical) methods, optimal methods, neural networks, fuzzy logic, and genetic algorithms. These methods can be grouped into two classes: those that start with the entire set in one cluster and split it up (divisive methods) and those that start with each item in one cluster and then combine them (agglomerative methods).

10.

What is the measure of similarity in a cluster analysis setting?

One measure of similarity is the sum of the pairwise distances among all item pairs in each cluster. Ideally, this measure is to be minimized.

Section 7.4 Review Questions 1.

Briefly describe the CRISP-DM model of the data mining process.

This process has six steps. First, an understanding of the data and an understanding of the business issues to be addressed are developed concurrently. Next, data are prepared for modeling; are modeled; model results are evaluated; and the models can be employed for regular use.

2.

What are the steps of data mining, according to the Six Sigma method?

This process has five steps, based on the standard Six Sigma method for improving the statistical quality of any process. They are: Define, Measure, Analyze, Improve and Control.

3.

What steps are recommended by the SAS Institute’s SEMMA model of the data mining process?

This process has six steps. They are: Sample, Explore, Modify, Model, and Assess.

4.

Define KDD. KDD, Knowledge Discovery in Databases, is sometimes used as a synonym for data mining.

Others define KDD as a more comprehensive process that encompasses data mining. It begins prior to data mining, with the selection of the data to be used for mining; includes the data mining activities themselves, and concludes with interpretation and evaluation of the data mining results.

5.

Identify some of the myths of data mining.

“Technology Insights:7.6” gives these five “myths,” as well as their corresponding realities: • Data mining provides instant, crystal-ball predictions.

• Data mining is not yet viable for business applications.

• Data mining requires a separate, dedicated database.

• Only those with advanced degrees can do data mining.

• Data mining is only for large firms that have lots of customer data.

6.

What are some of the common errors made in data mining projects?

“Technology Insights 7.7” gives these ten: 1.

Selecting the wrong problem for data mining.

2.

Ignoring what your sponsor thinks data mining is and what it really can and cannot do.

3.

Leaving insufficient time for data preparation. It takes more effort than one often expects.

4.

Looking only at aggregated results and not at individual records.

5.

Being sloppy about keeping track of the mining procedure and results.

6.

Ignoring suspicious findings and quickly moving on.

7.

8.

9.

10.

Running mining algorithms repeatedly and blindly. It is important to think hard enough about the next stage of data analysis. Data mining is a very hands-on activity.

Believing everything you are told about data.

Believing everything you are told about your own data mining analysis.

Measuring your results differently from the way your sponsor measures them.

Section 7.5 Review Questions 1.

What is text mining?

Text mining is the application of data mining to unstructured, or less structured, text files.

2.

What are some of the text processing specialties related to text mining?

Information retrieval, information extraction, and information summarization.

3.

Give examples of automated information extraction applications.

The text provides one example in Application Case 7.9, in which Aer Lingus uses data mining to extract information from incident reports. A quick Web search found several others, including: searching the medical literature to discover the function of a newly sequenced gene, searching patent application files for patents that are potentially relevant to new research, and determining which documents in a set were probably written by the same author. Students should be able to find many without difficulty.

4.

Identify some current and potential applications of text mining.

See previous question. The answer would be similar but can be broader, as the overall category of text mining includes both the retrieval step, before automated information extraction, and the summarization step, after it.

5.

What are some of the basic steps in performing text mining?

1.

2.

3.

4.

6.

Eliminate common words such as “the” and “and” Replace words with their stems or roots Group synonyms, including applicable phrases Calculate weights, based on the frequency of a term’s use within a document (a high value suggests relevance to that document) and the number of documents in the set that use it (a high value suggests it is common in the subject area and therefore not useful for classification)

Identify the basic components of any text mining system.

1.

2.

3.

A system to handle documents in various forms from different sources.

Components to process these documents to create data files that can be mined.

Data mining tools such as classifying and clustering software

Section 7.6 Review Questions 1.

Define Web mining.

Web mining is the discovery and analysis of interesting and useful information from the Web and about the Web, usually through Web-based tools.

2.

Identify the three main areas of Web mining.

The three main areas of Web mining are Web content mining, Web structure mining and Web usage (or activity) mining.

3.

What uses are made of Web structure mining?

Web structure mining can be used to rank search engine results, to determine which pages to add to a collection of Web sites, to understand relationships and to understand Web communities.

4.

What types of information do Web servers collect when you visit a Web page and engage in an interactive activity?

• • •

5.

Log data in server access logs, referrer logs, agent logs and client-side cookies User profiles Metadata such as page attributes, content attributes and usage data

List five examples of observations a Web mining analyst may be able to make by Web usage data.

The bulleted list on p. 335 gives these six uses for Web mining: • Determine the lifetime value of clients • Design cross-marketing strategies across products • Evaluate promotional campaigns • Target electronic ads and coupons at user groups, based on their access patterns • Predict user behavior based on previously learned rules and users’ profiles • Present dynamic information to users based on their interests and profiles You may have a different understanding of the word “observation” and therefore of what you expect in a correct answer. If this is the case, you may want to clarify your expectations to students.

6.

Identify applications developed by major e-commerce sites that may be based on Web usage mining.

Amazon.com offers four examples of Web usage mining applications: site personalization by name, offers based on previous purchases1, offers based on choices of similar users, and special “Gold Box” offers. Students can also find examples by

1

It does more than simply show products that resemble what one bought in the past. If a person returns to the site a year after buying a gift for a two-year-old child, he or she will be shown gifts suitable for threeyear-olds.

searching the Web or, in all likelihood, by visiting e-commerce sites they have used in the past.

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Explain the relationship between OLAP and data mining.

The general distinction is that OLAP is guided by a person whereas data mining is driven by software. Data mining thus requires more software capability: “Data mining software features more complicated algorithms for neural networking, clustering, segmentation, and classifications that are generally more sophisticated than OLAP methods” (pp. 323– 4).

The two can work together. Both, after all, are directed toward finding and understanding patterns in data. One aspect of data mining is adding analytics, using software capabilities such as those listed above, to database tools such as OLAP. (This is the same question as Discussion Question 2 at the end of Chapter 6. The answer given there is based on the content of that chapter. This answer is based on the content of this one.)

2.

Discuss what an organization should consider before making a decision to purchase data mining software.

The factors are the same as those it should consider before making a decision to purchase any type of software, though they will differ in details. Major ones include: • • • • • • •

3.

Functionality: Does it do what the organization needs done?

Ease of use: Will its interface be acceptable to its expected user community?

Compatibility: Does it work with the organization’s existing databases, infrastructure, etc.?

Support: Will acceptable support be available?

Vendor considerations: Is the vendor stable? Is it committed to this product for the future? Do its strategic directions match the organization’s expected needs?

Financial: Do the potential benefits justify its cost? Is a particular package priced in line with competitive packages?

Legal: Are the contract terms acceptable, or (if this size of this purchase is large enough to justify flexibility on its part) will the vendor negotiate to make them so?

Distinguish data mining from other analytic tools.

The major distinction here is between data mining as an automated process and the use of other analytic tools as guided processes.

4.

Define data mining and list its major technologies.

Data mining is defined in the answer to Review Question 1 of Section 7.2.

Major categories of data mining technologies are listed in the answer to Review Question 5 of that section.

5.

Differentiate data mining, text mining, and Web mining.

Data mining is the general concept, defined in the answer to Review Question 1 of Section 7.2.

Text mining is a specific application of data mining: applying it to unstructured text files.

Web mining is a specific application of data mining: applying it to information on and about the Web (content, structure and usage).

6.

Distinguish between KDD and data mining. Explain how the Web is affecting BI/business analytics methods and technologies.

There are two separate questions here.

For the first, see the answer to Review Question 4 of Section 7.4 above.

For the second: The Web affects BI/business analytics methods and technologies in three ways: 1.

Web browsers define a common user interface for BI applications.

2.

The Web provides rapid access to data that is useful in these applications.

3.

The Web has itself become a source of data to be analyzed by these methods.

7.

Discuss what an organization should consider before making a decision to purchase data mining software.

This is the same question as Discussion Question 2. The inadvertent duplication was discovered after the book had gone to press.

8.

Distinguish data mining from other analytic tools.

This is the same question as Discussion Question 3. The inadvertent duplication was discovered after the book had gone to press.

9.

Explain the process of text mining.

See answers to Review Questions 2 and 5 in Section 7.5. The entire process is as follows: 1.

Information retrieval 2.

Information extraction 2.1.

Eliminate common words such as “the” and “and” 2.2.

Replace words with their stems or roots 2.3.

Group synonyms, including applicable phrases 2.4.

Calculate weights, based on the frequency of use in a document 3.

Information summarization

10.

Describe why visualization is so important in BI. BI (business intelligence) covers a variety of methods whose purpose is to extract information from data. The reason for so doing is to permit managers to make informed

decisions on the basis of the data in question. Making informed decisions requires them to understand the information that the BI tools have extracted. Visualization presents this information in a way that most managers find easy to understand.

(You may prefer to assign this question with Section 6.7.)

11.

In what situations might classification and clustering software be used together?

Classification involves identifying patterns of data that assign a case or entity to a specific category based on similarity to known members of that category. The categories and their characteristics must come from somewhere. If they are previously known, that is sufficient. If they are not, clustering software can be used to create them.

12.

Discuss why text mining is not the same thing as natural language processing.

Text mining seeks to create information about the content of the text, but not necessarily to “understand” that content directly. Understanding text, in the sense of being able to take action based on interpreting its content, is the goal of natural language processing.

ANSWERS TO END- OF- CHAPTER APPLICATION CASE QUESTIONS 1.

   

What is the practical application of text mining?

The specific practical application of text mining discussed in this case is extracting useful information from the thousands of customer communications (such as e-mails) that HP receives.

2.

How do you think text mining techniques could be used in other businesses?

Any business that deals with individual customers, either during the sales process or (like HP) after that, has a wealth of data in its written customer communications. Any such business can obtain useful information from a suitable analysis of those data. The methods HP used to do this are generally applicable across all such industries.

Many businesses may have other uses for these techniques as well. These depend on the industry that the business (or other organization) is in.

3.

What were HP’s challenges in text mining? How were they overcome?

HP had several challenges in this text mining application. These included combining data from structured databases with unstructured data from text, customizing the software package’s vocabulary for the words used at HP, bringing in text data that is not in traditional data warehouses, and using the results to find insights into HP’s customers.

4.

In what other areas, in your opinion, can HP use text mining?

This is an open-ended question, as “in your opinion” suggests. HP’s size and its range of products, target markets and marketing channels give it a corresponding range of

potential text mining applications. A few possibilities are listed below. Students may have many others, based on their own experiences and creativity.

• Searching internal R&D documents to find relevant work done in another part of the company in order to avoid duplication of effort • Finding human resources policies and reports throughout the company for guidance to a division that has encountered a situation it has not seen before • Looking for vendor evaluations for components needed in a new product, where it is possible that another part of the company may have needed similar components in the past • Scanning technical literature for evidence of “prior art” and existing patents prior to filing a patent application • Determining the source of information leaks from the board of directors based on similarities between the leaked information and known writings of different directors, making it unnecessary to use illegal and quasi-legal methods to uncover their source. (This was major source of concern, and potentially of legal difficulties, for HP as a company and for some of its top managers individually in 2006.)

CHAPTER

8

Neural Networks for Data Mining

Learning Objectives for Chapter 8 ⧫ ⧫ ⧫ ⧫ ⧫

Understand the concept and different types of artificial neural networks (ANN) Learn the advantages and limitations of ANN Understand how backpropagation neural networks learn Understand the complete process of using neural networks Appreciate the wide variety of applications of neural networks

CHAPTER OVERVIEW The previous chapter discussed data mining. This one dives deeper into a specific technology that can be of use in data mining: neural networks. In common with other areas of artificial intelligence, these did not live up to their earlier hype—but this is more the fault of excessive early hype than it is of the concepts themselves. As the examples in the chapter demonstrate convincingly, neural networks have useful business applications today. This chapter will teach students about them.

CHAPTER OUTLINE 8.1

OPENING VIGNETTE: USING NEURAL NETWORKS TO PREDICT BEER FLAVORS WITH CHEMICAL ANALYSIS A. PROBLEM B. SOLUTION C. RESULTS  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

8.2

BASIC CONCEPTS OF NEURAL NETWORKS A. BIOLOGICAL AND ARTIFICIAL NEURAL NETWORKS ⧫ Technology Insights 8.1: The Relationship Between Biological and Artificial Neural Networks ⧫ Application Case 8.2: Neural Networks Help Reduce Telecommunications Fraud B. ELEMENTS OF ANN 1.

Processing Element 2.

Network Structure 3.

Network Information Processing 4.

Hidden Layers C. NEURAL NETWORK ARCHITECTURES  Section 8.2 Review Questions

8.3

LEARNING IN ANN ⧫ Application Case 8.3: Neural Networks Help Deliver Microsoft’s Mail to the Intended Audience] A. THE GENERAL ANN LEARNING PROCESS B. HOW A NETWORK LEARNS C. BACKPROPAGATION  Section 8.3 Review Questions

8.4

DEVELOPING NEURAL NETWORK-BASED SYSTEMS A. DATA COLLECTION AND PREPARATION B. SELECTION OF NETWORK STRUCTURE C. LEARNING ALGORITHM SELECTION D. NETWORK TRAINING ⧫ Technology Insights 8.4: ANN Software E. TESTING F. IMPLEMENTATION OF AN ANN  Section 8.4 Review Questions

8.5

A SAMPLE NEURAL NETWORK PROJECT  Section 8.5 Review Questions

8.6

OTHER NEURAL NETWORK PARADIGMS A. HOPFIELD NETWORKS B. SELF-ORGANIZING NETWORKS  Section 8.6 Review Questions

8.7

APPLICATIONS OF ANN ⧫ Application Case 8.5: Neural Networks for Breast Cancer Diagnosis  Section 8.7 Review Questions

8.8

A NEURAL NETWORK SOFTWARE DEMONSTRATION (This section has no review questions.)

Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata Student Network (TSN) and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises ⧫ End of Chapter Application Case: Sovereign Credit Ratings Using Neural Networks  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

Neural networks are a bit out of the DSS/BI mainstream but have proven their worth in a number of applications, most of them related to pattern recognition and classification. Their importance in a course on decision support systems will vary from instructor to instructor. If you would rather use your finite time on other topics, it can be skipped without interfering with students’ ability to understand the chapters that follow. There is little to be gained from a “once over lightly” on this topic: if it’s worth covering at all, beyond perhaps reading the opening sections for a general idea of the concept, it’s worth covering in full depth. (If you want to cover it but are also a bit pressed for time, you can omit Section 8.6 without impacting what students get from the rest of the chapter.)

Regarding that vignette: Students may note that the chapter title is “Neural Networks for Data Mining.” Data mining usually makes one think of data warehouses. The connection between this vignette and data mining is not via a data warehouse, but it is still data mining in a more general sense: finding meaning in the data that relate a beer’s flavor to its chemical composition.

Where the book refers to artificial neurons “more or less resembling the structure of” their biological counterparts in Section 8.2 (the first “content” section of the chapter), students must understand that this does not mean a physical resemblance. An artificial neuron is a silicon construct, usually embodied in a software model. The ANN equivalent of sending a signal to another biological neuron is placing data in a shared location or sending a software message. The structural resemblance is functional, not physical. This may seem obvious to an instructor, but is often not equally obvious to students seeing the concept for the first time.

In Section 8.3, it is important for students to understand the simple learning example on pp. 358–60. The material in the book is not enough for them to learn on their own, and some errors in Table 8.1 that were unfortunately not caught in time to correct in the printed book don’t help. It is best to avoid them by ignoring the two rightmost columns of the table (under the heading “Final”). Instead, take the final weights from one stage from the initial weights of the next one, which are correct.

Note in this table that only the weight corresponding to an active input line is changed. It is changed by 0.2 (the learning rate parameter) of the Delta amount (which is always 0 or 1).

Thus, if the neuron outputs 0 where it should output 1, the corresponding input weights are increased. (Were it to output 1 where it should output 0, they would be decreased, but that doesn’t happen in this example.)

Also, note that the “step” number in the leftmost column might better be called “cycle.”

The learning process goes through sixteen steps here: four cycles, each going through each of the four possible input values. The learning is complete after twelve steps. The last cycle of four steps serves simply to confirm this. As no more learning takes place in any of the steps of that cycle—Delta is 0 in every one—the process can terminate.

The rest of the chapter poses no particular pedagogic challenges. There is, however, one other error that made its way into print that you may want to mention. Team Assignment 4 on pp.

378–9 shows qualitative GMAT scores from 35 to 90. As students who are applying or have recently applied to graduate school will probably know, the qualitative portion of the GMAT is scored from 0 to 60. This does not affect the validity of the exercise, but you may wish to point it out so the error does not impact your students’ opinion of the book. (This will be corrected in the second printing, so check a student’s or a bookstore copy before mentioning this point even if the error is in your desk copy.)

Finally, if you assign the end-of-chapter case on sovereign credit ratings (which IMHO is excellent) you might want to tell your students what sovereign credit ratings are before they start in on it. Granted, that’s giving away the answer to the first part of the case’s Question 1, but I think the benefits in reducing their confusion are worth it. The second part of that question is still a good one to ask.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 8.1 Review Questions 1.

Why is beer flavor important to the profitability of Coors?

It is important because beer flavor is an important factor in customer preferences.

2.

What is the objective of the neural network used at Coors?

To determine the link between the chemical composition of a beer, which can be measured, and its flavor, which cannot be—but which consumers can detect and care about.

3.

Why were the results of the Coors neural network initially poor, and what was done to improve the results?

The results were initially poor for two reasons. First, the network was only trained with one type of beer, so the variation in its inputs was low. Second, it was only trained for one flavor factor, so its performance was impacted by the inputs that had no impact on that factor but whose variations within the training sample created distracting “noise.”

To improve the results, Coors trained the neural network using a wider variety of products and more combinations of inputs. (The initial plan was to train it on all possible combinations of inputs, but this turned out to be impractical for reasons discussed on p.

345.)

4.

What benefits might Coors derive if this project is successful?

If this project is successful, Coors might ultimately be able to control the flavors of its beers via their chemical composition, which could be monitored automatically and presumably controlled during the brewing process. Being able to do this depends, of course, on more than knowing the relationships between the chemicals in beer and its flavor.

5.

What modifications would you provide to improve the results of beer flavor prediction?

Based on the next-to-last paragraph of the case, I would refine the sensitivity of the instrumentation, measure a larger number of flavor-active compounds, and measure the factors that contribute to mouth-feel and the beer’s physical characteristics. Whether this is practical, if practical whether it is cost-effective, and whether it would result in a more effective been production process than Coors can achieve with experienced, professional brewmasters, is (as far as we can tell from this case) still an open question.

Section 8.2 Review Questions 6.

What is an ANN? An ANN (artificial neural network) is a computer program that models a biological neural network to learn to recognize patterns, then recognize them in new data.

7.

Explain the following terms: neuron, axon, and synapse.

A neuron is defined in the book (p. 347) as one of the cells in the human brain. More generally, and as students may have learned in biology courses, it is an electrically excitable cell in the nervous system of an animal.

An axon is the component of a neuron which sends signals to other cells.

A synapse is a junction between two neurons through which signals pass from one neuron to the next, perhaps being altered en route.

(The corresponding question in the previous edition of this book, Question 7 of Chapter 12, also asked students to explain dendrite. An answer that explains that term, in addition to the above three, should be regarded with suspicion.)

8.

How do weights function in an ANN? Weights define the impact that a given input has on a neuron in the next layer. As such, they embody what the network has learned so far. As a network learns, its weights are adjusted.

9.

What is the role of the summation function?

The summation function determines the total input to a neuron by calculating the weighted sum of its individual input values. Its output is input to the transformation (transfer) function.

10.

What is the role of the transformation function?

The transformation (or transfer) function determines the output of a neuron from its input (i.e., output of the summation function).

Many possible transformation functions exist. Most are sigmoid functions. (A sigmoid function is a non-decreasing function whose value is in the range 0–1.) A simple transformation function produces an output of 0 for inputs up to a threshold value, an output of 1 from that value up.

Section 8.3 Review Questions 1.

Briefly describe backpropagation.

Backpropagation (short for back-error propagation) is a method of supervised training for an ANN. In it, output errors are propagated back to preceding layers where they are used to change the weights in the network.

2.

What is the purpose of a threshold value in a learning algorithm?

A threshold value, described in Section 8.2, applies to a transformation in which low inputs to a neuron produce an output of 0 and high inputs produce an output of 1. The threshold value is the input value at which the output changes from 0 to 1.

In a learning algorithm, the threshold values in a network collectively help determine the output that the network will have for a given input. Since the purpose of training is to use outputs to control the weights in the network, the threshold values, by influencing the outputs that are produced for a given set of weights, ultimately help determine the final set of weights that the training process will arrive at.

3.

What is the purpose of a learning rate?

To control the rate at which output errors can change the weights in the network. A low learning rate may result in a slow learning process, but a high learning rate can lead to instability of the learning process.

4.

How does error between actual and predicted outcomes affect the value of weights in neural networks?

It determines the changes that will be applied to the weights. To oversimplify somewhat, weights that contribute to correct answers are increased, while those that contribute to incorrect answers are decreased.

Section 8.4 Review Questions 1.

List the nine steps in conducting a neural network project.

These are shown in the flowchart of Figure 8.11. After testing (step 8), it is possible to return to a previous step: Step 1: Collect data Step 2: Separate (data) into training and test sets Step 3: Define a network structure Step 4: Select a learning algorithm Step 5: Set parameters and values, initialize weights Step 6: Transform data to network inputs Step 7: Start training, determine and revise weights Step 8: Stop and test Step 9: Implementation: use the network with new cases

2.

What are some of the design parameters for developing a neural network?

• • •

Number of layers (at least two; for n layers, there will be n–2 hidden layers) Number of nodes in each layer Transformation functions, with parameters (e.g., threshold values) as applicable to each • Initial weights • Learning parameters: learning rate, momentum The last two bullets do not represent design parameters of the network itself. They are parameters of its development process.

3.

Describe different types of neural network software available today.

• • • • •

4.

Complete packages that lack only data and training (analogous to expert system shells) Components of data mining tools Specialized development tools through which one can develop one’s own ANN software Add-ins to spreadsheets and other software Subroutine and/or class libraries for use with general-purpose programming languages

• How are neural networks implemented in practice when the training/testing is complete?

First, they must often be interfaced with other information systems from which they are to obtain input data.

User training is usually necessary. This should include explanations to gain the confidence of users and management in the new approach.

Finally, ongoing monitoring and feedback to the developers will lead to system improvements and long-term success.

5.

What parameters may need to be adjusted in the neural network training process?

The learning rate and momentum may be adjusted to control the learning (training) process itself.

The intent of the training process is to adjust the weights in the system, though this is done by the software, not by people, so the weights do not “need to be adjusted” in the sense of this question.

Beyond that, it is possible for any ANN training process not to converge to a useful system. If that happens it is necessary to adjust higher-level design parameters, to rethink the nature of system inputs or the training set, or in an extreme case to abandon the project altogether.

Section 8.5 Review Questions 1.

What parameters can be used to predict failure of a firm?

The standard set of parameters used for this purposes includes these five: • X1: Working capital/total assets • X2: Retained earnings/total assets • X3: Earnings before interest and taxes/total assets • X4: Market value of equity/total debt • X5: Sales/total assets Since the question reads “… can be used …,” students could theoretically come up with other lists and argue that they are correct, but in this case that would be inappropriate. If you assign this question, you may wish to guard against that possibility in advance by explaining what you want. (These are also the parameters used in Hands-On Exercise 3 on p. 375ff at the end of the chapter, so if you want to use that exercise, there is no wiggle room in parameter choice.)

2.

How were data divided between training and test sets for this experiment?

The training set was chosen by selecting 20 records (firms) from the overall sample, maintaining an equal number of firms that went bankrupt during the year and firms that did not.

3.

Explain what is meant by resampling in this context? How was resampling used for this problem?

Resampling, as a process in statistics, refers to taking a series of successive samples from a set. (It is often contrasted with taking one large sample of the same total size.) In this problem, it was used to create different pairs of training and testing sets, so as to evaluate the effectiveness of the ANN for solving this type of problem better. With a single

training/testing set pair, one could not be sure its results were not due to random chance, and would have no idea of the statistical variation one might expect in its results.

4.

What were the network parameters for this neural network experiment?

Five input neurons (one for each parameter), one hidden layer with ten neurons, and two output neurons (one for each result).

5.

How was an output converted to mean bankrupt or nonbankrupt?

Each output node was considered to indicate bankrupt or nonbankrupt, as appropriate, if its value was 0.5 or greater. The system overall was considered to indicate “bankrupt” if the value of the “bankrupt” output neuron was ≥0.5 and that of the “nonbankrupt” output neuron was <0.5, and vice versa. Cases in which the outputs of both neurons were ≥0.5 or both were <0.5 did not indicate either meaning and were considered misclassifications.

6.

How did the neural network model compare with a logistic regression model in this experiment?

In general, the neural network performed “significantly better.”

Section 8.6 Review Questions 1.

List some of the different types of neural networks.

• • •

Discussed earlier in the chapter: MLP-based (multilayer perceptron-based) neural networks with feed-forward or recurrent structures.

Covered in this section: Hopfield networks, self-organizing networks (or Kohonen networks).

Others listed: radial basis function networks, probabilistic neural networks, generalized regression neural networks, support vector machines.

Sufficiently motivated students may be able to find other types in online or literature searches.

2.

What is one key difference between an MLP network and a Kohonen network?

The structure of an MLP network is fixed by its designer. The structure of a Kohonen network is intended to change as part of its learning process. It eventually evolves to represent the structure (e.g., clustering) of the input data.

3.

What is another name for a Kohonen network?

A “self-organizing network.”

4.

Briefly describe a Hopfield network.

A Hopfield network is a neural network in which all nodes are connected to each other— as distinct from an MLP network, where nodes are only connected in a “forward” direction. Each node has a stored value in addition to instantaneous inputs and outputs.

As nodes fire, these values change. Eventually the network converges to a solution. The nature of the problem to be solved is embedded in the structure of the network rather than in definitions of its inputs and in its weights.

Section 8.7 Review Questions 1.

List some applications of neural networks in accounting/finance.

Stock/commodity trading, loan application approval, predicting ratings/profitability of corporate bonds and exchange rate prediction are cited in the text. Students may find other examples.

2.

What are some engineering applications of neural networks?

Analyzing magnetic resonance spectroscopy data could be considered to be an engineering application and is cited in the text. Students should be expected to find other examples.

3.

How have neural networks been used in the health care field?

Tumor classification is cited in the text (in Application Case 8.5). Students should be expected to find other examples.

4.

What are some applications of neural networks in information security?

Credit card fraud detection and validation of bank signatures are cited in the text.

Students should be expected to find other examples.

5.

Conduct a Web search to identify homeland security applications of neural networks.

Open-ended answer, depending on what a student finds. (A Google search for the two strings “homeland security” and “neural network” on November 13, 2006 returned 48,600 pages. A scan of the summary information provided by Google for the first several dozen suggested that many would have provided relevant answers to this question.)

Section 8.8 Review Questions Section 8.8 has no review questions.

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar?

The physical aspects of biological neural networks are not mimicked by ANNs.

ANNs mimic the functional aspects of biological networks at an elementary level, though practical ANNs have far fewer neurons than an insect’s (let alone any mammal’s, even a gerbil’s) brain.

2.

The performance of ANN relies heavily on the summation and transformation functions.

Explain the combined effects of the summation and transformation functions and how they differ from statistical regression analysis.

The summation and transformation functions use a neuron’s inputs to create its output.

First, the summation function aggregates the inputs into a single value based on weighted summation. Second, the transformation function converts this input value to an output value, generally between 0 and 1.

This approach differs from statistical regression analysis in its ability to find more complex relationships than statistical comparisons can find.

3.

ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode.

Learning in supervised mode is simpler. Since we know the desired output and the value of inputs as well as all the algorithms that express relationships in the system, it is possible to compute values of output for given weights. By comparing actual and desired weights one can see the difference. This difference can be reduced by adjusting the values of the weights. Ideally the differences should be driven to zero. Finding the ideal value of the weights is called the learning or training.

In unsupervised situations, the desired outputs are not specified. Therefore, the process is only semi-automatic (i.e., a human must examine the result to determine when the training needs to stop). Weights and other parameters can be adjusted once the outputs are examined.

4.

Explain the difference between a training set and a testing set. Why do we need to differentiate them? Can the same set be used for both purposes? Why or why not?

The same set cannot be used for both purposes because that would create a “self-fulfilling prophecy:” having learned to separate two parts of the input set from each other, correct behavior would then be tested by simply confirming that it has done so. This is not meaningful. It is necessary to test whether or not it has learned to separate the two parts of the overall universe under investigation from each other.

For example, suppose an aerial surveillance ANN is designed to tell tanks from rocks and is thus trained with photographs of those two items. Suppose, hypothetically, that the photographs of tanks were (because of differences in the time they were taken, equipment used, etc.) slightly darker than those of rocks. The ANN might successfully learn to separate the photographs in the training set into the two appropriate parts. A person simply confirming that it did so, using those same photographs, would have no way of knowing that it might have just learned to tell darker photographs from lighter ones. It is necessary to test the ANN with a totally different set of photographs to confirm that it can actually identify tanks.

5.

Say that a neural network has been constructed to predict the creditworthiness of applicants. There are two output nodes: one for yes (1 = yes, 0 = no) and one for no (1= no, 0 = yes). An applicant receives a score of 0.83 for the “yes” output node and a 0.44 for the “no” output node. Discuss what may have happened and whether the applicant is a good credit risk.

The score for “yes” is stronger than the score for “no.” The factors that led to the scores are unknown, but the two scores can be expected to be independent of each other and may be based on different combinations of inputs. Were a simple threshold value of 0.5 used to determine the validity of an output value this network would have affirmed creditworthiness, denied non-creditworthiness.

The ANN output suggests that the applicant is probably a good (though perhaps not outstanding) credit risk. The relatively high score for non-creditworthiness suggests a possible problem in the applicant’s background that should be looked into further before credit is granted.

6.

Everyone would like to make a great deal of money on the stock market. Only a few are very successful. Why is using an ANN a promising approach? What can it do that other decision support technologies cannot do? How could it fail?

The factors that lead to changes in stock prices are imperfectly known. Attempts to understand them have not been successful1. However, it is reasonable to suppose that those factors, while we may not understand them, do exist. An ANN might be able to find them in a mass of data because it has no preconceived notions about what they should be.

Other decision support technologies are driven by human guidance in some way and rely ultimately on human decision makers to identify the relevant factors.

An ANN could fail in this application if it cannot identify the relevant factors, or if it identifies a set of factors that would have predicted stock movements during one time period (when the market was driven by one set of factors) but are not useful in predicting them in another period (when it is driven by different factors). Attempting to use factors derived from analysis of an earlier period to guide investments in a later one could be a recipe for bankruptcy.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

What are sovereign ratings? Why are sovereign ratings important?

Sovereign ratings are an assessment of the creditworthiness of an independent (i.e., sovereign) country and the long-term value of its currency. They are important because the credit rating of any entity is one factor in determining the availability of credit to that entity and the interest rate it will have to pay if credit is available. This is as true of countries as it is of businesses.

In addition, sovereign ratings are one measure of the creditworthiness of firms that transact business in that country’s currency. Businesses care about the ratings of the

1

It is possible that someone has figured them out but is, for understandable reasons, keeping quiet.

countries in which they operate because they require access to capital. A low sovereign rating of a country in which they are based, or in which they have significant operations, will impact this access.

2.

What role do rating agencies play?

Rating agencies issue sovereign ratings, much as they issue credit ratings for businesses and individuals.

3.

What is the role of neural networks in sovereign ratings? Do you think we should entirely rely on neural network prediction?

The last paragraph of the case answers this question well: The findings indicate that ANN for fitting credit ratings for corporations as practiced by the major rating agencies (e.g., Moody’s, S&P) can be successfully applied to sovereign ratings. An analyst’s role and partly subjective process of assigning a credit rating cannot be eliminated by neural networks.

However, it appears that ANN could inform and support the analyst in the decision-making process.

4.

What would you conclude from the outcomes of neural network–based rating prediction experiments?

See previous answer.

5.

You are a credit analyst at Standard & Poor’s. You have been asked to rate India’s sovereign credit. What factors would you consider in arriving at a credit rating, and how would you use neural networks in arriving at a result? Explain.

The quantifiable factors one should consider in arriving at a credit rating are those listed in the table at the upper right of p. 380: the ratio of external debt to exports, the ratio of fiscal balance to GDP for the past three years, the current account balance relative to GDP for the past three years, the average inflation rate for that period, GDP per capita, average annual rate of GDP growth over the past four years, and the IMF classification of the country as industrial or not industrial. (The table gives the conventional units in which some of these factors are measured, such as percentages or U.S. dollars, but using those units is not inherent to the concepts being measured.)

One could use a neural network to arrive at a sovereign credit rating by, first, training a neural network to reproduce the ratings assigned by human credit analysts on the basis of the listed factors. This was done in the experiment described in the case. One would then input the known values of these factors for India to the network. It would output a credit rating. As stated in the answer to question 3 above, this should probably not be used as the final credit rating but can help the analyst reach it. The analyst may want to consider other factors, such as the likelihood of a change in government that would lead to changes to economic policies, and similar non-quantifiable factors before assigning the final rating.

CHAPTER

9

Business Performance Management

Learning Objectives for Chapter 9 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the all-encompassing nature of business performance management (BPM) Understand the closed-loop processes linking strategy to execution Describe some of the best practices in planning and management reporting Describe the difference between performance management and measurement Understand the role of methodologies in BPM Describe the basic elements of the balanced scorecard and Six Sigma methodologies Describe the differences between scorecards and dashboards Understand some of the basics of dashboard design Understand the potential uses of business activity monitoring (BAM)

CHAPTER OVERVIEW

              

Managing the performance of the business is, in a nutshell, what executive jobs are all about.

The technology that supports this part of their work is, appropriately, termed Business Performance Management. It differs from the subjects of previous chapters in its top-level strategic focus, which lets top managers focus on the key factors that drive success rather than getting bogged down in a mass of detail. Because of its different focus and different audience. BPM requires different technologies and approaches from other types of BI/DSS. This chapter discusses them.

CHAPTER OUTLINE 9.1

OPENING VIGNETTE: CISCO AND THE VIRTUAL CLOSE A. PROBLEM B. SOLUTION C. RESULTS

165

166

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

D.

 Questions for the Opening Vignette WHAT WE CAN LEARN FROM THIS VIGNETTE

9.2

BUSINESS PERFORMANCE MANAGEMENT (BPM) OVERVIEW A. BPM DEFINED B. BPM AND BI COMPARED C. SUMMARY OF BPM PROCESSES  Section 9.2 Review Questions

9.3

STRATEGIZE: WHERE DO WE WANT TO GO? A. STRATEGIC PLANNING B. THE STRATEGY GAP  Section 9.3 Review Questions

9.4

PLAN: HOW DO WE GET THERE? A. OPERATIONAL PLANNING B. FINANCIAL PLANNING AND BUDGETING  Section 9.4 Review Questions

9.5

MONITOR: HOW ARE WE DOING? A. DIAGNOSTIC CONTROL SYSTEMS B. PITFALLS OF VARIANCE ANALYSIS ⧫ Application Case 9.1: Discovery-Driven Planning: The Case of Euro Disney  Section 9.5 Review Questions

9.6

ACT AND ADJUST: WHAT DO WE NEED TO DO DIFFERENTLY? A. PAUCITY OF ANALYSIS ⧫ Technology Insights 9.2: Benchmarking Best Practices for BPM  Section 9.6 Review Questions

9.7

PERFORMANCE MEASUREMENT A. PROBLEMS WITH EXISTING PERFORMANCE MEASUREMENT SYSTEMS B. EFFECTIVE PERFORMANCE MEASUREMENT ⧫ Application Case 9.3: International Truck and Engine Corporation  Section 9.7 Review Questions

9.8

BPM METHODOLOGIES A. BALANCED SCORECARD (BSC) 1.

The Meaning of Balance 2.

Aligning Strategies and Actions 3.

BSC Certification B. SIX SIGMA 1.

Six Sigma Defined 2.

The DMAIC Performance Model

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

3.

167

Limitations of Six Sigma ⧫ Technology Insights 9.4: BSC Meets Six Sigma  Section 9.8 Review Questions

9.9

BPM ARCHITECTURE AND APPLICATIONS A. BPM ARCHITECTURE 1.

Database Tier 2.

BPM Applications 3.

BPM User Interface B. COMMERCIAL BPM SITES ⧫ Technology Insights 9.5: The Structure and Benefits of a Commercial BPM Suite  Section 9.9 Review Questions

9.10

PERFORMANCE DASHBOARDS A. DASHBOARDS VERSUS SCORECARDS ⧫ Application Case 9.6: Dashboards for Doctors B. DASHBOARD DESIGN C. WHAT TO LOOK FOR IN A DASHBOARD  Section 9.10 Review Questions

9.11

BUSINESS ACTIVITY MONITORING (BAM) ⧫ Application Case 9.7: City of Albuquerque Goes Real-Time A. BENEFITS OF BAM B. BAM ISSUES C. BAM VENDORS  Section 9.11 Review Questions Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises ⧫ End of Chapter Application Case: Vigilant Information Systems at Western Digital  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

        

This chapter is different from those that have gone before or will come in the future. It discusses largely management issues, with secondary attention (though not no attention!) to the technologies used to support them. Management students will take to such things as the BPM processes

168

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

or Six Sigma like ducks to water; in fact, they may have already studied some of them in other courses. Technically oriented students may wonder why you’re wasting their time.

The fact is that a great deal of BI work today is focused on BPM and the other areas covered in this chapter. They are today’s version of what was once called an “executive information system,” highly evolved but conceptually similar. Other than the technology having changed, so has the way these systems are used. The old EIS was pretty much independent of everything else that went on in the company; it could be used, or not used, according to the whims of each executive. Today’s BPM software is integrated into the planning structure of the organization. Using it is not optional. As regards your students: understanding that structure isn’t optional either if they are to work with this kind of software.

If you happen to have mostly a technical background yourself, you may find some of the concepts in this chapter a bit alien and difficult to teach. If it’s practical in your situation, you may be able to prevail on one of your more business-oriented colleagues to take a class or two at this point.

From a pedagogic point of view, this chapter is more unified than some of the others because it covers a subject area which is itself inherently more unified. You won’t have to add as many transitions from one topic to the next; they’re already there. Students will probably find that it flows better, too. Compared to the earlier chapters, you (and they) will probably also find that the Questions for Discussion at the end involve more hard work!

Section 9.2, the first section after the opening vignette, introduces BPM concepts. The basic BPM methodology breaks down into four processes, which are discussed in order in Sections 9.3–9.6. Once students understand the processes, the rest of the chapter looks at specific aspects of them in more depth. In many cases these discussions are connected back to the four processes of BPM. In others, you might want to make the connection explicit yourself.

A few detail-level comments: • In Section 9.9, much of Technology Insights 9.5 is from Hyperion’s marketing materials. This is, of course, properly acknowledged, the content is factually correct, but students may not notice the source and its tone tends to convey competitive advantages that may not exist. It may be useful to point out that other vendors also have good products, with another perhaps better for a given situation, and that the high Gartner ranking that led to Hyperion’s value as an example may change for reasons discussed at the end of the answer to Review Question 7 of Section 9.9.

• Students may have learned through any of several popular introductory MIS texts (whose authors should really know better!) that expert systems are an idea whose time has passed, that they did not fulfill their early promise and that they are seldom used today. The “conventional wisdom” in those books is incorrect. This one conveys a more accurate picture of expert systems’ value. The technology, far from disappearing, has become so well accepted that its concepts are now used in a wide variety of applications without a second thought. BAM (Section 9.11) is one; its “rules engines” are in effect an embedded expert system by another name.

You may wish to point this out to help give students a more accurate perspective on the topic as they approach Chapter 12.

• Also in Section 9.11, the Web reference at the very end of the section (the list of BAM vendors) is no longer valid, perhaps because Computerworld’s staff felt it had become more than “slightly” dated. You can offer students

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

169

http://www.businessactivitymonitoring.com/vendors.htm instead (though some of it is out of date too) or simply have them do a Web search.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

      

Section 9.1 Review Questions 1.

What is the virtual close system?

“Closing,” in an accounting context, refers to closing the books: completing the information gathering and calculations needed to present the total financial picture of an organization as of a known point in time.

The virtual close system was an information system that enabled Cisco to obtain comparable management information in two hours rather than the two weeks they needed previously—and which many companies still need.

2.

What explanation did John Chambers, Cisco CEO, give for the company’s downturn in 2001?

His fundamental explanation was that Cisco was no exception to the dramatic economic downturns that started in early 2001 and that affected the entire high-tech segment. This downturn is sometimes referred to as the “dot-com implosion,” since it was triggered by the bursting of the Internet commerce bubble, but its effects spilled over to companies such as Cisco that depended on e-commerce indirectly for much of their business. (The first part of Section 9.3, on p. 388, discusses the extent of the overall downturn.)

Beyond that, he pointed out that the virtual close system did not provide the sort of information needed to predict macroeconomic trends. Indeed, it was never intended to provide that sort of information. Measuring and monitoring, however sophisticated it may be, can only take one so far.

3.

What was Cisco’s strategic response to its financial downturn in 2001?

Cisco had both a short-term response and a long-term response.

Its short-term response consisted of cost-cutting moves designed to improve its profitability at lower revenue levels. These moves included: • Reducing its inventory (with a $2.5 billion write-down) • Reducing its headcount (by 20,000 employees) • Reducing the number of models in each product group (i.e., simplifying its product line) • Extending its product line via internal development rather than acquisition Its long-term response consisted of leveraging its business and technical expertise to enter six new product areas: optical networks, wireless LANs, network security, IP telephony, storage networking and consumer/home networking.

One could argue that only the long-term response was strategic and the other was tactical, but the strategy consisted of the combination of both. Without the short-term part, the long-term part could not have succeeded.

170

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Section 9.2 Review Questions 1.

Define BPM. “A framework for organizing, automating, and analyzing business methodologies, metrics, processes, and systems to drive the overall performance of the enterprise. It helps organizations translate a unified set of objectives into plans, monitor execution, and deliver critical insight to improve financial and operational performance.” (p. 386)

2.

Summarize the ways in which BI for BPM differs from traditional BI. BPM is an outgrowth of BI and extends it. BI needs to be extended to support BPM properly. Thirteen specific differences are listed in Table 9.1, p. 387.

3.

List the major BPM processes.

1.

2.

3.

4.

Strategize Plan Monitor Act and Adjust

Section 9.3 Review Questions 1.

Why does a company need a well-formulated strategy?

“Without specific goals or objectives, it is difficult to evaluate alternative courses of action. Without specific priorities, there is no way to determine how to allocate resources among the alternatives selected. Without plans, there is no way to guide the actions among those working on the alternatives. Without analysis and evaluation, there is no way to determine which of the opportunities are succeeding or failing. Goals, objectives, priorities, plans, and critical thinking are all part of a well-defined strategy.” (p.388)

2.

What are the basic tasks in the strategic planning process?

1.

Conduct a current situation analysis 2.

Determine the planning horizon 3.

Conduct an environmental scan 4.

Identify critical success factors 5.

Complete a gap analysis 6.

Create a strategic vision 7.

Develop a business strategy 8.

Identify strategic objectives and goals

Section 9.4 Review Questions

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

1.

171

What is the goal of operational planning?

An operational plan translates an organization’s strategic objectives and goals into a set of well-defined tactics and initiatives, resource requirements, and expected results for some future time period, usually, but not always, a year—in other words, to make the strategic plan into a useful guide for operational decision making.

2.

What is tactic-centric planning? What is budget-centric planning?

These are two approaches to operational planning.

In tactic-centric planning, the focus is on arriving at tactics that will meet the strategic goals. After those have been arrived at, the cost of implementing them is considered. If it exceeds the organization’s willingness to spend, the plan can be revised.

In budget-centric planning, the focus is on arriving at tactics that meet financial targets. After these have been arrived at, the results of using them are considered. If they do not meet the organization’s strategic goals, the plan can be revised.

3.

What is the primary goal of a financial plan?

The primary goal is to allocate resources in line with the organization’s needs and priorities.

Section 9.5 Review Questions 1.

What are the key elements of a diagnostic control system?

Inputs, a process for transforming inputs into outputs, a standard against which to compare these outputs, and a feedback channel allowing information on variances to be communicated so that it can be acted upon.

2.

What is management by exception?

A management approach whereby the great majority of management’s time and attention is focused on areas in which a diagnostic control system identified significant variations from the standard, in order to investigate the cause of the variation and initiate appropriate action.

3.

What is one of the major pitfalls of variance analysis, from a managerial perspective?

The text identifies two major pitfalls on p. 393. A correct answer can discuss either: 1.

Focus on identifying, diagnosing and correcting negative variances, thus failing to capitalize on opportunities that might be signaled by positive variances.

2.

Take the comparison standard as given, not considering the possibility that changed circumstances or invalid assumptions might make the standard wrong rather than the process being at fault.

4.

What types of planning problems are addressed by discovery-driven planning?

Discovery-driven planning addresses an aspect of item (2) in the previous answer, implicit assumptions that turn out to be faulty.

172

5.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

What are the major steps in discovery-driven planning?

1.

2.

3.

4.

Reverse-income statement, in which the desired results are used to work back to the necessary business drivers.

Pro forma operations specs, in which the cost component of the reverse-income statement are analyzed.

Assumption checklist, which identifies possible performance metrics.

Milestone planning, deciding when assumptions from the checklist will be tested.

Section 9.6 Review Questions 1.

What do The Hackett Group benchmarks measure?

The Hackett Group, part of the consulting firm Answerthink (answerthink.com) has been benchmarking the planning and reporting practices of organizations, in considerable detail as discussed in Technology Insights 9.2 (p. 397).

2.

Based on The Hackett Group benchmark results, in what ways do world-class companies differ from average companies?

In several ways: • Average companies do not want to revisit their plans more often than necessary, typically annually. World-class companies revisit and adjust strategic plans and forecasts frequently throughout the year.

• Average companies look at many metrics and line numbers. World-class companies focus on a small set of key performance indicators that are tied to their strategic objectives.

• World-class companies are significantly more efficient than their peers at managing costs.

• World-class companies focus on operational excellence.

• World-class companies experience reduced voluntary turnover, in part due to devoting more resources to employee life cycle issues.

• World-class companies drive down costs via hybrid sourcing strategies that combine shared services and outsourcing.

• World-class companies provide their management with the tools and training to leverage corporate information.

• World-class companies align their strategic and tactical plans, enabling functional areas to align with and contribute to overall goals.

3.

According to The Hackett Group, how much time and resources does the average company spend on planning and management reporting?

The time varies with the specific plan or report: 5 months to create a strategic plan, 4 months for a tactical plan, 5 days to close their books at the end of an accounting period (you may want to relate this to the comparative figures for Cisco, before and after virtual closing, in the chapter’s opening vignette), 11 days after month’s end to produce management reports, and 15 days after month’s end to produce revised forecasts.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

173

The resources are given as 23,000–25,000 (misprinted as “23,000 and 25,000” in the book’s first printing) person-days per year.

(Note that while the question refers to “the average company,” the text discusses “the average billion-dollar company.” Since the average company has far less than $1 billion annual revenue, it is fair to assume that it spends less time and fewer resources on planning and reporting, but the text does not give us that information. If you assign this question, you may want to clarify that it refers to billion-dollar companies, not to companies in general.)

4.

What is one of the major reasons companies spend so little time analyzing the management data they collect?

Because planning and reporting take so much time, management has little time left to review results from a strategic perspective, decide what should be done differently, and act on that decision.

Several other reasons are given in this section as well. A correct answer may reflect any of them.

Section 9.7 Review Questions 1.

What is a performance measurement system?

A system whose purpose is to “Assist managers in tracking the implementations of business strategy by comparing actual results against strategic goals and objectives. A performance measurement system typically comprises systematic methods of setting business goals together with periodic feedback reports that indicate progress against goals.” (p. 398, original source given there)

2.

What are some of the drawbacks of relying solely on financial metrics for measuring performance?

The text cites these four (p. 399): • Financial measures are usually reported by organizational structures (e.g., research and development expenses) and not by the processes that produced them.

• Financial measures are lagging indicators, telling us what happened, not why it happened or what is likely to happen in the future.

• Financial measures (e.g., administrative overhead) are often the product of allocations that are not related to the underlying processes that generated them.

• Financial measures are focused on the short term, and provide little information about the longer term.

3.

What is the principle of obliquity?

Michael Hammer’s principle of obliquity states that the measures management tracks are generally those which it cannot control directly (but only obliquely, hence the name of the principle). This leads to the conclusion that some way of influencing these measures through management action is necessary.

174

4.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

What are some of the characteristics of a “good” collection of performance measures?

The text cites the following five (p. 400): • Measures should focus on key factors.

• Measures should be a mix of past, present, and future.

• Measures should balance the needs of shareholders, employees, partners, suppliers, and other stakeholders.

• Measures should start at the top and flow down to the bottom.

• Measures need to have targets that are based on research and reality rather than be arbitrary.

5.

What steps did International Truck and Engine use to create the measures for its system?

What factors were critical to its success?

International Truck and Engine created the measures for its system by surveying its financial managers, then re-surveying them annually to determine measures to add, continue, or drop. The critical factors in this system’s success were: • A highly committed executive sponsor • Adequate funding • An incremental development and release schedule • A thorough Web-based requirements assessment • A knowledgeable and talented team • A core of subject matter experts • An existing data warehouse • A pilot to demonstrate the value and validity of the product • Trust in the data • Communication to explain the benefits, functionality, and rollout of the new system

Section 9.8 Review Questions 1.

What are the characteristics of an effective performance measurement system?

An effective performance measurement system should help do the following: • Align top-level strategic objectives and bottom-level initiatives.

• Identify opportunities and problems in a timely fashion.

• Determine priorities and allocate resources based on those priorities.

• Change measurements when the underlying processes and strategies change.

• Delineate responsibilities, understand actual performance relative to responsibilities, and reward and recognize accomplishments.

• Take action to improve processes and procedures when the data warrant it.

• Plan and forecast in a more reliable and timely fashion.

2.

What are the four perspectives in BSC? The four perspectives in BSC are financial, customer, internal business processes, and learning/growth.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

3.

175

What does the term balanced refer to in BSC? It refers to the balance in the combined set of measures used in a balanced scorecard.

Specifically, the method attempts to achieve a balance between financial and nonfinancial indicators, between leading and lagging indicators, between internal and external indicators, between quantitative and qualitative indicators, and between shortterm and long-term indicators. By so doing it hopes to eliminate some of the defects of traditional management measuring methods, such as the tendency to concentrate almost exclusively on internal, financial, quantitative lagging indicators. (See answer to Review Question 2 of Section 9.7 for more on the dangers of relying exclusively, or even largely, on financial indicators.)

4.

What steps are used in creating a BSC? Step1. Identify strategic objectives for each of the perspectives (about 15 to 25 in all).

Step 2. Associate measures with each of the strategic objectives; a mix of quantitative and qualitative should be used.

Step 3. Assign targets to the measures.

Step 4. List strategic initiatives to accomplish each of the objectives (i.e., responsibilities).

Step 5. Link the various strategic objectives through a cause-and-effect diagram called a strategy map.

5.

What is a strategy map?

A strategy map is a graphic description of the relationships among the key organizational objectives for all four BSC perspectives. A strategy map begins at the top with financial objectives, moving down from there to customer objectives, internal objectives, and learning objectives.

6.

What does Six Sigma refer to?

The Greek letter sigma (σ) is the mathematical symbol for the standard deviation of a normal distribution. A deviation of six standard deviations from the norm is likely to occur about 3.4 times in one million. (About two-thirds of any sample is within one standard deviation of the mean; about 95 percent, within two.)

Actually, this is an oversimplification. The Six Sigma approach allows for the process mean to shift by 1.5σ, so the defect frequency of 3.4 per million is actually based on 4.5σ deviation from the worst expected process mean. A true 6σ deviation occurs less than once in 50 million samples for a normal distribution.

Today, the term Six Sigma refers to a structured methodology for quality improvement and control directed toward reducing process defects to this level. From a practical standpoint, that is essentially equivalent to eliminating them.

7.

What are the basic processes in the DMAIC model?

The basic processes in the DMAIC model is define, measure, analyze, improve, and control.

176

8.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

How can BSC and Six Sigma be integrated? What are some of the barriers to integrating the two?

The two can be integrated by using BSC on the front end, to identify performance weaknesses and uncover opportunities for improvement. Six Sigma then follows as a methodology for dealing with the underlying causes of a performance shortfall.

Six Sigma also defines an infrastructure to ensure that performance improvement activities have the necessary resources. It relies on a small group of critical roles to make this happen. This aspect of Six Sigma complements BSC; it does not conflict with it. For more on these roles, see Online File W9.4.

Companies that have successfully integrated BSC with Six Sigma were generally found to have achieved integration by five steps: translating their strategy into quantifiable objectives, cascading objectives through the organization, setting targets based on listening to customers, implementing strategic projects using Six Sigma, and executing processes in a consistent fashion to deliver business results.

Barriers to integration include the need to align the actions of individuals throughout the organization with measures flowing from organizational objectives, the need to align management culture with the fact-based approaches of BSC and Six Sigma rather than a “fix it now” approach, and the need to develop an IT infrastructure that supports their integration.

Section 9.9 Review Questions 1.

What is system architecture?

“The term system architecture refers to both the logical and physical design of a system.

The logical design details the functional elements of a system and their interactions. The physical design specifies how the logical design is actually implemented and deployed across a specific set of technologies, such as desktop computers, servers, databases, communication protocols, and the like.” (p. 409)

2.

What are the three key elements of a BPM system architecture?

The three key elements of a BPM system are: the database tier, the application tier, and the client (or user interface) tier.

Note that this is only one of many possible BPM system architectures. It is the one proposed by the BPM standards group, which gives it a certain credibility; it is without question a good one; but students should understand that it is not the only possible one. As technology evolves over the decades that today’s students will be in the work force, tradeoffs will undoubtedly change. The ideal architecture for BPM will equally undoubtedly evolve with those changes.

Also, the architecture at the level of the one-line list of its key elements is also the architecture of many other information systems besides BPM. It is the further specification of those elements that makes it uniquely a BPM architecture. The question as written does not require them to be further specified in a correct answer. If you want more, you may want to be specific as to your requirements here. Alternatively, you may want to assign some of the following questions in addition.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

3.

177

What does a BPM database provide?

A BPM database contains a definition of the organization, covering the past, present, and future, expressed in terms of structures and business rules. It also contains information, or links to it, in the form of plans and results. The other components of a BPM system operate on this database.

Expressed differently, a BPM database provides both metadata and data for BPM applications. Metadata include field definitions, hierarchical structures, measurement definitions, account attributions, currency conversion methods, and the like. Much of the data in this database is structured or quantitative in nature, and is stored in relational tables or multidimensional cubes. The database also houses other structured and unstructured data artifacts that are important for various BPM processes: strategy documents, tactical plans, assumptions, comments, top-down targets, etc.

The first of the above paragraphs comes largely from p. 410; the second, from p.

411. Either, a combination, or similar information expressed differently can be correct.

4.

What are some alternative designs for a BPM database?

Some alternative designs for a BPM database are transactional data stores, application data marts or a centralized data warehouse. The data marts or warehouse can be organized as a multidimensional or OLAP database.

5.

What are the major categories of BPM applications?

Budgeting, planning and forecasting Profitability modeling and optimization Scorecard applications Financial consolidation Statutory and financial reporting

6.

What are some of the types of BPM user interfaces?

Two types discussed in the text are a Web browser interface and a spreadsheet interface.

7.

What are the basic categories in Gartner’s magic quadrant? Who are some of the vendors in the various categories?

Gartner’s Magic Quadrant is a 2×2 grid. One axis covers Gartner’s assessment of execution: basically, the quality of a vendor’s products today. The other axis is their assessment of a vendor’s strategic vision. Both axes are divided into “strong” and “limited.” The four quadrants that result are labeled “Niche” (limited execution, limited vision); “Challengers” (strong execution, limited vision); “Visionaries” (limited execution, strong vision) and “Leaders” (strong execution, strong vision).

When Gartner publishes a quadrant, it positions vendors along a continuum on both axes. Two vendors may both have strategic vision in the “strong” half of the grid, but one may be portrayed as stronger than the other.

Vendors in each quadrant of the MQ for BPM suites include: • Leaders: Hyperion Solutions, Cognos

178

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• • •

Challengers: SAP (the only vendor listed here; it is not possible to list “some vendors” in this category) Visionaries: Oracle CPM, Business Objects, others (see Table 9.4) Niche: Oracle/PeopleSoft EPM, Lawson, others (see Table 9.4)

Gartner’s rankings change from year to year as products evolve, visions change, and movement in the Gartner analyst community brings new perspectives. In addition, vendors in all segments of IT spend considerable effort to influence Gartner’s magic quadrant rankings1; as they learn what leads to a good MQ position, they can improve theirs by simply presenting their products and vision in a way that Gartner finds more compelling. A high ranking one year does not guarantee one the next.

8.

What are some of the BPM applications that Hyperion Solutions supports?

Financial applications include financial modeling; Web-based financial planning, budgeting, and forecasting; headcount, salary, and compensation planning; and Web-based financial software for global collection reporting and analysis.

BI reporting applications include Interactive Reporting for user-directed query and analysis; Financial Reporting and Production Reporting for generating reports; Compliance Management Dashboard for combining internal control information and financial data to assess potential risk exposures; and Enterprise Metrics, a centralized metrics library and engine that reduces the time and expense of creating, measuring, and reporting KPI.

Section 9.10 Review Questions 1.

What are the major differences between a scorecard and a dashboard?

Table 9.5 identifies these five differences: Characteristic Purpose Users Updates Data Display

2.

Dashboard Measures performance Supervisors, specialists “Right-time” feeds Events Visual graphs, raw data

Scorecard Charts progress Executives, managers, staff Periodic snapshots Summaries Visual graphs, text comments

What is an operational dashboard? What is a tactical dashboard? What is a strategic dashboard?

All are dashboards—i.e., user interfaces intended to enable organizations to measure, monitor and manage business performance effectively. They can be distinguished as follows:

1

There are even consulting firms, staffed largely by ex-Gartner analysts, whose (highly profitable!) business consists almost entirely of advising vendors on how to accomplish this.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

179

An operational dashboard is used by frontline workers and supervisors to monitor key operational data that are lightly summarized and updated frequently throughout the day.

A tactical dashboard is used by managers and analysts to track detailed and summarized data generated from departmental processes and projects on a daily or weekly basis.

A strategic dashboard is used by executives, managers, and staff to monitor detailed and summarized data pertaining to the execution of strategic objectives on a monthly or quarterly basis.

3.

What type of dashboard—operational, tactical, or strategic—was the EMARS system?

Why was this type useful for physicians and administrators in an emergency room?

The Emergency Medicine and Analysis Report System (EMARS) was [is?] a tactical dashboard. This type of dashboard was suitable for emergency room physicians and administrators to monitor the health of the department itself, not the health of patients.

That, in turn, was because even the physicians at Emergency Medical Associates held what were essentially management, not patient care, positions. They weren’t actually in emergency rooms, they managed emergency rooms. Had they been in the ER treating patients, they might have needed something else.

4.

What criteria are important in selecting the particular display widgets to use with particular metrics on a dashboard?

The two most important criteria are that it needs to be the most appropriate one for the type of data being displayed, and that it needs to be functional even when reduced in size.

Note 1: Answering this question requires reading Online File W9.5.

Note 2: The use of the term “widget” in this context is slightly different from what students may be used to from Mac OS X, Windows Vista (which will be widely available by the time any course using this book reaches Chapter 9) or third-party packages such as Konfabulator.

5.

What are the characteristics of a well-designed dashboard?

•

• • • • •

It uses visual components (e.g., charts, performance bars, sparklines, gauges, meters, stoplights) to highlight, at a glance, the data and exceptions that require action. (Note: While this answer is taken from the main text, the term sparkline is defined in Online File W9.5.)

It is transparent to the user, meaning that they require minimal training and are extremely easy to use.

It combines data from a variety of systems into a single, summarized, unified view of the business.

It enables drill-down or drill-through to underlying data sources or reports, providing more detail about the underlying comparative and evaluative context.

It presents a dynamic, real-world view with timely data refreshes, enabling the end user to stay up-to-date with any recent changes in the business.

It requires little, if any, customized coding to implement, deploy, and maintain.

180

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Section 9.11 Review Questions 1.

Define BAM. BAM, or Business Activity Monitoring, can be thought of as “real-time BPM.” It consists of “real-time systems that alert managers to potential opportunities, impending problems, and threats, and then empower them to react through models and collaboration.” (p. 421)

2.

Describe the BAM system used by the city government of Albuquerque.

The BAM used by the city government of Albuquerque monitors events within the government and sends e-mails the next day to those affected. Vendors are notified of payments issued to them, residents are notified of water bills, appropriate employees are notified of online payments made to the city, and employees responsible for campaign financing law compliance are notified of candidate contribution report filings. All these are produced automatically by Cognos NoticeCast software.

3.

What are the key technical elements of a BAM system?

•

4.

ETL technology (Extract-Transform-Load, as students should remember from Chapter 5) Process modeling technology Rules engines Messaging servers E-mail in-boxes, portals, dashboards, and Web services

• • • • • What are some of the key issues that arise in implementing BAM? • • • • • • • • • • •

Executive failure to consider the readiness of technology for what they want to do Executive failure to consider the readiness of the business processes they want to monitor Lack of a senior management champion Failure to involve and train users properly Poor integration Difficulties in extracting data from multiple sources Difficulties in providing data to multiple users Inadequate change management Managers “stepping on the toes” of lower-level managers because of detailed, upto-date information which they did not previously have Not working closely enough with business units to identify CSFs and analytical techniques that can provide reliable alerts Failure to focus on a well-defined business problem with a measurable return

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

181

        

SAP uses the term strategic enterprise management (SEM), Cognos uses the term corporate performance management (CPM), and Hyperion uses the term business performance management (BPM). Are they referring to the same basic ideas? Provide evidence to support your answer.

Yes, they are. Evidence is in the definitions of the terms on p. 386 under the heading “BPM defined.” The three terms are defined in that paragraph. Reading the definitions shows that they describe similar concepts.

2.

BPM encompasses four basic processes: strategize, plan, monitor, act and adjust. Select one of these processes and discuss the types of software tools and applications that are available to support it.

(Note that the last comma in the first sentence as printed in the book is a typo. It changes the meaning, creating what appear to be five processes. “Act and adjust” are a single basic process. This was discovered only after the book had gone to press. If you are using this manual after early 2007, you may want to see if the typo is still in your students’ copies before you mention it, as it will probably be corrected in the second and subsequent printings. It is correct above.)

The section titled “BPM Applications” (p. 412ff) addresses this question. In it, Gartner is cited as saying that the majority of BPM processes at all levels can be handled by five types of applications: budgeting, planning and forecasting; profitability modeling and optimization; scorecard applications; financial consolidation; and statutory and financial reporting.

3.

Select a public company of interest. Using the company’s 2006 annual report, create five strategic financial objectives for 2007. For each objective, specify a strategic goal or target. The goals should be consistent with the company’s 2006 financial performance.

Student answers will vary depending on the company they chose. Goals for 2007 should reflect not only the financial performance of 2006, but also the firm’s industry and its overall growth rate (firms in faster-growing industries can set more aggressive revenue targets, but may have to accept lower profit margins) and its market share (it is easier for a company with a small market share to increase it than it is for a company that already dominates its segment).

4.

In recent years, the Beyond Budgeting Round Table (BBRT; bbrt.org) has called into question traditional budgeting practices. A number of articles on the Web discuss the BBRT’s position. In the BBRT’s view, what is wrong with today’s budgeting practices?

What does the BBRT recommend as a substitute?

The BBRT feels that ten things are wrong with budgeting (http://www.bbrt.org/bbprob.htm):

182

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Budgets are time-consuming and expensive They provide poor value to their users They fail to focus on shareholder value They are too rigid and prevent fast response They protect rather than reduce costs They stifle product and strategy innovation They focus on sales targets rather than customer satisfaction They are divorced from strategy They reinforce a dependency culture They lead to unethical behavior As a substitute, they recommend an adaptive model, where responsive processes replace the fixed budget; and decentralized management rather than the traditional fixed hierarchy. They feel that companies that adopt their principles will benefit from having faster response, innovative strategies, lower costs, and more loyal customers.

5.

Describe how a BSC fits the description of a diagnostic control system.

A diagnostic control system is defined (pp. 392–3) as an information system with which it is possible to (1) set a goal in advance, (2) measure outputs, (3) compute or calculate absolute or relative performance variances, and (4) use the variance information as feedback to alter inputs and/or processes to bring performance back in line with present goals and standards. A balanced scorecard system enables all of these and can therefore be used diagnostically.

6.

Distinguish performance management and performance measurement.

Performance measurement is finding out what happened. Performance management is based on performance measurement—the old management adage “you can’t manage what you can’t measure” may apply here—but goes beyond it, using its output to drive the overall enterprise performance.

7.

The Performance Prism established by Andrew Neely and others at Cranfield University provides an alternative performance measurement and management framework. Using materials from the Web, discuss the major tenets of the framework. Compare the framework to BSC and Six Sigma.

Three Web information sources on Performance Prism are: http://www.som.cranfield.ac.uk/som/research/centres/cbp/products/prism.asp http://www.valuebasedmanagement.net/methods_performance_prism.html http://en.wikipedia.org/wiki/Performance_Prism These are short summaries. The first two have links to other information sources. A Web search will turn up many more. (The first URL redirects from www.performanceprism.com, which may be easier to remember. The second and third contain underscore characters that may be hidden by the underlines. If you see a space, it’s a single underscore.)

Three fundamental premises underpin the Performance Prism concept. First, it is no longer acceptable (or even feasible) for organizations to focus just on the needs of one

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

183

or two of their stakeholders—typically shareholders and customers—if they wish to survive and prosper in the long term. Second, an organization’s strategies, processes, and capabilities have to be aligned and integrated with one another if the organization is to be best positioned to deliver real value to all of its crucial stakeholders. Third, organizations and their stakeholders need to recognize that their relationships are reciprocal – stakeholders have to contribute to organizations, as well as expect something from them; relationships are symbiotic and so there are interdependencies. (From the cited Wikipedia page, accessed November 21, 2006, with U.K. spelling changed to United States.)

The major difference between Performance Prism and the other frameworks is that PP begins with stakeholders, what they want/need and what they can offer, deriving everything else from that starting point. This perspective is implicit in the other frameworks. If the needs of a particular firm’s stakeholders are reasonably typical, the eventual strategies, plans and actions that will emerge from the PP methodology are not likely to differ significantly from those that would have emerged from BSC. If they are not typical, BPM may overlook this to the organization’s detriment.

Since Six Sigma operates at a lower level than BSC (see question 10 below) it is likely to yield lower-level answers than either of the others.

8.

Create a measure for some strategic objective of interest (you can use one of the objectives formulated in Discussion Question 3). For the selected measure, complete the measurement template found in Table W9.3.1 in the online file for this chapter.

Such an objective for a company entering a brand new market might be a market share increase from 20 to 30 percent. For that objective, a possible measurement template might look like this:

Measurement Name Purpose

Relates to

Metric/formula

Target level(s) Frequency Source of data Who measures

Who acts on the data (owner)

Property What is it called, and is it understandable?

Why is it being used, and what actions is it encouraging?

Explanation Market share increase; yes Used to measure improvement in our competitive position in our industry; encourages sales What strategies does it support, and what other Supports strategy of expanding measures is it linked to? market penetration to obtain new customers while market is still in early growth stages; linked to shipments What is the actual measurement?

Ratio of sales revenue to total market size using public industry data What is the desirable level of performance?

30 percent How often is the measurement made and reported? Monthly Where do the data come from?

Our data: accounting; industry data: IDC What are the name and function of the person John Doe, Business Analyst responsible for collecting, collating, and analyzing the data?

Who—name and function—is responsible for Jane Roe, VP of Sales ensuring the performance levels?

184

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Measurement What do they do

Property How will the owner use the data, and what actions can he or she take to improve performance?

Efficiency

Are all the data that are being collected used?

Are there easier ways to collect the data?

Is there any duplication among the measures?

Can the amount of data being collected be reduced through sampling?

Can the frequency of collection or reporting be reduced?

Effectiveness

9.

Can the measure be eliminated because it is no longer being used?

Are there new tools that can be used to produce the measure in new ways?

Are all key processes adequately measured?

Are proper process measures in place to keep performance within acceptable limits?

Is everyone getting the information they need?

Does everyone understand the measures so they can interpret the results?

Are users taking action as a result of the measures?

Can the measures be easily used?

Are the measures sufficiently timely and accurate?

Does everyone have access to the measures they need?

Is the relative impact of different measures clear to users?

Have the measures been changed to reflect changes in processes?

Explanation If the data do not show an increase in market share tending toward at least 30 percent, she will use this as evidence of a need to develop new sales programs to achieve it They will be No, as far as we know No No; it must be collected for other reasons in any case No; monthly collection frequency is required for other reasons No; it is being used No Yes Yes Yes Yes Yes Yes Yes Yes Yes N/A, processes have not changed

Create a strategy for some hypothetical company, using the four perspectives of the BSC. Express the strategy as a series of strategic objectives. Produce a strategy map depicting the linkages among the objectives.

The four BSC perspectives are financial, customer, internal business processes, and learning and growth. A possible strategy for a (very!) hypothetical start-up might be “to leverage our anti-gravity patents to become a leading supplier of helicopters.”

Financial objectives would include revenue and profitability targets, appropriate to the early stage the company is in. These would probably include targets for raising venture capital.

Customer objectives would include lining up trial users and obtaining letters of intent.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

185

Internal business process objectives would include targets for R&D and production, as well as the development of new products (elevators? forklifts? construction equipment? etc.) based on anti-gravity technology.

Learning and growth could involve developing the right culture for the new firm and continued research in anti-gravity.

The strategy map could look like this:

Financials

Growth, revenue targets

Raise capital

Test sites

Letters of intent

R&D, production targets

New products

Culture

Anti-gravity research

Customer

Internal

Learning

10.

Compare and contrast the DMAIC model with the closed-loop processes of BPM. The DMAIC model (the performance improvement model used in the Six Sigma methodology) and BPM are both closed-loop models. However, their purposes are different: BPM is for overall monitoring of an organization, while the DMAIC model is focused on improving performance of a process or area. Their emphasis and the resulting processes therefore differ. BPM has four processes (strategize, plan, monitor, act and adjust) while the DMAIC model has five (define, measure, analyze, improve, control).

Specific differences between them include: • BPM takes a more strategic approach. It begins with strategizing. DMAIC takes strategic objectives as given, and continues from there.

• The Plan process of BPM corresponds to the Define process of the DMAIC model.

• The DMAIC model, as fits its purpose, goes into more detail in the later (implementation) stages of the improvement process, which BPM lumps under “act and adjust.”

186

11.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Gartner’s BPM magic quadrant lists the major software vendors that provide BPM suites.

Compare and contrast two of the offerings (other than the Hyperion Solutions suite).

Open-ended answer, since there are so many possible combinations. (Some of the companies in the table may no longer exist, or may no longer be operating under the same names, when your students carry out this exercise. For example, ALG Software was purchased by Business Objects in late 2006. While it still uses its old name and can be found on the Web at www.algsoftware.com as this manual is being written, there is no guarantee that either situation will continue indefinitely.)

In answering this question your students will have to establish criteria for comparison. These can include product functionality/features, platform and compatibility considerations, and more.

They will also need to choose which products to compare, since most vendors in this list have more than one BPM offering. Ideally, they should find two that are directly comparable. For example, Cartesis offers a range of BPM products, some of which focus on cost and profitability analysis. ALG Software’s offerings, by contrast, are all about cost and profitability analysis. Comparing the two offerings overall will yield one set of answers. Comparing the Cartesis cost and profitability offering with ALG’s total product line will yield a different one.

12.

A number of companies provide both scorecard and dashboard products. Compare and contrast the scorecard and dashboard offerings from one company.

Conceptually, it is difficult to “compare and contrast” scorecard and dashboard offerings in the traditional sense, where one takes two items of the same or similar kinds and looks at which of their characteristics are similar, which differ. That is not possible here because scorecards and dashboards are fundamentally different. A scorecard is a methodology for measuring an organization’s performance. A dashboard is a means of presenting measurements from whatever source. Thus, a dashboard could be used to present a scorecard. The two concepts are complimentary, not competitive.

That said, the scorecard and dashboard offerings of a given vendor, if it has both, are usually designed to work with each other. For example: •

•

•

Information Builders Inc. (www.informationbuilders.com) is one of the vendors listed in Table 9.6 as having both dashboard and scorecard products. Its description of its scorecard product, the Performance Management Framework, says that it provides “role-based access [to the data] via Business Intelligence Dashboard.”

That shows the complementary relationship between the two products. idashboards (www.idashboards.com). Its home page shows four dashboards that were created with its software. One of them presents a balanced scorecard; the other three present different types of information, showing how a dashboard can be used to present any of them.

Obvient Strategies. At www.obvient.com/dashb.html, it shows eight dashboards created with its software. Two are for scorecards, the other six are not. Again, the message is that a dashboard can be used to present scorecard information or other information.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

•

13.

187

Views from Performancesoft (previously Panorama Business Views). This is a combined dashboard and scorecarding product, which provides both sets of features.

Using the criteria established in Table 9.1, compare BAM with traditional BI and BI for BPM. Based on this comparison, which type of BI is BAM closer to?

(The table number as printed in the book, 9.2, is a typo. The correct table is 9.1 on page 387. This was discovered only after the book had gone to press. If you are using this manual after early 2007, you may want to see if the error is still in your students’ copies before you mention it, as it will probably be corrected in the second and subsequent printings. It is correct above.)

The table uses thirteen factors to contrast traditional BI with BI for BPM. BAM is closer to BI for BPM in terms of these factors. Factors which convey this strongly include: • Focus: traditional BI is historical, BI for BPM and BAM are both timely • Users: traditional BI is for analysts, BI for BPM and BAM are both for everyone • Orientation: traditional BI is reactive, BI for BPM and BAM are both proactive • Measures: traditional BI uses metrics, BI for BPM and BAM both use key performance indicators • Visuals: traditional BI uses tables and charts, BI for BPM and BAM both use dashboards and scorecards • Interaction: traditional BI is pull-oriented (ad hoc queries), BI for BPM and BAM are both push-oriented (alerts) • Data: traditional BI is numeric only, BI for BPM and BAM are both numeric, text, etc.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

What is VIS? VIS, which stands for Vigilant Information System, is the name Western Digital gave its BAM system. It “integrates data and distills information and business intelligence from various sources to detect changes, initiate alerts, assist with diagnosing and analyzing problems, and supports communication for quick action.” (p. 428; quoted portion quoted in the text as well.)

2.

In what ways is the architecture of VIS similar to or different from the architecture of BPM? The elements of a BPM architecture are (see Review Question 2 of Section 9.9) the database tier, the application tier and the client (or user interface) tier.

The VIS architecture fits this description, but since it is a specific system rather than a general conceptual model its characteristics can be described more precisely. VIS is based on data from various transactional sources. The application tier consists of BI

188

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

applications which analyze the data to determine whether they meet certain pre-specified conditions. The client tier is the dashboard.

3.

What are the similarities and differences between the closed-loop processes of BPM and the processes in the OODA decision cycle?

BPM has four processes: strategize, plan, monitor, act and adjust. OODA also has four: observe, orient, decide, and act.

The OODA approach accepts the strategy and plan as given, whereas they are included in the BPM approach. OODA’s four processes therefore correspond, in total, to the last two processes of BPM. OODA breaks those two BPM processes down to a finer level of detail.

4.

Using Eckerson’s dashboard types, describe the types of dashboards provided in the WD system.

Eckerson describes three types of dashboards: operational, tactical, and strategic. Using these categories, the VIS factory dashboards would be operational; the corporate dashboards tactical.

5.

What are the basic benefits provided by WD’s VIS and dashboards?

Cost savings. These are broken down into three in the text (p. 429), with total savings of $3 million over an unspecified time period for one aspect, $900,000 per year for another, and $350,000 per year for a third.

Strategic advantages, derived from speeding “the delivery of data and information to executives and managers at all levels.”

6.

What sorts of advice can you provide to a company that is getting ready to create its own VIS and dashboards?

In principle, this is an open-ended question. However, students should generally base answers on lessons learned in WD’s experience with VIS (p. 429): • Design the real-time management dashboards to be the nerve center for managing the enterprise.

• Plan and schedule the coordination among teams to use the dashboards to manage enterprise-wide.

• Build a learning loop around each OODA loop to foster group learning because the faster the loop, the more important the learning reviews and the more frequent they need to be.

• Match the latency of each OODA loop to the organization’s needs and capabilities to become truly vigilant.

• Provide the building blocks for the “sense and respond” real-time enterprise through VIS and real-time management dashboards.

• Justify VIS on a strategic basis rather than a cost or return-on-investment basis.

• Make implementation of an enterprise-wide VIS a management initiative because it requires active collaborative engagement from top management to instill the needed organizational transformation.

CHAPTER

10

Collaborative Computer-Supported Technologies and Group Support Systems

Learning Objectives for Part IV ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the concepts and process of groupwork Describe how IT-based collaboration and communication support groupwork in general and decision making in particular Understand the fundamental principles and capabilities of groupware and group support systems (GSS) Describe the fundamental principles and capabilities of knowledge management (KM) Describe tools of KM and how they relate to decision support Relate KM, collaboration, and communication to each other

Learning Objectives for Chapter 10 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the basic concepts and processes of groupwork, communication, and collaboration Describe how computer systems facilitate communication and collaboration in an enterprise Explain the concepts and importance of the time/place framework Explain the underlying principles and capabilities of groupware, such as group support systems (GSS) Understand the concepts of process gain, process loss, task gain, and task loss and explain how GSS introduces, increases, or decreases each of them Describe indirect support for decision making, especially in synchronous environments Become familiar with the GSS products of the major vendors, including Lotus, Microsoft, WebEx, and Groove Understand the concept of GDSS and describe how to structure an electronic meeting in a decision room

189

190

⧫ ⧫ ⧫ ⧫ ⧫

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Describe the three settings of GDSS Describe specifically how a GDSS uses parallelism and anonymity and how they lead to process/task gains and losses Understand how the Web enables collaborative computing and group support of virtual meetings Describe the role of emerging technologies in supporting collaboration Define creativity and how it can be facilitated by computers

CHAPTER OVERVIEW It is a rare decision that can be made by one person without input, discussion or approval from anyone else. All other decisions, which is to say the overwhelming majority, involve some form of group activity or collaboration. While such decisions can be helped by the tools discussed in previous chapters—a data warehouse, for example, can provide useful information to one or a dozen decision makers equally well—they do not address the group aspect of the decision. This chapter covers tools that do.

CHAPTER OUTLINE 10.1

OPENING VIGNETTE: COLLABORATIVE DESIGN AT BOEINGROCKETDYNE A. PROBLEM B. SOLUTION 1.

Collaborative Technology Used C. RESULTS  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

10.2

MAKING DECISIONS IN GROUPS: CHARACTERISTICS, PROCESS, BENEFITS, AND DYSFUNCTIONS A. CHARACTERISTICS OF GROUPWORK B. THE GROUP DECISION-MAKING PROCESS C. THE BENEFITS AND LIMITATIONS OF GROUPWORK ⧫ Technology Insights 10.1: Benefits of Working in Groups and Dysfunctions of the Group Process D. IMPROVING THE MEETING PROCESS  Section 10.2 Review Questions

10.3

SUPPORTING GROUPWORK WITH COMPUTERIZED SYSTEMS A. AN OVERVIEW OF GROUP SUPPORT SYSTEMS (GSS) B. GROUPWARE ⧫ Application Case 10.2: How General Motors Is Collaborating Online C. TIME/PLACE FRAMEWORK

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

D.

191

WHAT COMPUTERS CAN AND CANNOT DO ⧫ Technology Insights 10.3: Unsupported Aspects of Communication  Section 10.3 Review Questions

10.4

TOOLS FOR INDIRECT SUPPORT OF DECISION MAKING A. GROUPWARE TOOLS 1.

Synchronous Versus Asynchronous Products 2.

Virtual Meeting Systems B. REAL-TIME COLLABORATION TOOLS 1.

Electronic Teleconferencing ⧫ Application Case 10.4: Videoconferencing Is Ready for Prime Time 2.

Interactive Whiteboards a.

Screen Sharing b.

Instant Video C. SUPPORT OF ASYNCHRONOUS COMMUNICATION 1.

Online Workspaces  Section 10.4 Review Questions

10.5

INTEGRATED GROUPWARE SUITES A. Lotus Notes/Domino and Lotusphere B. Microsoft Netmeeting and Live Meeting ⧫ Application Case 10.5: NetMeeting Provides a Real-Time Advantage C. GROOVE NETWORKS D. WEBEX MEETING CENTER AND PLACEWARE CONFERENCE CENTER E. GROUPSYSTEMS AND OTHER VENDORS F. CONCLUSIONS ABOUT GROUPWARE SUITES  Section 10.5 Review Questions

10.6

DIRECT COMPUTERIZED SUPPORT FOR DECISION MAKING: FROM GROUP DECISION SUPPORT SYSTEMS TO GROUP SUPPORT SYSTEMS A. GROUP DECISION SUPPORT SYSTEMS (GDSS) B. GROUP SUPPORT SYSTEMS ⧫ Technology Insights 10.6: Modeling in Group Decision Making: EC11 for Groups C. HOW GDSS (OR GSS) IMPOVES GROUPWORK D. FACILITIES FOR GDSS 1.

Decision Rooms ⧫ Application Case 10.7: Eastman Chemical Boosts Creative Processes and Saves $500,000 with Groupware 2.

Multiuse Facilities 3.

Internet/Intranet-Based Systems 4.

Which GSS Facility to Use?  Section 10.6 Review Questions

192

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

10.7

PRODUCTS AND TOOLS FOR GDSS/GSS AND SUCCESSFUL IMPLEMENTATION A. ORGANIZING A GSS SESSION ⧫ Technology Insights 10.8: The Standard GHSS Process B. GROUPSYSTEMS PRODUCTS: MEETINGROOM AND ONLINE C. GSS SUCCESS FACTORS 1.

Crafting a Collaborative Culture C. IMPLEMENTATION ISSUES FOR ONLINE COLLABORATION  Section 10.7 Review Questions

10.8

EMERGING COLLABORATION TOOLS: FROM VOIP TO WIKIS A. VOICE OVER IP ⧫ Technology Insights 10.9: VoIP Systems Help Increase Productivity and Enhance Learning Experiences at the State University of New York (SUNY) 1.

The Benefits of VoIP B. COLLABORATIVE WORKFLOW C. WIKIS 1.

Wikilog 2.

Commercial Aspects of Wikis and Their Derivatives D. COLLABORATION HUBS E. COLLABORATIVE NETWORKS F. CORPORATE (ENTERPRISE) PORTALS  Section 10.8 Review Questions

10.9

COLLABORATIVE EFFORTS IN DESIGN, PLANNING, AND PROJECT MANAGEMENT A. COLLABORATIVE DESIGN AND PRODUCT DEVELOPMENT B. COLLABORATIVE PLANNING ALONG THE SUPPLY CHAIN 1.

The CPFR Project 2.

Vendor-Managed Inventory C. PROJECT MANAGEMENT ⧫ Application Case 10.10: CPFR Initiatives at Ace Hardware and Sears  Section 10.9 Review Questions

10.10 CREATIVITY, IDEA GENERATION AND COMPUTERIZED SUPPORT A. CREATIVITY B. IDEA GENERATION THROUGH ELECTRONIC BRAINSTORMING C. CREATIVITY-ENHANCING SOFTWARE 1.

Computer Programs That Exhibit Creative Behavior 2.

Electronic Idea Generation for Problem Solving 3.

Software That Facilitates Human Creativity  Section 10.10 Review Questions Chapter Highlights

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

193

Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises ⧫ End of Chapter Application Case: Dresdner Kleinwort Wasserstein Uses Wiki for Collaboration  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

Most students will relate to this chapter because of the expanding use of online collaborative technology in the classroom. The challenge is to use that as a basis for developing an understanding of collaborative technology in other settings, such as business decision making and idea generation.

One way to reinforce this concept is to use your online system (e.g., Blackboard, WebCT) to set up group forums. Assign each team a problem; see how many ideas they can generate in 30 minutes without any other form of communication. They will probably find that they were more productive with the online tool than they would have been in person, even though generalized tools such as these are not focused on the specific problem you asked them to deal with. Imagine, you can tell them, how much more productive they could be if they had specialized ones.

The opening vignette discusses a common problem, not a one-time occurrence. Virtual teams are increasingly common. In-person meetings are often impractical, at least if one wants enough of them for a virtual team to function as a unit. The Rocketdyne solution is an example of using technology as the answer. You might generalize from this example by postulating a few other situations, then asking which of the tools and approaches used at Rocketdyne would help in those, which could be extended to help there or could have equivalents that would help there, and which were specific to the situation in the case. This thinking process will put students in a good position to put the content of this chapter, which includes a great deal of detailed information, in its broader perspective.

The rest of the chapter takes a top-down approach to its subject. It starts with general groupwork (sometimes seen as two words elsewhere, but written consistently as one word here to emphasize that it is a specific concept distinct from the general use of the phrase) concepts, moves to frameworks, then to computer support, and then to tools from the simple to the complex and emerging. Most students will relate to this approach, also.

Section 10.2 is about groupwork. Your students will surely have had experiences with group decision making on the job, in student organizations or even in their families. You can draw on these experiences to make the characteristics of group work and its processes, its plusses and its minuses more than academic concepts.

The remainder of the chapter presents no unusual teaching problems, but a great deal of material with considerable depth. It will take time for your students to learn it all. While I happen to think it’s all important, the fact is that the time constraints of a semester sometimes interfere with covering all we’d like to cover in any course. Here, after Section 10.2, if you must cut back,

194

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

I’d urge you to cover Sections 10.3, 10.4, and 10.6 as a minimum. Those are the more conceptual sections, ones which students must understand in order to have any grasp of GDSS/GSS. If you flesh these out with a few examples taken from the sections you skip, the others can be considered enrichment material which students can read on their own.

Section 10.5 contains an error on p. 448 that you should be aware of so you can correct it for your students (or, for those who would catch it, show that the publisher is aware of it too and will correct it in the second printing). Lotusphere is not conferencing software. It is the annual conference for users of IBM’s Lotus products, usually held in Orlando, Fla., in January.

In discussing wikis, in Section 10.8, it can be fun for students to go to the Wikipedia entry for their sport, hobby, home town, or other topic of interest, think of information they could add, and add it. Checking the entry for your school can be a good in-class demo. They’re sure to find something that’s wrong, incomplete, or just a year out of date. Change it, re-visit the page and see how the change was reflected instantly in its content. (This assumes you have a classroom computer connected to the Internet, but in 2007 that’s a safe bet in most universities.)

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 10.1 Review Questions 1.

Why was a group needed and why were the members in different locations?

Primarily because this project was only 15 percent of their work. The other 85 percent of team members’ work was in their regular work locations, which were in many different locations. Limiting team membership to people who were located in the same place would have meant unacceptable compromises in the quality of the team.

2.

Why did the project take so long? Why were so many meetings needed?

Project duration was longer than it might have been otherwise because team members devoted only 15 percent of their time to the project, they had varied backgrounds in many respects, and they had never worked closely together before.

The number of meetings was required because the engine being designed involved complex interactions among elements designed by different team members and analysis from different perspectives.

That said, “take so long” may not be entirely accurate. Compared to the six years similar projects typically took in the past, ten months is not “so long.” Similarly for “so many meetings:” 89 is not a large number when compared with the number of times colocated team members would be expected to get together, as a group or in smaller groups, over that period.

3.

What computer support was provided? What type of software was used?

Five types of software were used: • The “Internet Notebook,” in which sketches and other data could be created, accessed, commented on and more, with an electronic whiteboard that allowed multiple team members to access the same entry

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

195

• Project Vault, for secure storage and access for shared files • Teleconferencing • Videoconferencing • Desktop analysis software Computer support for these applications is not described in the vignette, but it is reasonable to assume that it consists of at least one professionally administered server and powerful desktop client systems that must run the analysis software as well as the collaboration tools and Web browsers.

4.

Comment on idea generation in this process.

Ideas were generated and evaluated through electronic brainstorming. Sharing understanding of problems facilitated creativity. The work environment and team spirit, while not discussed in the vignette, were surely major contributors to this creativity as well.

5.

Comment on sharing and collaboration in this process.

The group environment supported a culture of sharing and collaboration. Non-technical reasons for this probably include the members’ sense that they were hand-picked for a critical project, encouragement from their management to collaborate, and the project manager’s leadership. Given a willingness to collaborate for these reasons, it is then necessary to provide team members with the tools they need to do so. If they are fortunate enough to be in the same location, that is trivial, but in this case they were not. technical tools such as those discussed in this vignette are required as well.

Section 10.2 Review Questions 1.

Define groupwork.

Groupwork is “work done by two or more people together.” (p. 436)

2.

List five characteristics of groupwork.

The text (p. 436) gives the characteristics listed below. A correct answer can consist of any five: • A group performs a task (sometimes decision making, sometimes not).

• Group members may be located in different places.

• Group members may work at different times.

• Group members may work for the same organization or for different organizations.

• A group can be permanent or temporary.

• A group can be at one managerial level or can span several levels.

• There can be synergy (leading to process and task gains) or conflict in groupwork.

• There can be gains and/or losses in productivity from groupwork.

• The task may have to be accomplished very quickly.

• It may be impossible or too expensive for all the team members to meet in one place, especially when the group is called for emergency purposes.

196

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• • • •

3.

Some of the needed data, information, or knowledge may be located in many sources, some of which may be external to the organization.

The expertise of non-team members may be needed.

Groups perform many tasks; however, groups of managers and analysts frequently concentrate on decision making.

The decisions made by a group are easier to implement if supported by all (or at least most) group members.

• Describe the process of a group meeting for decision making.

The text (pp. 436–7) lists the items listed below as “activities and processes” that characterize meetings. One could argue that some of the items in the list are neither an activity nor a process, but more of a description, though that would be nit-picking. Other answers, that are more descriptive of the meeting process, can also be correct. If you have specific requirements in this regard, it might be well to state them.

• The decision situation is important, so it is advisable to make [that decision] in a group in a meeting.

• A meeting is a joint activity engaged in by a group of people typically of equal or nearly equal status.

• The outcome of a meeting depends partly on the knowledge, opinions, and judgments of its participants and the support they give to the outcome.

• The outcome of a meeting depends on the composition of the group and on the decision-making process the group uses.

• Differences in opinions are settled either by the ranking person present or, often, through negotiation or arbitration.

• The members of a group can be in one place, meeting face-to-face, or they can be a virtual team, in which case they are in different places while in a meeting.

• The process of group decision making can create benefits as well as dysfunctions.

4.

Describe five potential gains of group meetings.

The text (pp. 437–8) gives the ones listed below. A correct answer can consist of any five, or of others students may be able to find in other sources. (As a technicality, one could argue that the list in the text is for groupwork in general, which is subtly different from group meetings, but as a practical matter this list is what the question refers to.)

• It provides learning. Groups are better than individuals at understanding problems.

• People readily take ownership of problems and their solutions. They take responsibility.

• Group members have their egos embedded in the decision, so they are committed to the solution.

• Groups are better than individuals at catching errors.

• A group has more information (i.e., knowledge) than any one member. Group members can combine their knowledge to create new knowledge. More and more creative alternatives for problem solving can be generated, and better solutions can be derived (e.g., through stimulation).

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• • • •

5.

197

A group may produce synergy during problem solving. The effectiveness and/or quality of groupwork can be greater than the sum of what is produced by independent individuals.

Working in a group may stimulate the creativity of the participants and the process.

A group may have better and more precise communication working together.

Risk propensity is balanced. Groups moderate high-risk takers and encourage conservatives.

Describe five potential losses of group meetings.

The text (pp. 437–8) gives the ones listed below. A correct answer can consist of any five. (Also see note at the beginning of the previous answer.)

• Social pressures of conformity may result in groupthink (i.e., people begin to think alike and do not tolerate new ideas; they yield to conformance pressure).

• It is a time-consuming, slow process (i.e., only one member can speak at a time).

• There can be lack of coordination of the meeting and poor meeting planning.

• Inappropriate influences (e.g., domination of time, topic, or opinion by one or few individuals; fear of contributing because of the possibility of flaming).

• There can be a tendency for group members to either dominate the agenda or rely on others to do most of the work (free-riding).

• Some members may be afraid to speak up.

• There can be a tendency to produce compromised solutions of poor quality.

• There is often nonproductive time (e.g., socializing, preparing, waiting for latecomers—air-time fragmentation).

• There can be a tendency to repeat what was already said (because of failure to remember or process).

• There is a high cost of meeting (e.g., travel, participation).

• There can be incomplete or inappropriate use of information.

• There can be too much information (i.e., information overload).

• There can be incomplete or incorrect task analysis.

• There can be inappropriate or incomplete representation in the group.

• There can be attention blocking.

• There can be concentration blocking.

Section 10.3 Review Questions 1.

Why do we use computers to support groupwork?

Because it can help a group follow a process, save cost, expedite decisions, support virtual teams, improve access to external experts, and improve the decision-making process overall.

198

2.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Define groupware.

Groupware are“computerized tools [that] provide group support” (p. 439). One could add that groupware provides a mechanism for group members to share opinions, data, information, knowledge, and other resources (p. 440).

3.

Describe the components of the time/place framework.

The two components of the time/place framework are time and place. Either can be the same or different. The four combinations that result (same time/place, same time/different place, different time/same place, different time/place) define the framework.

4.

What can computers not do in terms of supporting groupwork?

They are not good at communicating non-verbal information such as body language, which may convey 50 percent or more of what we mean. This is improving via workaround such as “smilies” (more formally emoticons), increased use of video, and new technologies. They also do not do well at communicating language subtleties and crosscultural concepts.

Section 10.4 Review Questions 1.

List the major groupware tools and divide them into synchronous and asynchronous types.

Synchronous (real-time) groupware tools include all the items discussed in the answers to Questions 2–4: audio teleconferencing, videoconferencing, data conferencing, Web conferencing, whiteboards, screen sharing and instant video.

Asynchronous groupware tools include blogs, wikis, discussion groups, autoresponders, workflow software, interactive portals, and online workspaces. Other asynchronous communication tools, such as e-mail, can be useful to groupwork but are not true “groupware.”

2.

Describe the various types of electronic teleconferencing, including Web-based conferencing.

There are four types of electronic teleconferencing: Audio teleconferencing, where multiple people in multiple locations talk to each other.

This term is sometimes used synonymously with telephone conference call. (Some people make the distinction that a telephone conference call must involve at least three locations from the telephone carrier’s point of view, whereas an audio teleconference can use equipment such as speakerphones and microphones to connect multiple people in conference rooms in two locations.)

Video teleconferencing (videoconferencing), where data, pictures, graphics, and animation can be shared along with voice.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

199

Data conferencing, where groups can work on documents and exchange computer files during a teleconference.

Web conferencing, where multiple people can view the same thing on their computer screens while interacting via audio or messaging.

3.

Describe whiteboards and screen sharing.

Whiteboards are a type of groupware. Computer-based whiteboards work like real-world whiteboards with markers and erasers, except for one big difference: Instead of one person standing in front of a meeting room drawing on the whiteboard, all participants can join in.

Screen sharing software lets group members work on the same document, shown on each participant’s screen. For example, two people can collaborate on one manuscript or spreadsheet. Changes can be made by using the keyboard or by touching the screen.

4.

Describe instant video.

Instant video is linking people via both voice and audio, using video cameras and audio conferencing.

This technology is evolving extremely rapidly, especially among people of college age and younger. The wait-and-see attitude of the book (“This idea is still in its early stages,” etc.) is already dated. Many of your students are surely using this technology regularly in their personal lives. Some laptops priced under US $1,100 as of November 2006 include built-in cameras for this purpose as standard equipment. Today’s students will expect it on the job as a matter of course.

5.

Describe the online workspace.

An extension of screen sharing that “allow[s] people to share documents, files, project plans, calendars, and so on in the same online place, though not necessarily at the same time” (p. 447).

Section 10.5 Review Questions 1.

Define integrated collaboration suite.

A software suite is a set of programs designed to work together. Microsoft Office is a familiar example.

The term “integrated” implies that the programs in the suite were designed from the start to work with each other, rather than being assembled for marketing purposes with little true integration, but this term has no precise definition.

“Collaboration” refers to the suite’s function, in this case to support collaborative groupwork.

The concept as a whole, then, is an integrated set of programs that work together to support different aspects of groupwork.

200

2.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Describe Lotus/Domino and its major capabilities.

Lotus Notes and Domino provide collaborative document management capabilities enhanced with a wide range of features for editing, discussing, routing, and generally working with those documents, all in one environment with a graphical user interface.

3.

Describe Microsoft’s collaboration products.

Microsoft’s two major collaboration products are NetMeeting and Live Meeting.

NetMeeting is a real-time collaboration package that includes whiteboarding (i.e., support of relatively free-form graphics to which all participants can contribute simultaneously), application sharing (of any Microsoft Windows application document), remote desktop sharing, file transfer, text chat, data conferencing, and desktop audio- and videoconferencing. (A NetMeeting client is included in the Windows operating system.)

Live Meeting is a hosted Web conferencing tool.

4.

What is unique about Groove?

Its major unique feature is its peer-to-peer architecture, which eliminates the need for professionally managed servers.

Students may find other things they consider unique about Groove. One possibility, though it is not the intent of this question, is that it was developed by the same person (Ray Ozzie) who designed Lotus Notes. After Microsoft purchased Groove Networks, Ozzie rapidly took on significant executive responsibilities at his new, and much larger, firm.

5.

Describe the process of renting a place for a virtual meeting, using companies such as WebEx. One simply signs up for the service. It is not necessary to rent a physical place for the purpose.

Section 10.6 Review Questions 1.

Define GDSS and list the limitations of the initial GDSS software.

A GDSS is “an interactive computer-based system that facilitates the solution of semistructured and unstructured problems by a group of decision makers.” Its goal is “to improve the productivity of decision-making meetings, either by speeding up the decision-making process, by improving the quality of the resulting decisions, or both” (p.

452).

The initial GDSS software had these three major limitations: 1.

Its use was limited to face-to-face meetings.

2.

Its use depended on a special “decision room” facility. This was expensive.

3.

It was designed to support a single, clearly defined, narrow task.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

2.

201

Define GSS and list its benefits.

A group support system (GSS) is “any combination of hardware and software that enhances groupwork either in direct or indirect support of decision making. GSS is a generic term that includes all forms of collaborative computing” (p. 453).

GSS benefits are improving the productivity and effectiveness of meetings by streamlining and speeding up the decision-making process (efficiency) and/or improving the quality of the decision (effectiveness). This is achieved by providing support for group members in generating and exchanging ideas, opinions and preferences, using specific features such as parallelism and anonymity.

The book provides a list of twelve GSS support activities on pp. 454–455.

Students may be attracted to this list to answer this question. This would be incorrect, since activities are not benefits.

3.

Define EMS.

4.

An electronic meeting system (EMS) is a form of GSS that supports anytime/anyplace meetings. While there was once a clear distinction between the two concepts, today they are synonymous since all GSS support such meetings as well as desktop videoconferencing (which once served to distinguish EMS).

List process gain improvements made by GSS. The text lists the following improvements on pages 454–5: • GDSS supports parallel processing of information and idea generation (parallelism).

• GDSS enables the participation of larger groups with more complete information, knowledge, and skills.

• GDSS permits the group to use structured or unstructured techniques and methods.

• GDSS offers rapid, easy access to external information.

• GDSS allows parallel computer discussions.

• GDSS helps participants frame the big picture.

• Anonymity allows shy people to contribute to the meeting (i.e., get up and do what needs to be done).

• Anonymity helps prevent aggressive individuals from driving a meeting.

• GDSS provides for multiple ways to participate in instant, anonymous voting.

• GDSS provides structure for the planning process to keep the group on track.

• GDSS enables several users to interact simultaneously (i.e., conferencing).

• GDSS records all information presented at a meeting (i.e., organizational memory).

5.

Define decision room.

A decision room is a customized, special-purpose (and usually expensive) facility designed for use with GDSS. Decision rooms are equipped with computers at every seat, a seat in the front of the room and a server for the facilitator, and a large screen in front of the room as well. The individual computers are connected to the central computer via a LAN. The size of a decision room is typically from 12 to 30 seats.

202

6.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Describe a GSS multiuse facility.

A GSS multi-use facility is a room that has the necessary equipment for GSS (see previous answer) but can also be used for other activities as well when it is not serving its primary purpose. Such rooms are especially well suited for use as computer labs or classrooms because they have the necessary facilities for those purposes as well. It may be necessary to add software that the GSS application wouldn’t need, but this cost is minor compared to the value of the increased utilization.

7.

Describe Web-based GSS. Web-based or intranet-based groupware allows group members to work from any location at any time. This groupware often includes audio- and videoconferencing. The availability of relatively inexpensive groupware, combined with the power and low cost of capable PCs and the ubiquity of the Internet, makes this type of system very attractive.

8.

Why is the third option for a GDSS facility the most popular?

(The three GDSS facility options are a decision room, a multi-use facility or an Internet/intranet-based system.) The third is most popular for two reasons: 1.

It does not require specialized physical facilities, using instead commonly available and/or inexpensive software running on standard desktop computers.

While some GDSS capabilities may require upgrading these computers and/or purchasing additional equipment such as video cameras, the cost is still low compared to the other options.

2.

Participants can work from wherever they are rather than gathering in a common location.

Section 10.7 Review Questions 1.

List the steps of organizing a GDSS session.

Step 1: Meet with the facilitator to plan the meeting, select the software tools, and develop an agenda Step 2: Participants meet in the decision room (or other facility), where the leader poses the problem Step 3: Participants brainstorm by typing ideas and comments on other ideas; results are displayed Step 4: Facilitator organizes the ideas into categories; results are displayed Step 5: Leader starts a verbal or electronic discussion of the categorized ideas; participants prioritize Step 6: Top five or ten topics are sent on following discussion Step 7: Next steps—voting, repeating the process, etc.—vary with the situation and requirements

2.

List GroupSystems’ major products.

GroupSystems’ major products are MeetingRoom and OnLine.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

203

(If you want more from your students than just the list asked for in the question, you may want to be specific as to your requirements for a correct answer.)

3.

List some success factors of GDSS/GSS. These can be organized into two groups: factors that influence the success of any information system and factors that are unique to GDSS/GSS (or are at least not general to all IS).

Factors that affect the success of any information system, which you students may have studied in other courses, include organizational commitment, an executive sponsor or “champion,” an operating sponsor, user involvement and training, a user-friendly (the text uses “user-seductive” here) interface, and so on.

Factors that are unique to GDSS/GSS include a collaborative organizational culture, a well-trained and personable facilitator, having the right tools for the organization’s groupwork, supporting parallelism and anonymity, good planning, and demonstrable cost savings.

4.

How can a company create a collaborative culture?

Through the three steps outlined and described further on p. 461: • Know what you want, and get the team to agree on that • Determine resource constraints: the situation within which participants must collaborate • Determine what technologies can be used to overcome these constraints

5.

List three implementation issues of GDSS/GSS. • • • •

The need to connect to business partners The need to connect collaborative tools to file and database management software Automatic language translation, where a team does not have a common language The need for a standard protocol to integrate new applications and for communication

Section 10.8 Review Questions 1.

Describe VoIP and its advantages.

VoIP, or Voice over Internet Protocol, uses the Internet to transmit digitized sound waves representing a voice conversation. It can use the microphones and speakers that are built in to many computers (though that presents the possibility of annoying feedback from speaker to microphone) or specialized headsets or handsets that plug into a computer’s audio in and out ports. Some commercial firms use VoIP to provide inexpensive telephony services to consumers and businesses, but other applications of the technology simply let users of two computers talk to each other.

The benefit of VoIP over regular telephone conversations is that, since it uses the free Internet, it is more economical than a telephone for many types of calls. It is also convenient when users are already at their computers and online. Computer software can

204

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

provide call management features that surpass those of most telephone carriers. The text lists these benefits, originally from a Siemens Communications ad, on p. 463: For businesses: • • • • • • •

Allows CIOs to explore different deployment options for company’s communications needs Lowers total cost of ownership through voice/data convergence Lowers operational costs through use of integrated applications Reduces hardware requirements on the server side for certain applications (e.g., VoIP) Provides a holistic approach to security, enhanced by encryption and identity management Helps streamline workflows by empowering companies to communicationsenable different business processes Enables optimized conferencing tools to replace business travel

For users: • • • •

2.

Eliminates unwanted interruptions and unproductive actions by intelligently filtering communications Provides access to real-time presence information, which helps decisions get made faster Initiates ad hoc conferencing/collaboration sessions without the need to prearrange separate audio- or videoconferencing bridges Enables participation in conferencing sessions quickly and easily via a variety of mobile devices

Define collaborative workflow.

Collaborative workflow refers to software products that address project-oriented and collaborative processes. They are administered centrally yet are capable of being accessed and used by workers from different departments and even from different physical locations. The goal of these tools is to empower knowledge workers by allowing workers to communicate, negotiate, and collaborate within an integrated environment.

3.

Define wiki and wikilog.

A wiki is a Web site in which users can freely create and edit content, using any Web browser, via special server-based software. A wiki supports hyperlinks and has a simple text syntax for creating new pages and cross-links between internal pages on-the-fly. It is especially suited for collaborative writing. (The word “wiki” means “quick” in Hawaiian.)

A wikilog (or wikiblog, also known as a bliki) is essentially a blog that allows everyone to participate as a peer (a combination of wiki and blog). Anyone may add, delete, or change content. It is like a loose-leaf notebook with a pencil and eraser left in a public place. Anyone can read it, scrawl notes, tear out a page, and so on. Creating a

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

205

wikilog is a collaborative process. Information in it can be changed or deleted by anyone (though many wikis preserve previous copies of posted contributions in the background).

Articles added to a wiki are at the editorial mercy of its other participants.

4.

Define collaborative hub.

A collaboration hub is used by members of a B2B supply chain to improve effectiveness among manufacturing companies, their suppliers, and contract producers by reducing inventory, improving flexibility, and increasing supply-chain transparency through the Internet.

5.

Define corporate (enterprise) portal.

A corporate (enterprise) portal is a gateway to a corporate Web site that enables communication, collaboration, and access to company information. It is a personalized, single point of access via a Web browser to business information located inside and outside an organization. In contrast with commercial portals such as Yahoo! and MSN, corporate portals provide a single point of access to information and applications available in a specific organization.

Section 10.9 Review Questions 1.

Define CPFR and describe its process.

CFPR stands for Collaborative Planning, Forecasting, and Replenishment. This is an industry-wide project in which suppliers and retailers, often driven by one major player in that industry, agree to collaborate in planning and demand forecasting. The purpose is to ensure that members of a supply chain will have the raw materials and finished goods they need when they need them.

2.

Define VMI. VMI (vendor-managed inventory) allows retailers to make suppliers responsible for determining when to order and how much to order. The retailer provides the supplier with real-time information (e.g., point-of-sale data), inventory levels, and a threshold below which orders are to be replenished. The supplier ships what the retailer needs, when the retailer needs it.

3.

Describe the benefits of collaboration to project management.

Any project too large to be completed by one person requires collaboration of some type.

Any tools that improve collaboration, such as those described in this chapter, will help the project. The specific benefits will depend on the nature of the project and of the tool.

The text provides one example on p. 468; another is in Online File W10.5.

206

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Section 10.10 Review Questions 1.

Define creativity.

“The human trait that leads to the production of acts, items, and instances of novelty” (p.

469) “The use of the imagination or original ideas, esp. in the production of an artistic work” (Oxford American Dictionary) “A mental process involving the generation of new ideas or concepts, or new associations between existing ideas and concepts” (Wikipedia) “The quality or ability to create or invent something; originality” (Wiktionary) Students may find other, equally correct, definitions.

2.

Relate creativity to collaboration and problem solving.

Creativity relates to problem solving because the second stage of problem solving involves generating alternatives. Creativity generates of more, often better, alternatives to choose from.

The connection between collaboration and creativity is in the other direction.

Creativity does not enhance collaboration; collaboration enhances creativity. When two or more people try to generate ideas together, they often stimulate each other to generate ideas that they would never have generated working individually for the same amount of time.

3.

List software categories of creativity enhancement.

Other than electronic brainstorming, which helps human beings produce creative results, there are two major software categories for creativity enhancement. One category includes software that exhibits creative behavior on its own (see next question). The other is software that facilitates human creativity, often by overcoming factors that block it.

4.

Describe software programs that exhibit intelligent behavior.

There is a definitional issue here: does “exhibit intelligent behavior” mean that the behavior must be intelligent, whatever that means, or that it must be a behavior which, if we saw it in biological creatures, we would consider intelligent? The first option raises too many philosophical issues to attempt an answer here. We will therefore assume the second.

The field of artificial intelligence is devoted to applications whose behavior, if were we to see it in a person, would be considered “intelligent.” Software in this field includes expert systems, natural language interpretation, speech recognition (not the same thing), robotics (beyond pre-programmed mechanical actuators) and more. One example is the DARPA challenge to designers of automated cars. The problem of driving across a desert, avoiding natural obstacles en route to a stated objective, was solved in 2006; they have moved on to driving in traffic.

The text does not describe AI in this way. Instead, it notes that intelligence is often characterized by creativity. It then goes on to describe computer programs that exhibit that. If you prefer that interpretation of this question, you might want to make that clear to your students. In that case, the answer is in the third paragraph of p. 471.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

207

         

How does groupware attain its primary objective?

It obtains its primary objective by supporting groups doing their work, ideally in a comprehensive way.

2.

What is nonverbal communication? Explain why it is important in human-to-human interaction. What methods are currently being used to incorporate nonverbal communication into collaborative computing?

Nonverbal communication includes body positioning and actions, voice inflections, eye motion, and so on. It is important because over 50 percent (some social scientists say 70 percent) of human communication occurs through nonverbal means. Nonverbal communication is enabled in collaborative computing through such features as Web cams and streaming video.

3.

Explain why it is useful to describe groupwork in terms of the time/place framework.

Because the type of information system needed to support the groupwork depends on where the work is in terms of this framework.

4.

Describe the kinds of support that groupware can provide to decision makers.

The support can be divided into three categories: General (can be either synchronous or asynchronous) • Built-in e-mail, messaging system • Browser interface • Joint Web-page creation • Sharing of active hyperlinks • File sharing (graphics, video, audio, or other) • Built-in search functions (by topic or keyword) • Workflow tools • Use of corporate portals for communication, collaboration, and search • Shared screens • Electronic decision rooms • Peer-to-peer networks Synchronous (same-time) • Instant messaging (IM) • Videoconferencing, multimedia conferencing • Audio conferencing • Shared whiteboard, smart whiteboard • Instant video • Brainstorming

208

5.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• Polling (voting), and other decision support (consensus builder, scheduler) Asynchronous (different times) • Workspaces • Threaded discussions • Users can receive/send e-mail, SMS • Users can receive activity notification alerts, via e-mail or SMS • Users can collapse/expand discussion threads • Users can sort messages (by date, author, or read/unread) • Auto responder • Chat session logs • Bulletin boards, discussion groups • Use of blogs, wikis, and wikilogs • Collaborative planning and/or design tools • Use of bulletin boards • Explain why most groupware is deployed today over the Web. See answer to Review Question 8 for Section 10.6.

6.

Compare GDSS and noncomputerized group decision making.

GDSS provide many benefits. One potential problem, that a group might feel less cohesive due to the technology, was proven false at least in one experiment using four meetings over a four-week time period. GDSS let people work faster and tends to get better results.

7.

Explain why meetings can be so inefficient. Given this, explain how effective meetings can be run.

Almost all of the reasons involve people. Some don't take meetings seriously. Other issues are that a large amount (perhaps as high as 90 percent) of the information discussed at a meeting is either not remembered or remembered incorrectly. (This might make you wonder why we teach classes in an essentially meeting manner!) This could become an open-ended answer if you let the students describe their meeting experiences.

How can effective meetings be run? Some items that might be included are: have an agenda, keep to the subject, record the minutes of each meeting, send out a meeting notice, and to ask meeting participants to summarize results.

8.

Discuss the details of process gains (i.e., benefits) of groupwork.

See answer to Review Question 4 of Section 10.2. The qualification at the beginning of that answer does not apply to this question, which is phrased slightly differently.

9.

Discuss the details of process losses (i.e., dysfunctions) of groupwork.

See answer to Review Question 5 of Section 10.2. The qualification at the beginning of the answer to Review Question 4 does not apply to this question, which is phrased slightly differently.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

10.

209

Explain how GDSS can increase some of the benefits of collaboration and decision making in groups and eliminate or reduce some of the losses.

GDSS provides support to reduce the negative effects of group decision making and increase the positive effects. For example, anonymity means more equal participation, whereas parallelism saves time. By moving quickly, people get the urge to contribute.

There might be less free-riding. There are many more benefits.

11.

The original term for group support system (GSS) was group decision support system (GDSS).Why was the word decision dropped? Does this make sense? Why or why not?

Because GSS can help even when there is no decision to be made. For instance, it can help group members communicate. Many meetings are not held to make a formal decision. Researchers noticed this and changed the name of the technology to reflect reality.

12.

Discuss how parallelism and anonymity can produce improvements in group processes.

Parallelism allows everyone at a meeting to contribute simultaneously. This can substantially cut down on idea generation time. For example, 10 people contributing for 10 minutes each in a traditional meeting would take 100 minutes. In a GSS, parallelism cuts this down to 10 minutes. (Total meeting time is not reduced in the same ratio, since the time it takes the group to evaluate the ideas that were generated remains the same, but time savings are still substantial.)

Anonymity allows shy people to contribute. It also levels the playing field. Rank in an organization has no effect. Also, assertive (“pushy”) people do not take over the meeting as much.

13.

Describe the three technologies through which GSS is deployed. What are the advantages and disadvantages of each?

The three options for deploying GSS technology are: (1) a special-purpose decision room; (2) a multiple-use facility; and (3) Web-based groupware running wherever the group members are. Their descriptions are given in the answers to Review Questions 5, 6, and 7 of Section 10.6. Advantages and disadvantages include: Special-purpose decision room Advantages: meets the need for face-to-face communication at meetings; can double as videoconference room; provides a pleasant environment; secure because software runs locally on a LAN. Disadvantages: expensive to construct and use; limited to same time/same place meetings.

Multiple-use facility Advantages: provides many of the decision room benefits and doubles as a computer lab or for a similar use.

Disadvantages: again, limited to same time/same place meetings.

210

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Web-based groupware Advantages: provides group support for anytime/anyplace meetings; can be used in the office, at home, or on the road; relatively inexpensive to develop and use; anonymity maximized, which might be especially desirable in a distance learning environment.

Disadvantages: there are problems with facilitation and the isolation of participants (people like to know with whom they are working), a facilitator is still needed; new methods must be developed for managing meetings.

14.

Explain in detail what creativity is and how it relates to decision support.

Creativity is a complex concept to define formally, but very easy to know when you see it. Formally, creativity may be defined either as a trait or achievement. As a trait, creativity is a dispositional variable characteristic of a person, leading to the production of acts, items, and instances of novelty. As an achievement, we refer to the creative product, e.g., the output of a process, like the “quality” of the ideas generated in an electronic brainstorming session.

Creativity relates to decision support because the second stage of decision making involves generating alternatives. While these are in some cases obvious (a factory is to produce a certain number of product A and product B, the decision is the number of each) in other cases they are less so. Creativity enables the generation of more, and often better, alternatives to choose from.

(This question resembles, but asks for more depth than, Review Question 1 of Section 10.8.)

15.

Explain how GSS can support creativity.

The best way (so far) is through idea generation support, either by directly using a GSS with others, or with a random idea generator, or with an intelligent agent acting as a facilitator.

16.

Explain how idea generation (i.e., electronic brainstorming) works.

Electronic brainstorming gathers ideas and comments in an unstructured manner. Groups work rapidly in generating a free flow of ideas. Participants contribute simultaneously (parallelism) and anonymously. The brainstorming software can assist them by presenting a random selection of other participants’ ideas, by presenting random words to encourage free association, by offering suggestions on ways in which previous ideas could be modified, and more.

17.

Can computers be creative? Why or why not? Discuss.

This is an open-ended, philosophical question. Computers running certain AI programs can produce creative output in the sense that the developers of those programs could not have foreseen. In that sense, yes, they can be creative. (Whether or not their creativity involves anything we might call “thinking” is a separate—and even more philosophical— question, which this IM will not attempt to answer.)

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

18.

211

Discuss the benefits of CPFR to retailers and suppliers.

For the retailer, collaborative forecasting means fewer out-of-stocks and resultant lost sales and less stored inventory. For the manufacturer (supplier), collaborative forecasting means fewer expedited shipments, optimal inventory levels, and optimally sized production runs.

19.

Discuss the improvements to supply-chain management that result from using CPFR and VMI. These and similar approaches, under the overall term “collaborative planning,” can synchronize production and distribution plans and product flows, optimize resource utilization over an expanded capacity base, increase customer responsiveness, and reduce inventories.

20.

Explain the potential benefits of wikis to a director of marketing.

This is an open-ended question, as there are many possible benefits.

One benefit is in the area of developing a marketing plan. Through use of a wiki, people can draft sections within their own specialties, comment on and perhaps alter other sections, contribute according to their own skills (a copywriter might come up with creative slogans, a copy editor might fix their grammar) and more. The net result is a better plan, arrived at more quickly, compared to what could be achieved without the wiki.

Other areas where the marketing function could use a wiki effectively include all areas where collaborative writing is called for: marketing communications, competitive analysis and more.

21.

Discuss the benefits of VoIP as a facilitator of communication.

See Review Question 1 in Section 10.8.

22.

Discuss the benefits of collaborative design.

The design of anything more complex than a doghouse usually requires the skills of more than one person; in other words (or word), collaboration. Collaborative design allows each person to contribute to part of the project and for that person’s contributions to be integrated with the others to create the overall design.

While collaborative design as a concept is ancient, and its benefits have been understood for centuries, the use of information technology to facilitate it is new. For example, the wing box for the Boeing 787 was designed in Japan while the fuselage was designed in the United States. Collaborative design tools made this practical without an inordinate (and inordinately expensive) number of physical meetings between the two design groups.

23.

Discuss the benefits of CPFR and relate it to decision support.

Benefits of CPFR were discussed in the answer to Question 18 above.

CPFR relates to decision support because the ordering and replenishment decisions involved were previously made individually by people. With the CPFR

212

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

approach, people make policy decisions instead. Information systems then follow those policies in making programmed operational decisions.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

What capabilities of a wiki are not available in e-mail?

A wiki works on a central document that is available to all its users. Since e-mail messages are sent individually from one user to one or more others, no central document exists. Among other things, the sharing of ideas is therefore much simpler in a wiki than it would be via e-mail.

2.

Describe the applications of wikis in finance and operations.

One part of the finance function at Dresdner Kleinwort Wasserstein (DrKW) used a wiki to eliminate the cumbersome number of e-mails, to view the development of business plans, and to store commonly used information. The team also created an open forum where anyone can post views, comments, and questions on given subjects, publish and share white papers and bulletins, coordinate sales and marketing activities, and organize important team tasks.

In operations, a group that develops back-end applications used a wiki to share and develop new system specifications and product overviews and to help with documentation. It provided an instantly editable collaboration platform that simplifies the publication process. The version history function was also useful for product specs, where it is important to retain a complete audit trail.

(Since the question does not restrict answers to the DrKW examples mentioned in the text, students may find other correct answers in other sources or in their own personal experiences.)

3.

How does DrKW’s wiki increase employee productivity?

One way (see answer to Question 1 above) is by eliminating the extra work involved in editing a common document by sending it around, having people write up comments, having other people make changes to reflect those comments, and repeating the process until everyone is satisfied or time runs out.

The case gives, or implies, several others.

4.

How does DrKW’s wiki help with foreign languages and training?

The text is not specific on the question of foreign languages, saying only that the wiki “simplifies the roles of people writing in other … languages.” We can, however, infer several ways in which it can help. One of these is by providing a means through which commonly misunderstood terms can be clarified by anyone who sees a need and is able to meet it.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

213

It helps with training by reducing training time through helping trainees understand internal and external jargon more quickly and easily than they could without it.

CHAPTER

11

Knowledge Management

Learning Objectives for Chapter 11 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Define knowledge and describe the different types of knowledge Describe the characteristics of knowledge management Describe organizational learning and its relationship to knowledge management Describe the knowledge management cycle Describe the technologies that can be used in a knowledge management system (KMS) Describe different approaches to knowledge management Describe the activities of the chief knowledge officer and others involved in knowledge management Describe the role of knowledge management in organizational activities Describe ways of evaluating intellectual capital in an organization Describe how KMS are implemented Describe the roles of technology, people, and management in knowledge management Describe the benefits and drawbacks of knowledge management initiatives Describe how knowledge management can revolutionize the way an organization functions

CHAPTER OVERVIEW Decisions are, or at least ought to be, based on knowledge. The more knowledge a decision maker has, the more relevant that knowledge is to the decision, and the higher its quality, the better the decision is likely to be. Knowledge is therefore important to organizations: an asset, in an informal sense if not to an accountant. Since any asset should be managed for maximum value, knowledge should be managed as well. Since technology offers an effective way to manage non-physical assets, using technology to manage knowledge is an attractive concept— but knowledge has characteristics that make it difficult to manage. This chapter addresses how knowledge can be managed with the use of today’s technologies.

213

214

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

CHAPTER OUTLINE 11.1

OPENING VIGNETTE: SIEMENS KNOWS WHAT IT KNOWS THROUGH KNOWLEDGE MANAGEMENT A. PROBLEM B. SOLUTION C. RESULTS  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

11.2

INTRODUCTION TO KNOWLEDGE MANAGEMENT A. KNOWLEDGE MANAGEMENT CONCEPTS AND DEFINITIONS B. KNOWLEDGE C. EXPLICIT AND TACIT KNOWLEDGE ⧫ Application Case 11.1: Cingular Calls on Knowledge  Section 11.2 Review Questions

11.3

ORGANIZATIONAL LEARNING AND TRANSFORMATION A. THE LEARNING ORGANIZATION B. ORGANIZATIONAL MEMORY C. ORGANIZATIONAL LEARNING D. ORGANIZATIONAL CULTURE  Section 11.3 Review Questions

11.4

KNOWLEDGE MANAGEMENT ACTIVITIES A. KNOWLEDGE MANAGEMENT INITIATIVES AND ACTIVITIES B. KNOWLEDGE CREATION C. KNOWLEDGE SHARING D. KNOWLEDGE SEEKING  Section 11.4 Review Questions

11.5

APPROACHES TO KNOWLEDGE MANAGEMENT A. THE PROCESS APPROACH TO KNOWLEDGE MANAGEMENT B. THE PRACTICE APPROACH TO KNOWLEDGE MANAGEMENT ⧫ Application Case 11.2 Texaco Drills for Knowledge C. HYBRID APPROACHES TO KNOWLEDGE MANAGEMENT D. BEST PRACTICES E. KNOWLEDGE REPOSITORIES 1.

Developing a Knowledge Repository  Section 11.5 Review Questions

11.6

INFORMATION TECHNOLOGY (IT) IN KNOWLEDGE MANAGEMENT A. THE KMS CYCLE B. COMPONENTS OF KMS C. TECHNOLOGIES THAT SUPPORT KNOWLEDGE MANAGEMENT 1.

Artificial Intelligence

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

2.

3.

4.

215

Intelligent Agents Knowledge Discovery in Databases Extensible Markup Language (XML)  Section 11.6 Review Questions

11.7

KNOWLEDGE MANAGEMENT SYSTEMS IMPLEMENTATION A. KNOWLEDGE MANAGEMENT PRODUCTS AND VENDORS 1.

Software Development Companies and EIS Vendors ⧫ Application Case 11.3: Portal Opens the Door to Legal Knowledge ⧫ Application Case 11.4: Knowledge Management: You Can Bank on It at Commerce Bank 2.

Knowledge Management Consulting Firms 3.

Knowledge Management ASPs B. INTEGRATION OF KMS WITH OTHER BUSINESS INFORMATION SYSTEMS 1.

Integration of KMS with DSS/BI Systems 2.

Integration of KMS with AI 3.

Integration of KMS with Databases and Information Systems 4.

Integration of KMS with CRM Systems 5.

Integration with SCM Systems 6.

Integration of KMS with Corporate Intranets and Extranets  Section 11.7 Review Questions

11.8

ROLES OF PEOPLE IN KNOWLEDGE MANAGEMENT A. THE CHIEF KNOWLEDGE OFFICER B. THE CEO, OFFICERS, AND MANAGERS OF THE ORGANIZATION C. COMMUNITIES OF PRACTICE ⧫ Application Case 11.5: Online Knowledge Sharing at Xerox D. KMS DEVELOPERS ⧫ Technology Insights 11.6: Seven Principles for Designing Successful COP E. KMS STAFF  Section 11.8 Review Questions

11.9

ENSURING THE SUCCESS OF KNOWLEDGE MANAGEMENT EFFORTS A. SUCCESS STORIES OF KNOWLEDGE MANAGEMENT ⧫ Application Case 11.7: The British Broadcasting Corporation Knowledge Management Success B. USEFUL APPLICATIONS OF KMS 1.

Finding Experts Electronically and Using Expert Location Systems ⧫ Application Case 11.8: How the U.S. Department of Commerce Uses an Expert Location System C. KNOWLEDGE MANAGEMENT VALUATION 1.

Financial Metrics for Knowledge Management Valuation 2.

Nonfinancial Metrics for Knowledge Management Valuation D. CAUSES OF KNOWLEDGE MANAGEMENT FAILURE

216

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

⧫ A.

B. C.

Application Case 11.9: When KMS Fail, They Can Fail in a Big Way FACTORS THAT LEAD TO KNOWLEDGE MANAGEMENT SUCCESS ⧫ Technology Insights 11.10: Knowledge Management Traps POTENTIAL DRAWBACKS OF KMS CLOSING REMARKS ON KNOWLEDGE MANAGEMENT  Section 11.9 Review Questions

Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises ⧫ End of Chapter Application Case: DaimlerChrysler EBOKs with Knowledge Management  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

This chapter is somewhat free standing in its content. If you are pressed for time in your course and need to cut topics, it offers one possibility. Its content is not used later in the book. (Section 14.9, which discusses knowledge representation in the context of the Semantic Web, is selfcontained.) Should you go this route, you may still want your students to read some of the early part of this chapter and Section 11.9 so they’ll have at least vocabulary-level acquaintance with the concepts.

Knowledge management has fast become an integral part of many organizations’ IT strategy. It is important to relate the concept of knowledge management to its practical implementations. As students study this chapter, you could have them research different KMS implementations as individual or team projects. How do they differ? What issues did each organization encounter? The results can lead to interesting discussions.

An example of knowledge students can relate to is “What should I take next year?” Some answers are documented in official policies: “All business sophomores must take Accounting 101–2.” Some are part of a nominal program that can be varied: “MIS juniors normally take Marketing 301, but it can be taken in the fall of the senior year.” Some are part of advisors’ personal knowledge: “Art 311, Web Site Design, is a good way for MIS majors to fulfill half their liberal arts distribution requirement.” And some is even less official: “Prof. Fink is a better MIS 301 instructor than Prof. Lavin; she grades easier, too.” Most topics in this chapter can be related to this example: how could this information be stored? How could it be retrieved? Does the culture encourage people to share it? What would it take to change that? How can the technologies of this chapter be applied to it?

The Siemens vignette that opens this chapter is a good preview of what is to come. You might want to point out some of the coming themes in it, such as the nature of the knowledge,

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

217

the technologies they used and the need for cultural change. Some of these are brought out in the review questions for the section, but they’re worth noting even if you don’t use those.

In Section 11.2, tacit knowledge is the tricky concept. How can you manage something if you don’t even know exactly what it is? One point is that a lot of tacit knowledge doesn’t have to be, if we think of it the right way.

Section 11.3 is more important in practice than its length suggests. The information systems field is full of areas where the people problems are harder than the technological ones.

This is one. Students, especially those who gravitated toward MIS or CS as a major because they expected it to be relatively free of those pesky “people problems,” may find this a tough lesson to learn—but learn it they must. (By the way, you may want to discuss Communities of Practice with this section even though they’re covered in Section 11.8. I happen to think they fit better here, though it works either way.)

Sections 11.4 and 11.5 continue in a non-technical vein by discussing KM processes and approaches. Finally, 11.6 and 11.7 get to what some of your students have undoubtedly been waiting for: how technology fits into all this. Several of the tools discussed here, such as document management and content management systems, were not originally designed for knowledge management but turned out to be useful in it nonetheless. The Commerce Bank case, whose centerpiece is a knowledge management system built using Lotus Notes, is a good example of this point.

(In Section 11.5, draw students’ attention to the middle of p. 494 where it is pointed out that a knowledge repository, despite sometimes being called a “knowledge base,” is quite different from the knowledge base of an expert system. As you know, this is far from the only example of confusing terminology in IT, but it’s worth pointing out because some of the value of the first course in any field is enabling students to use its vocabulary correctly.)

In Section 11.8 students may initially see the title CKO, or Chief Knowledge Officer, as something of a status grab on the part of a middle, or middle-senior, manager. However, it makes sense. It is analogous to CFO, CIO, and others: responsibility for whatever goes on within their area, even in parts of the organization that do not report to them directly. For example, a CFO is responsible for budgeting for, depreciating, paying property taxes on, and all the other financial issues related to a new dump truck, even if another part of the company uses the truck. Similarly, the CKO is responsible for identifying knowledge sources, capturing knowledge, and making it available throughout the company, even if the knowledge belongs to people who report elsewhere (as it usually will).

Section 11.9 is an excellent wrap-up of the entire chapter. If students are likely to be working in this area in the future, the subsections on reasons for success and failure can be enough reason by themselves not to sell this book back to the bookstore on or amazon.com after the final.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 11.1 Review Questions 1.

How did the Siemens KMS evolve?

The Siemens KMS evolved from 1996 to 1999 as an employee-driven, bottom-up effort.

A group of employees researched what other companies were doing and began creating

218

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

repositories, communities of practice, and informal knowledge sharing techniques. In 1999, Siemens management, recognizing the value of what had been done, formalized the KMS by creating an organizational unit to be responsible for worldwide deployment of knowledge management. Because Siemens already had a reputation for technical brilliance, it was a natural extension for them to utilize technology to help share information.

2.

How does Siemens view knowledge (i.e., intellectual) assets?

Siemens’s top management considers knowledge and its management important enough that they created an organizational unit responsible for worldwide deployment of knowledge management. Another indication of the importance they ascribe to it are the tangible rewards they give employees for sharing their knowledge.

3.

What does leveraging expertise mean? How did Siemens do this? Explain how this relates to Siemens’s high return on investment.

Leveraging expertise means making employees’ knowledge available to other employees, saving them the time and trouble of rediscovering it or the less-than-ideal results of not having it at all.

Siemens did this by implementing a Web site called ShareNet. It combined a knowledge repository, a chat room where employees can ask questions and others can answer, and a search engine to locate useful knowledge that has been placed in the repository. Its current version stores and catalogs knowledge, makes it available across the company, and thereby enhances global collaboration.

One example of how this relates to Siemens’s high ROI is the investment of $7.8 million to develop part of ShareNet to support the Information and Communication Networks Group. This helped generate $122 million in additional sales within the next two years. (Perceptive students will point out that (a) the profits derived from sales of $122 million are considerably less than this figure, and (b) we do not know how important the KMS contribution was to the sales increase.)

4.

Describe the benefits of the Siemens ShareNet KMS. The ShareNet KMS combines a knowledge repository, a chat room where employees can ask questions to which others might have answers, and a search engine to locate useful knowledge that has been placed in the repository. Its current version stores and catalogs knowledge, makes it available across the company, and thereby enhances global collaboration.

5.

Explain the meaning of culture transformation as it occurred at Siemens. Include in your answer how the various constituencies bought into the system.

Culture transformation at Siemens meant convincing employees that it is important to participate in the exchange of ideas and experiences and to share what they know.

Making this happen involved making it easy to share knowledge, assigning “evangelists” to motivate people to do so (among their other responsibilities) and rewarding them for sharing.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

219

Constituencies that have bought into the system and are mentioned in the case include: • Top management, which recognized the value of knowledge sharing, formalized programs to share knowledge, and provided the necessary resources for doing so.

• Commission-driven salespeople, who have found that the knowledge available in ShareNet helps them win business • Other employees (the case specifically mentions marketing, service and R&D, while adding “and other departments”) realize that the system “provides them with useful information in a convenient way.”

6.

Explain how Internet and Web technologies enabled the Siemens KMS. In (at least) two ways: • The Internet provides the communications infrastructure that makes it possible for employees throughout the company to participate in knowledge sharing.

• The KMS is structured as a Web site, which gives users the advantages of a familiar, easy-to-use interface as noted in other contexts several times earlier in this book.

Section 11.2 Review Questions 1.

Define knowledge management and describe its purposes.

Knowledge management (KM) is the systematic and active management of ideas, information, and knowledge residing in an organization’s employees.

Its purposes include effective and efficient problem solving, dynamic learning, strategic planning, and decision making. KM initiatives focus on identifying knowledge, explicating it in such a way that it can be shared in a formal manner, and leveraging its value through reuse.

2.

Distinguish between knowledge and data.

Knowledge and data are related through information, which is also defined in this answer.

Data are facts, measurements, and statistics.

Information is organized or processed data that is timely (i.e., inferences from the data are drawn within the time frame of applicability) and accurate (i.e., with regard to the original data).

Knowledge is information that is contextual, relevant, and actionable.

(The conventional definition of actionable is “giving reason for legal action, such as slander.” If you accept that meaning for the term, you may prefer an alternative phrase here. However, since no single English word means “providing a practical basis for management action,” the term actionable is increasingly used in that sense today even though some still consider it incorrect.)

220

3.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Describe the knowledge-based economy.

A knowledge-based economy is one in which the primary resource used to produce economic benefits is knowledge—as distinct from land, capital, energy or any other resource. One difference between knowledge and these other resources is that knowledge can be shared without diminishing, and can actually increase as a result of such sharing.

Knowledge is also easier to transfer than are other resources, making laws and barriers difficult to apply and reducing the effect of location. Also, knowledge can have widely different values to different people, or even to the same person at different times.

(The term “knowledge-based economy” was first popularized, if not invented, by Peter Drucker as the title of Chapter 12 in The Age of Discontinuity (1969). Business students should recognize Drucker as one of the great management thinkers of the 20th century.)

4.

Define tacit knowledge and explicit knowledge.

(This answer. approaches the terms in the opposite order from the question, as explicit knowledge is discussed first in the text and also seems easier to define.)

Explicit knowledge is knowledge that has been or can be articulated, codified, and stored. It deals with objective, rational and technical information in manuals, documents, procedures, software, and even stories (though this last is less common in business).

Tacit knowledge has been described as “know-how,” as opposed to the “know-what” and “know-why” of explicit knowledge. It is often highly personal and difficult to formalize, and deals with subjective, cognitive and experiential learning. Some tacit knowledge could be made explicit if the person holding the knowledge recognized the value of doing so, but in other cases it is difficult to codify (such as knowing how to ride a bicycle).

Capturing and sharing tacit knowledge is a more difficult knowledge management task than is capturing and sharing explicit knowledge.

5.

Define KMS and describe the capabilities of KMS. The term KMS, or knowledge management system(s), refers to the use of modern information technology to systematize, enhance and expedite knowledge management inside and outside a firm. (See the answer to Review Question 1 of this section above for “knowledge management.”)

KMS capabilities include capturing knowledge, storing it, allowing users to search the system for relevant knowledge, and making knowledge available to them.

Some KMS use specific technologies for these purposes (e.g., the use of AI to process queries in the Cingular example in Application Case 11.1, p. 485) and some add additional features (e.g., the ability to contact a person who stored certain knowledge in the system with a question about it) but those are the basic conceptual elements.

Section 11.3 Review Questions 1.

Define learning organization and identify the characteristics of learning organizations.

A learning organization is one that is able to learn from its past experience. A learning organization performs these five activities well: solving problems systematically,

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

221

experimenting creatively, learning from past experience, learning from the best practices of others, and transferring knowledge quickly and efficiently throughout the organization.

2.

Define organizational memory.

Organizational memory is the way an organization brings knowledge from the past to bear on its present activities. This requires a way to save, represent and share knowledge, often but not necessarily a KMS (see previous section).

3.

Describe organizational learning.

“The development of new knowledge and insights that have the potential to influence an organization’s behavior.”

4.

Define organizational culture and relate it to knowledge management.

Several definitions are possible: “A pattern of shared basic assumptions [based on] learning over time what works and what doesn’t work, which become second nature.” (p. 487, edited for continuity of excerpts) “The specific collection of values and norms that are shared by people and groups in an organization and that control the way they interact with each other and with stakeholders outside the organization … From organizational values develop organizational norms, guidelines or expectations that prescribe appropriate kinds of behavior by employees in particular situations and control the behavior of organizational members towards one another.” (Hill, C. W. L., and G. R. Jones, Strategic Management (5e), Houghton Mifflin, 2001) Culture is related to knowledge management because knowledge management, to be successful, must be accepted by members of an organization. Their willingness to do this, the causes of possible resistance, and the methods by which they can be motivated to overcome resistance all depend on the organization’s culture. This is true to some degree of any change, but especially of an effort to introduce something like knowledge management that has little obvious near-term benefit but has all the drawbacks listed in the shaded box on pp. 487–8.

Section 11.4 Review Questions 1.

Why do companies need knowledge management initiatives?

For two basic reasons: knowledge management is beneficial, and it won’t happen by itself.

Knowledge management is more beneficial today than in the past because of increasingly intense competition, the global marketplace, and (in part due to these two) increased personal job mobility.

Knowledge management won’t happen by itself because for the reasons listed in the shaded box on pp. 487–8.

222

2.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Describe the process of knowledge creation.

Knowledge creation is the generation of new insights, ideas, or routines. It is an interplay between tacit and explicit knowledge, a growing spiral as knowledge moves among the individual, group, and organizational levels. There are four modes of knowledge creation: • Socialization: converting tacit knowledge to new tacit knowledge through social interactions and shared experience among organization members.

• Combination: creating new explicit knowledge by merging, categorizing, reclassifying, and synthesizing existing explicit knowledge.

• Externalization: converting tacit knowledge to new explicit knowledge.

• Internalization: creating new tacit knowledge from explicit knowledge.

3.

What are the characteristics of knowledge sharing?

• It takes place when there is trust, interest, and shared language.

• It requires access to knowledgeable members of the organization.

• It requires a culture “marked by autonomy, redundancy, requisite variety, intention, and fluctuation.”

4.

Define knowledge seeking (or sourcing).

“The search for, and use of, internal organizational knowledge.”

Section 11.5 Review Questions 1.

Define the process approach to knowledge management.

The process approach attempts to codify organizational knowledge through formalized controls, processes, and technologies. It frequently uses IT in forms such as intranets and data warehousing.

(Note that this question opens with “Define,” while the next one opens with “Describe.”

This can be taken as an effort to vary the phrasing or as a deliberate request for different types of answers. The answers presented here are both brief descriptions. If you have more specific requirements, it might be well to make them explicit.)

2.

Describe the practice approach to knowledge management.

The practice approach to knowledge management assumes that the vast majority of organizational knowledge is tacit and that formal controls and processes are not effective in their transfer. The focus of this approach is to build communities of practice.

3.

Why is a hybrid approach to KM desirable?

Because both the practice approach and the process approach have drawbacks. By using each where it is most appropriate, typically evolving from a practice approach to a process approach, the advantages of both can be obtained while minimizing the impact of their drawbacks.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

4.

223

Describe best practices as they relate to knowledge management.

There are two possible, and very different, interpretations of this question: (a) What are best practices in knowledge management? and (b) How can KM help an organization disseminate and implement best practices as they relate to its operations? If you have one of these in mind, you may want to make your requirements known to students.

Much of the chapter, both what has gone before and what is to come, addresses the first interpretation of this question. The current section’s “Best Practices” subsection addresses the second. It points out that early knowledge repositories listed best practices and made them available within a firm, and that on-line, Web-accessible repositories can have a wide-ranging impact on the use of knowledge throughout it. It further categorizes the best practices that can be put into a repository into four levels, from lowest to highest and condensed from p. 494: 1.

A good idea that is not yet proven but makes intuitive sense.

2.

A good practice that has improved business results.

3.

A local best practice for all or part of the organization based on hard data.

4.

An industry best practice, similar to the third level but using hard data from industry.

5.

Define knowledge repository and describe how to create a knowledge repository.

A knowledge repository is a database in an informal sense, though not in the formal sense of database management systems. It is an electronic storehouse of knowledge which is often text based and may have widely varying characteristics. Its structure will depend on its purpose, the type(s) of knowledge it stores, and the way that knowledge is represented.

There are three ways to create a knowledge repository: to develop a custom Internet-based system, to purchase a document management system, or to purchase a knowledge management package. Whichever of these is chosen, it is important to make contributing knowledge easy for the contributor and to have a good way to catalogue the knowledge it will contain.

Section 11.6 Review Questions 1.

Describe the KMS cycle.

2.

The KMS cycle has the steps listed below. They are further elaborated on p. 495 and shown graphically on p. 496: 1.

Create knowledge.

2.

Capture knowledge.

3.

Refine knowledge.

4.

Store knowledge.

5.

Manage knowledge.

6.

Disseminate knowledge.

7.

List and describe the components of KMS. Components of KMS include communication, collaboration, and storage and retrieval.

224

3.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Describe how AI and intelligent agents support knowledge management.

Artificial intelligence supports knowledge management by doing the following: • Assist in and enhance searching knowledge (e.g., intelligent agents in Web searches) • Help establish knowledge profiles of individuals and groups • Help determine the relative importance of knowledge when it is contributed to and accessed from the knowledge repository • Scan e-mail, documents, and databases to perform knowledge discovery, determine meaningful relationships, glean knowledge, or induce rules for expert systems • Identify patterns in data (usually through neural networks) • Forecast future results by using existing knowledge • Provide advice directly from knowledge by using neural networks or expert systems • Provide a natural language or voice command–driven user interface for a KMS Intelligent agents support knowledge management by learning what the user prefers to see and how the user organizes it. Then, the intelligent agent takes over to provide that information at the desktop, just as a good administrative assistant would.

4.

Relate XML to knowledge management and to knowledge portals.

One of the challenges in knowledge management is selecting a good, common representation of knowledge embedded in documents. Extensible Markup Language (XML) assists in meeting this challenge by enabling standardized representations of data structures, so that data can be processed appropriately by heterogeneous systems without case-by-case programming. Thus, XML-based messages can be taken from back-end repositories and fed out through the portal interface. Wide adoption of XML can help solve the problem of integrating data from disparate sources. Due to its potential to simplify systems integration, XML may become the universal language of all portal vendors.

Section 11.7 Review Questions 1.

Define knowware.

Technology tools (for all practical purposes, software) to support knowledge management. (The term was coined by analogy to hardware, software, middleware, etc.

While it was by no means invented for this book, it has yet to gain widespread use. A Google search for knowware in Dec. 2006 got 83,300 hits, compared with over 14 million for middleware and nearly 40 million for firmware.)

2.

Describe the major categories of knowledge management tools.

The seven major categories of knowledge management tools are: • Collaborative computing tools, for transferring knowledge within an organization • Knowledge servers, for storing and providing access to a knowledge repository

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• • • • • 3.

225

Enterprise knowledge portals: see answer to Question 3 below Electronic document management: see answer to Question 4 below Knowledge harvesting tools: see answer to Question 5 below Search engines, for finding knowledge in a repository Knowledge management suites: see answer to Question 6 below

Define EKP. An Enterprise Knowledge Portal. EKPs provide convenient access to an organization’s body of stored knowledge, while a server handles document and knowledge management parts of a KMS.

4.

Define EDM and relate it to knowledge management and to CMS. Electronic Document Management. Since much knowledge is stored in the form of unstructured documents of various types, and EDM is designed to manage such documents, they are a natural fit.

EDM systems are related to Content Management Systems (CMS) because CMS is simply a new approach to EDM that includes features for versioning (keeping track of various versions of a document) and to reflect business rules in document management activities.

5.

Describe tools for knowledge harvesting.

Knowledge harvesting is the process of capturing knowledge from users with minimal involvement or activity on their part. Such tools analyze documents of specific types, such as e-mail or word processing, for content that indicates knowledge. They then use this information either to store the document as knowledge or to categorize the document’s originator as a source of knowledge.

6.

List the major systems that are frequently integrated with KMS. KMS integrate communications, collaboration, and storage systems.

Section 11.8 Review Questions 1.

Describe the role of the CKO. The Chief Knowledge Officer (CKO) is responsible for all knowledge managementrelated activities that go on in an organization. (See the “Teaching Tips” section of this manual for more on the use of this title.) Specifically, a CKO should: • Set knowledge management strategic priorities • Establish a knowledge repository of best practices • Gain a commitment from senior executives to support a learning environment • Teach information seekers how to ask better and smarter questions • Establish a process for managing intellectual assets • Obtain customer satisfaction information in near real-time • Globalize knowledge management

226

2.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

What other managers are involved with knowledge management?

Ideally, all of them! Responsibilities of specific managers listed in the text (p. 509) include: • CEO: championing a KM effort, finding a CKO, providing resources, gaining organizational support for KM and preparing the organization for the cultural changes KM will bring • CFO: ensuring financial resources • COO: ensure that people embed KM practices into their work • CIO: providing IT resources, including new KMS and use of existing systems if suitable • Managers in general: support the KM effort and provide access to sources of knowledge

3.

Describe COP and relate them to knowledge management.

COP (Communities of Practice) are groups of people with common professional interests, independent of role, geography, or project affiliation. For example, quality assurance engineers might form a community of practice in a multinational manufacturing firm.

COPs are related to knowledge management in two main ways: first, they are the place where knowledge in the area of interest resides, and second, they are where organizational culture shift must happen when KMS are deployed. They have other, relatively less central, connections as well.

4.

What is the importance of COP in organizations?

Communities of practice add value to an organization in (at least) six ways: • Creation of higher-quality knowledge • Fewer surprises and plan revisions • Greater capacity to deal with unstructured problems • More effective knowledge sharing among business and corporate staff units • Improved likelihood of implementing joint goals • More efficient individual development and learning

Section 11.9 Review Questions 1.

Describe the need for measuring the success of KMS. KMS involves the use of corporate resources, additional work for employees, and a cultural shift that may be difficult for some. These must be justified. While an initial KMS can be justified on the basis of faith in the technology, interest on the part of a pilot group, and case studies documenting other firms’ successes, later projects will require a degree of success with early ones. This means that their success must be measured.

2.

What are the issues in knowledge management valuation?

The fundamental issue in knowledge management valuation is whether knowledge should be valued as an asset, valued for what it would bring if sold, or knowledge management should be valued for its benefits. Each approach raises specific secondary issues.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

3.

227

List some financial (tangible) metrics of knowledge management.

The fundamental financial metric for KM valuation is the ROI of KM projects. Other possible financial metrics, not discussed in the text, are the increase in the value of knowledge based on either an asset approach or an if-sold approach.

4.

List some intangible (nonfinancial) metrics of knowledge management.

5.

These include (but are not necessarily limited to) the following: • Customer goodwill • External relationship capital • Structural capital • Human capital • Social capital • Environmental capital • List failure factors associated with knowledge management.

Reasons for failure mentioned in the text include an effort that relies mainly on technology, without addressing whether or not a proposed system meets organizational needs; lack of commitment; failure to consider cultural issues; and failure to provide incentives to use the system. Others are listed in cited references and in other sources.

6.

List success factors associated with knowledge management.

Factors that contribute to KM project success include the following (pp. 519–20): • A link to the firm’s economic value • A technical and organizational infrastructure on which to build • A knowledge structure that matches how the organization works and uses knowledge • A knowledge-friendly culture that leads directly to user support • A clear purpose and language, to encourage user buy-in • A change in motivational practices, to create a culture of sharing • Multiple channels for knowledge transfer—because individuals have different ways of working and expressing themselves • A level of process orientation to make a knowledge management effort worthwhile • Motivational methods to encourage users to contribute and use knowledge • Senior management support

7.

What are the potential drawbacks of KMS? The major drawback cited in the subsection with the corresponding title is that knowledge, once embedded in a KMS, may be difficult to change as circumstances change. Following yesterday’s best practices may not lead to tomorrow’s success.

228

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Other potential drawbacks, such as cost, need for possibly painful cultural change, and more, are mentioned throughout the chapter. Students may find them in other sources as well.

8.

Describe expert location systems.

Expert location systems are systems for locating experts, to help employees find and connect with colleagues who have the expertise required to solve specific problems, wherever they are.

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Why is the term knowledge so difficult to define?

There is a vast amount of literature about what knowledge and knowing means in epistemology, social sciences, philosophy, and psychology. Although business is more pragmatic, there is no uniform definition or consensus on what knowledge and knowledge management specifically mean. We suspect that there are so many definitions because each field approaches knowledge differently, and each has different sets of assumptions.

2.

Describe and relate the different characteristics of knowledge to one another.

Unlike other assets, knowledge has: (see Gray 1999, cited in the chapter references) • Extraordinary leverage and increasing returns. Knowledge is not subject to diminishing returns. When it is used, it is not consumed. Its consumers can add to it, thus increasing its value. It can improve.

• Fragmentation, leakage, and the need to refresh. As knowledge grows, it branches and fragments. Knowledge is dynamic; it is information in action. Thus, an organization must continually refresh its knowledge base to keep it as a source of competitive advantage.

• Uncertain value. It is difficult to estimate the impact of an investment in knowledge.

• Uncertain value sharing. Similarly, it is difficult to estimate the value of sharing the knowledge, or even who will benefit most.

3.

Explain why it is important to capture and manage knowledge.

Knowledge can have incredible impact on an organization when it is captured, managed, and most importantly reused. The leveraging of knowledge is clear from the opening vignette. An investment of less than $8 million in KM contributed, presumably significantly, to a sales increase of $122 million during two years. While we don’t have figures tying the revenue increase to a profit increase, the positive impact of these incremental sales on the bottom line is almost surely over $8 million.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

4.

229

Compare and contrast tacit knowledge and explicit knowledge.

See answer to Review Question 4 of Section 11.2. While it is more desirable to capture tacit knowledge because it is more valuable, explicit knowledge is typically more readily available.

5.

Explain why organizational culture must sometimes change before knowledge management is introduced.

If there is no culture of sharing, it must be developed or any KM effort is doomed to failure.

6.

How does knowledge management attain its primary objective?

The objective of KM is to identify knowledge, explicate it so it can be shared, and leverage its value through reuse. KM attains its objectives by providing a means to capture knowledge and then make it available throughout an organization.

7.

How can employees be motivated to contribute to and use KMS? Real reward systems and recognition seem to work best. People really like recognition (putting their names at the bottom as contributors). It can help if raises and promotions are tied to knowledge contribution and use.

8.

What is the role of a knowledge repository in knowledge management?

The knowledge repository is the storage mechanism for the knowledge. It is like a database in function, but is not a database as the term database is usually understood.

9.

Explain the importance of communication and collaboration technologies to the processes of knowledge management.

Without communication, there is no mechanism to share knowledge. Collaboration, a fancy word for people working together, is at the heart of any type of sharing—including knowledge.

10.

Explain why firms adopt knowledge management initiatives.

The top five reasons that firms initiate knowledge management systems are: • Faster response time to key issues (it takes less time to figure out what to do) • Better decision making (because of more knowledge of how to make them) • Reduced costs (because of better decisions) • Improved productivity (ditto) • Shared best practice (which can be considered to lead to the above)

11.

Explain how the wrong organizational culture can reduce the effectiveness of knowledge management.

If people are not motivated to share their knowledge, or departments take the stance that something they didn’t invent is no good, etc., then the KM effort will limp along or fail.

230

12.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Explain the role of the CKO in developing a KMS. What major responsibilities does he or she have?

The CKO is ultimately responsible for a KMS “from cradle to grave:” for the entire knowledge management project while under development, and then for the management of the system and the knowledge once it is deployed. He or she: • Heads up knowledge management efforts • Is responsible for defining the area(s) of knowledge within the firm that will be the focal point(s), based upon the firm’s mission and objectives.

• Is responsible for standardizing the enterprise-wide vocabulary and for controlling the knowledge directory.

• Must get a handle on the company's repositories of research, resources, and expertise, including where they are stored and who manages and accesses them.

• Must encourage pollination among disparate workgroups with complementary resources.

• Is responsible for creating an infrastructure and cultural environment for knowledge sharing.

• Must assign or identify knowledge champions within business units to manage content their group produces, adding to the knowledge base and encouraging their colleagues to.

13.

What is meant by a culture of knowledge sharing?

One in which the informal norm is to contribute knowledge into a system to and use it (not just pay lip service to it). It involves support at the highest level in an organization, and establishment of easy-to-use methods to capture and use the knowledge.

14.

Discuss the factors related to knowledge management success.

See list in the answer to Review Question 6 of Section 11.9. The discussion can be openended.

15.

Why is it so difficult to evaluate the impacts of knowledge management?

Because many of its benefits are intangible, or are seen only in combination with the results of other factors. For example, the outcome a decision that was made with the assistance of a KMS usually cannot be compared with the outcome of the decision that would have been made without the KMS, even if it is possible to figure out what that decision might have been.

16.

Explain how the Internet and related technologies (e.g., Web browsers, intranets) enable knowledge management.

In (at least) two ways: • The Internet provides the communications infrastructure that makes it possible for employees throughout the company to participate in knowledge sharing.

• The KMS is structured as a Web site, which gives users the advantages of a familiar, easy-to-use interface as noted in other contexts several times earlier in this book.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

231

(This is a generalization of Review Question 6 of Section 11.1, about the Siemens system described in the opening vignette.)

17.

List three top technologies most frequently used for implementing KMS and explain their importance.

The three top technologies most frequently used for implementing KMS are communication technologies, collaboration technologies, and storage/retrieval technologies. Without all of these, the objectives of knowledge management cannot be achieved.

18.

Explain the roles of a community of practice.

See answer to Review Question 4 in Section 11.8. Each of the bullet items there can have an open-ended explanation.

19.

Describe an EKP and explain its significance.

See answer to Review Question 3 in Section 11.7.

ANSWERS TO END- OF- CHAPTER APPLICATION CASE QUESTIONS 1.

   

Platform design at DaimlerChrysler led directly to a reduction in the time to market and in costs for new vehicles. Explain how it caused new problems.

By organizing around products rather than organizational functions or skill sets, it separated people from the old mentor-apprentice relationship. Younger, inexperienced, but nonetheless competent people made mistakes that had been made before. Older, experienced people knew solutions, especially in design, which they were not passing on to the next generation of designers because they were no longer working together.

2.

What is meant by a COP? How did DaimlerChrysler leverage the knowledge within such a community?

The first part of this question is answered in the answer to Review Question 3 of Section 11.8.

DaimlerChrysler leveraged the knowledge within communities of practice by identifying COPs and structuring them into “tech clubs.” It then used these to codify their knowledge within EBOKs and to provide experiences, such as mentoring, that had been provided by the earlier functional organization.

3.

Describe DaimlerChrysler’s EBOK. Explain how it is updated by adding new knowledge of practice.

The EBOK, or Engineering Book Of Knowledge, is an intranet-based knowledge repository on technical aspects of vehicle design and manufacturing.

232

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Its update process begins when a member of a community of practice proposes a best practice. The proposal is reviewed and discussed within that COP (or “tech club”) until it is approved, at which point it becomes part of the EBOK. 4.

The correct function for all knowledge management tools is to leverage technology in the service to human thinking. Explain this statement.

Open-ended answer. This makes general sense from the goals of knowledge management as stated earlier in the chapter. (The statement is not in the case as such, but is related to the comment that the EBOK “leverages technology knowledge,” at the top of the righthand column of p. 525.)

5.

How successful was the knowledge management initiative at DaimlerChrysler?

As far as we can tell from the case, extremely so.

6.

Consider how a book of knowledge could affect another organization, ideally one with which you are affiliated (e.g., your university, job, part-time job, family business).

Describe the potential impacts and list the benefits. Would there be any organizational culture issues to deal with? Why or why not?

Open-ended answer. You may want to standardize responses somewhat by narrowing the options for choosing an organization, at least to ensure that it could use a BOK effectively.

CHAPTER

12

Artificial Intelligence and Expert Systems

Learning Objectives for Part V ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the foundations, definitions, and capabilities of artificial intelligence and knowledge-based systems Understand the process and tools for developing knowledge-based intelligent systems Understand how artificial neural networks (ANN) are used to develop intelligent decision support systems (DSS) Understand how genetic algorithms are used to develop intelligent systems Understand how fuzzy logic can be used to develop intelligent systems Understand state-of-the art applications of intelligent agents and recommendation systems on the Internet

Learning Objectives for Chapter 12 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the concept and evolution of artificial intelligence Understand the importance of knowledge in decision support Describe the concept and evolution of rule-based expert systems (ES) Understand the architecture of rule-based ES Explain the benefits and limitations of rule-based systems for decision support Identify proper applications of ES Learn about tools and technologies for developing rule-based DSS

CHAPTER OVERVIEW From major hype in the 1950s and 1960s through the “AI winter” of the 1980s1, the field of artificial intelligence has weathered major ups and downs. Through all of this, one technology has become increasingly practical in business: that of expert systems. They may not have lived 1

You may want to read the Wikipedia page on this topic at: http://en.wikipedia.org/wiki/AI_winter

up to their early hype, but that is as much the fault of the early stages of the “hype cycle” as it is of the systems themselves2. Today, they are so common as components of larger systems that they are not always recognized for what they are—leading some to believe that they have disappeared from the scene, when nothing could be further from the truth. ES represent an important element of any DSS developer’s toolkit, which differs in important respects from others. Instead of modeling the problem, ES model one or more experts’ approach to it. This gives them unique strengths and weaknesses that DSS students must understand. This chapter will give students that understanding.

CHAPTER OUTLINE 12.1

OPENING VIGNETTE: CIGNA USES BUSNESS RULES TO SUPPORT TREATMENT REQUEST APPROVAL A. PROBLEM B. SOLUTION C. RESULTS  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

12.2

CONCEPTS AND DEFINITIONS OF ARTIFICIAL INTELLIGENCE A. ARTIFICIAL INTELLIGENCE (AI) DEFINITIONS ⧫ Application Case 12:1: Intelligent Systems Beat Chess Grant Master B. CHARACTERISTICS OF ARTIFICIAL INTELLIGENCE 1.

Symbolic processing 2.

Heuristics 3.

Inferencing 4.

Machine learning  Section 12.2 Review Questions

12.3

THE ARTIFICIAL INTELLIGENCE FIELD A. EVOLUTION OF ARTIFICIAL INTELLIGENCE B. APPLICATIONS OF ARTIFICIAL INTELLIGENCE ⧫ Application Case 12:2: Intelligent Systems Versus Natural Intelligence 1.

Expert Systems 2.

Natural Language Processing 3.

Speech (Voice) Understanding ⧫ Application Case 12.3: Natural Language Processing (NLP) ⧫ Application Case 12.4: Automatic Speech Recognition in Call Centers 4.

Robotics and Sensory Systems 5.

Computer Vision and Scene Recognition 6.

Intelligent Computer-Aided Instruction 7.

Automatic Programming 8.

Neural Computing 9.

Game Playing

2

A brief, readable introduction to the Hype Cycle concept is at: http://www.floor.nl/ebiz/gartnershypecycle.htm

10.

11.

12.

13. ⧫

Language Translation Fuzzy Logic Genetic Algorithms Intelligent Agents Application Case 12.5: Agents for Travel Planning at USC  Section 12.3 Review Questions

12.4

BASIC CONCEPTS OF EXPERT SYSTEMS (ES) A. EXPERTS B. EXPERTISE C. FEATURES OF ES D. WHY WE NEED ES ⧫ Application Case 12.6: Sample Sessions of a Rule-Based ES  Section 12.4 Review Questions

12.5

APPLICATIONS OF EXPERT SYSTEMS ⧫ Application Case 12.7: Sample Applications of ES A. Classic successful ES B. DENDRAL C. MYCIN D. XCON E. NEWER APPLICATIONS OF ES 1.

Credit Analysis Systems 2.

Pension Fund Advisors 3.

Automated Help Desks 4.

Homeland Security Systems 5.

Market Surveillance Systems 6.

Business Process Reengineering Systems F. AREAS FOR ES APPLICATIONS  Section 12.5 Review Questions

12.6

STRUCTURE OF EXPERT SYSTEMS A. KNOWLEDGE ACQUISITION SUBSYSTEM B. KNOWLEDGE BASE C. INFERENCE ENGINE D. USER INTERFACE E. BLACKBOARD (WORKPLACE) F. EXPLANATION SYSTEM (JUSTIFIER) G. KNOWLEDGE-REFINING SYSTEM  Section 12.6 Review Questions

12.7

HOW EXPERT SYSTEMS WORK: INFERENCE MECHANISMS A. KNOWLEDGE REPRESENTATION AND ORGANIZATION B. THE INFERENCE PROCESS 1.

Forward Chaining 2.

Backward Chaining

C. 12.8

12.9

DEVELOPMENT PROCESS OF ES  Section 12.7 Review Questions PROBLEM AREAS SUITABLE FOR EXPERT SYSTEMS  Section 12.8 Review Questions DEVELOPMENT OF EXPERT SYSTEMS A. DEFINING THE NATURE AND SCOPE OF THE PROBLEM B. IDENTIFYING PROPER EXPERTS C. ACQUIRING KNOWLEDGE D. SELECTING THE BUILDING TOOLS 1.

General-Purpose Development Environment 2.

ES Shells 3.

Tailored Turn-Key Solutions 4.

Choosing an ES Development Tool E. CODING THE SYSTEM F. EVALUATING THE SYSTEM  Section 12.9 Review Questions

12.10 BENEFITS, LIMITATIONS AND SUCCESS FACTORS OF EXPERT SYSTEMS A. BENEFITS AND LIMITATIONS OF ES 1.

Benefits of ES 2.

Problems and Limitations of ES B. ES SUCCESS FACTORS  Section 12.10 Review Questions 12.11 EXPERT SYSTEMS ON THE WEB ⧫ Application Case 12.9: Banner with Brains: Web-Based ES for Restaurant Selection ⧫ Application Case 12.10: Rule-Based System for Online Student Consultation  Section 12.11 Review Questions Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Internet Exercises ⧫ End of Chapter Application Case: Business Rule Automation at Farm Bureau Financial Services  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

This chapter begins a three-chapter module on Intelligent Systems. I happen to think they’re worth the time, for reasons discussed in the “Chapter Overview” section above. At the same time, not everyone feels the same. If your course is about DSS excluding expert systems and other AI applications, my thinking that you’re missing a bet won’t change that. Skip all three chapters.

Assuming that anyone who’s still reading will cover this chapter, its opening vignette is a good introduction to a fairly typical business application of expert systems. One of the key points is that they had experts in the problem, but not enough of them; the experts were willing to share their knowledge; and the knowledge could be reduced to a standard form where computers could apply it to specific application situations. It shouldn’t take a great leap of faith for your students to accept the assertion that situations with these characteristics are not rare.

The majority of the rest of the chapter goes into artificial intelligence from the general to the specific. It discusses the entire field of AI in Sections 12.2 and 12.3, then zeroes in on expert systems for the rest of the chapter. The general discussion of AI serves to position expert systems as part of that field of technology, but other than that doesn’t really help understand what follows.

(Section 12.2, by the way, presents the Turing test as it is usually described but not as proposed by Alan Turing. This form of the test is often felt to give the person an unfair advantage, as he or she can answer naturally whereas the computer must pretend to be something it isn’t. In Turing’s concept a computer and a male subject both pretend to be women, placing them on a more equal—if not entirely equal—footing.)

The essence of this chapter begins with Section 12.4. The comparison of conventional systems and expert systems in Table 12.1 is worth a few minutes.

The first difference is basic and often missed by students who have studied programming and wonder what expert systems do that a regular program couldn’t do with enough IF statements. The answer, of course, is “nothing”—but they do it in a way that gives them operational (not technical) advantages in many practical situations—as the fifth point in the table says. Business students ought to appreciate this point readily. CS majors may subconsciously have a more difficult time dealing with this.

The second point about ES in that table, “The program may make mistakes,” is often misunderstood. This doesn’t mean that ES don’t have to work. If they’re not right much of the time, if they’re not right more often than their users would be unaided, they’re useless. The point is that their knowledge is often insufficient to deal with every problem that might be presented to them. Therefore, the overall (organizational) system of which they’re a part has to be designed to tolerate errors in its ES subsystem. In the opening vignette, this was achieved by having people review the requests that eCare could not handle automatically.

Section 12.5 discusses examples of expert systems. Covering them is a good idea, but (as opposed to some other topics) it makes little difference where in the chapter discussion you do this. This spot works well, but if you like another there’s no reason not to move them.

Section 12.7 is where the “rubber hits the road” in terms of how these things really work inside. The knowledge base in the shaded box on p. 556 goes with the dialogues on pages 547 (Technology Insights 12.6) and 557 (Table 12.4). Students should be able to see that the knowledge base does not contain all the knowledge that would be needed to handle the implications of the user interface choices, but it still has enough to show how an ES works.

It can be constructive to relate Table 12.4 to the rules and how each approach to inference works through them. The book does this on pp. 557–558. Make sure your students understand what’s really going on here before you proceed. While the same rule base ought to reach the same conclusion from the same facts either way3, one that uses the wrong approach can waste time and ask unnecessary questions. Making the right choice can therefore be important to user satisfaction.

The problem areas, advantages and limitations of expert systems discussed in Sections 12.8 and 12.10 follow from how the technology works. As you discuss them, you may want to emphasize this point and show the connection between each major point and Section 12.7.

Finally, if you discussed the opening vignette in depth, you may not want to spend much time on the end-of-chapter case since the lessons of the two are similar. Fortunately, you won’t have any trouble finding alternatives on the Web—or asking your students to!

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 12.1 Review Questions 1.

Describe CIGNA’s motivation for developing the eCare system.

Cigna had to approve or turn down requests submitted by therapists for additional patient care. Its rules for doing so were cumbersome, hard to find, scattered over many software applications, and sometimes contradictory. The software was difficult to modify when problems were discovered. Its staff was also fully busy with the then-current volume of requests, so hiring additional staff would have been required to handle growth. It was felt that the eCare system could solve these problems.

2.

Explain the role of the intelligent systems and their potential benefits in the case.

There is one intelligent system in this case, eCare. Its role is to review behavioral therapists’ requests for additional patient care, to approve (or turn down? the case doesn’t say) those whose situations are covered by the system’s rules, and to pass the remainder (under 70 percent as the case was written) on to humans for decisions. The benefits are (a) lower cost, through not having to hire additional adjusters for that 30+ percent of all requests; (b) systematization and simplification of the rules, making them easier to find, easier to encode into the software and eliminating conflicts among them; and (c) putting the rules in a form where they would not be lost with employee turnover.

Two additional software tools were used to create eCare: HaleyAuthority and HaleyRules. The first of these encodes the rules used by behavioral health clinicians into a standard form. The second applies the rules to a specific therapist request.

3.

What major difficulties could be anticipated in the process of developing the system?

Primarily human issues. People might see their jobs as threatened if they thought a computer might be able to make these decisions in the future. Because of this concern,

3

Unless the rules were written to depend on the order in which a shell processes rules. This is usually a poor idea.

behavioral health clinicians might not cooperate with the developers and explain how they make decisions.

4.

How are the systems in this case different from traditional DSS described in previous chapters?

The systems described in previous chapters assist people in applying human intelligence to decision-making situations by providing them with information, helping them share knowledge, enabling them to work better together, and so on. This system embodies their intelligence (or at least the part of it that applies to additional mental health care benefits) in an information system, which can then make the decisions that people would otherwise have made.

Section 12.2 Review Questions 1.

What is artificial intelligence?

The book offers two definitions on page 535, citing their sources there: “Behavior by a machine that, if performed by a human being, would be called intelligent.”

“The study of how to make computers do things at which, at the moment, people are better.”

Students may find other definitions, such as this from Wikipedia: “a branch of computer science and engineering that deals with intelligent behavior, learning, and adaptation in machines,” or this from the Oxford American Dictionary: “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as [list].”

(U.S. Supreme Court Justice Potter Stewart’s 1964 opinion on pornography, “I shall not today attempt further to define … and perhaps I could never succeed in intelligibly doing so. But I know it when I see it” has been applied to AI. It is, however, unlikely to help a student answer this question.)

2.

What are the major capabilities of artificial intelligence?

There are several ways to approach this question. One, the only one based entirely on the content of this section, is to use the list of “signs of intelligence” on p. 536 (see below), though it does not specifically associate those signs with artificial intelligence.

• Learning or understanding from experience • Making sense out of ambiguous or contradictory messages • Responding quickly and successfully to a new situation (i.e., different responses, flexibility) • Using reasoning in solving problems and directing conduct effectively • Dealing with perplexing situations • Understanding and inferring in ordinary, rational ways • Applying knowledge to manipulate the environment • Thinking and reasoning • Recognizing the relative importance of different elements in a situation

Other answers could be based on Technology Insights 12.2 on p. 539: (AI is permanent, offers ease of duplication and dissemination, can be less expensive than natural intelligence, is consistent and thorough, can be documented, can execute some tasks faster than a human, and can perform some tasks better than most people). Answers may also be on the list of AI application areas beginning on p. 540: (expert systems, natural language processing, speech understanding, robotics and sensory systems, computer vision and scene recognition, intelligent CAI, automatic programming, neural computing, game playing, etc.). Students may provide other possible answers.

3.

What are the major characteristics of artificial intelligence?

Artificial intelligence is generally characterized by a combination of symbolic processing, heuristics (see Review Question 4 below), inferencing and machine learning.

4.

What are heuristics? Give an example.

Heuristics are “rules of thumb” learned from experience. The example in the text (p. 537) is that of a salesperson who, after visiting one client, proceeds to the next nearest one on the list. Another is this rule for finding an inexpensive fuel station: note the price at the first one you pass, then stop at the first one after that which is less expensive.

Section 12.3 Review Questions 1.

What are the major advantages of artificial intelligence over natural intelligence?

AI is permanent, offers ease of duplication and dissemination, can be less expensive than natural intelligence, is consistent and thorough, can be documented, can execute some tasks faster than a human, and can perform some tasks better than most people.

2.

What are the major disadvantages of artificial intelligence compared to natural intelligence?

Natural intelligence is creative, whereas AI is uninspired. Natural intelligence can benefit from direct sensory experience, whereas most AI systems must work with symbolic representations.

3.

What are the major characteristics of artificial intelligence?

See the answer to Review Question 3 of Section 12.2.

4.

Describe an application of artificial intelligence.

One application of artificial intelligence is in expert systems. An application of that technology, Cigna’s eCare system, is described in the opening vignette of this chapter. Its role and benefits are described briefly in the answer to Review Question 2 of Section 12.1.

5.

What technology can help move call centers that have been outsourced to other countries back to the United States?

Automatic speech recognition. (See Application Case 12.4, p. 542.)

6.

Define NLP and describe an application of NLP. NLP stands for Natural Language Processing, the ability to interpret strings presented in the user’s language. (The text refers to English, but the concepts apply equally to other languages.)

An application of NLP is interpreting queries such as “Graph sales by region for the past three years,” followed perhaps by “Now do it for four years,” where the system should deduce that “it” refers to the command “Graph sales by region.”

7.

Define speech recognition and discuss one application of the technology.

Speech recognition (or “speech understanding,” as the section heading in the text reads) is the ability to translate spoken utterances into strings of text in the speaker’s language.

It may then be followed by NLP to interpret those strings, but the two processes are conceptually distinct.

One application of the technology is collecting basic information from customers who call a company so that the call can be routed appropriately and/or a computer system can make information available to the person who will handle the call.

8.

Define intelligent agent and describe one application of intelligent agents.

An intelligent agent is a program that conducts certain tasks automatically on behalf of the computer’s user. (The book describes them as “small” programs. This is typically true, but is not necessarily part of their definition.)

One application of intelligent agents is in prioritizing e-mail messages based on their senders and (hopefully intelligent) analysis of the text they contain.

Section 12.4 Review Questions 1.

What is an ES? Expert systems “are computer-based information systems that use expert knowledge to attain high-level decision performance in a narrow problem domain” (p. 545).

I happen to like this definition a lot, though students may find others in other sources.

2.

Explain why we need ES. Companies need (Need? In any case, can benefit from) expert systems for several reasons: to preserve professional expertise when an expert leaves; to document knowledge for retention, examination and improvement; for training new employees and disseminating knowledge in an organization; and to transfer knowledge less expensively than transferring a human expert.

3.

What are the major features of ES? Expertise, symbolic reasoning, deep knowledge and self-knowledge.

4.

What is expertise? Provide an example of expertise.

Expertise is “the extensive, task-specific knowledge that experts possess” (p. 546).

An example of expertise is the ability of an experienced automobile mechanic to go directly to the source of “that funny sound, sort of like ‘kabump, kabump,’ when I turn right.”

5.

Define deep knowledge and give an example of it.

Deep knowledge is “complex knowledge not easily found among non-experts” (p. 547).

Deep knowledge is usually considered to be about the central ideas of a topic. It involves establishing complex connections to these concepts.

An example of deep knowledge is a physician’s understanding of a disease in terms of the underlying mechanism through which it affects the body and can be cured, versus a lay person’s superficial knowledge of the symptoms (e.g., headache). These can then only be alleviated (e.g., by aspirin that will not deal with the underlying cause of the condition).

Section 12.5 Review Questions 1.

What problem domain is MYCIN designed for solving?

MYCIN was designed to diagnosis of bacterial blood infections.

2.

Find two applications of ES in finance and describe their benefits.

Open-ended answer, which depends on the applications each student or group finds.

3.

Find two applications of ES in marketing and describe their benefits.

Open-ended answer, which depends on the applications each student or group finds.

4.

Find two applications of ES in homeland security and describe their benefits.

Open-ended answer, which depends on the applications each student or group finds.

Section 12.6 Review Questions 1.

Describe the ES development environment and contrast it with the consultation environment.

The development environment is used by (or in conjunction with) the expert to introduce knowledge into an expert system’s knowledge base. The consultation environment is used by a non-expert to apply that knowledge, along with facts pertaining to the current situation, to a specific problem.

2.

List and define the major components of an ES. The major components of ES are: knowledge base, inference engine, and user interface.

3.

What are the major activities performed in the ES blackboard (workplace)?

(Technically, no activities are performed there, since it’s a storage place, but we can discuss the types of information stored in it.)

The blackboard stores information about the current problem, including input data, intermediate hypotheses and decisions. These decisions can include a plan of attack on a problem, a list of actions awaiting execution, and alternative solutions.

4.

What are the major roles of the explanation subsystem?

The roles are tracing conclusions to their sources, in terms of rules and data.

5.

Describe the difference between a knowledge base of an ES and an organizational knowledge base.

An organizational knowledge base contains unstructured knowledge about problem areas.

It must be interpreted by people, often using a variety of software packages to deal with a variety of documents, before it is useful. The knowledge base of an ES contains knowledge that has been structured into the form (usually, but not necessarily, if-thenelse) of specific rules that can be applied directly to a problem. The only software needed is an inference engine with its associated user interface.

(This difference was brought out in the text on p. 494, but if you chose to skip Chapter 11 students may not have seen it there. In that case, though, they probably wouldn’t be familiar with organizational knowledge bases in the first place, and you probably wouldn’t assign this question.)

Section 12.7 Review Questions 1.

Write down two production rules that can represent the knowledge of repairing your car.

IF engine does not turn over AND headlights illuminate, THEN battery is good.

IF engine does not turn over AND headlights do not illuminate, THEN battery may be dead.

2.

Describe how ES perform inference.

Inference involves going through the knowledge base rule by rule. For each rule examined, the expert system determines whether the information in its antecedent (the IF part) makes it true or false. It then knows if its consequent (the THEN part, perhaps also the ELSE part) is true or false. It uses that information to work through other rules. The two approaches to choosing the order in which to examine and process rules, forward and backward chaining, are described in the answer to the next question.

3.

Describe the reasoning procedures of forward chaining and backward chaining.

The forward chaining approach is to determine what you know, apply that information to the rules, and see what conclusion(s) you reach. (This is also called data-driven reasoning.) It selects rules by determining that the system knows the truth or falsity of their antecedents (the IF parts). It then derives further facts from their consequents (the THEN parts). These new facts allow it to determine the truth or falsity of the antecedents of more rules. It can then evaluate those rules and determine still more facts from their consequents. The process continues until the system reaches its overall conclusion.

The process of backward chaining is to establish a goal, determine what must be found out to reach that goal, and try to find that knowledge. (This is also called goaldirected or goal-driven reasoning.) An expert system that uses backward chaining selects rules by examining the variables contained in their consequents. It then tries to determine the values of variables used in their antecedents to decide if the rule applies or not.

Section 12.8 Review Questions 1.

Describe a sample ES application for prediction.

One prediction area that is suitable for expert systems is weather prediction. This system could use rules about weather patterns such as “if there is a cold front over New York State and the wind is from the west, temperatures in Massachusetts will drop in three to six hours.” If the rules are derived from the knowledge of expert meteorologists, its predictions could be about as accurate as theirs.

2.

Describe a sample ES application for diagnosis.

One diagnosis area that is suitable for expert systems is fault diagnosis in complex mechanical or electrical equipment. It would use rules based on the knowledge of experienced repair personnel to narrow down the cause of a failure to the optimal replaceable unit (ORU). Such a system might have to rely on people to carry out some of the required physical tests and report the results.

3.

Describe a sample ES application for the rest of the generic ES categories.

Open-ended answer, depending on which of the eight categories is or are chosen.

Answers should resemble the above two in their general form.

Section 12.9 Review Questions 1.

Describe the major steps in developing rule-based ES. Developing rule-based expert systems typically involves six major steps: Step 1. Defining the nature and scope of the problem Step 2. Identifying proper experts Step 3. Acquiring knowledge Step 4. Selecting the building tools Step 5. Coding the system Step 6. Evaluating the system

2.

What are the necessary conditions for a good expert?

According to the text (p. 560): thorough understanding of problem solving in the domain, an understanding of the role of ES and of decision support technology, and good communication skills to transfer his/her knowledge. Students may find other (generally longer) lists in other sources.

3.

Compare three different types of tools for developing ES. Programming from scratch: The most work, but the only way to get a truly customized ES and also a good learning process for budding technical specialists. In most business situations, not worth the effort over using a shell unless the ES is to be a very small part of a larger system.

Using an ES shell: A good choice if one can be found to meet the requirements, as will usually be possible. Little is lost in performance over custom programming, since the inference engines of shells are carefully hand-coded by experts in inference methods and have, for popular shells, been refined over several generations of software.

Using a turn-key package: usually the most practical choice if a suitable one exists, but it often will not.

4.

List the criteria for choosing a development tool.

Cost (total, not just purchase price); functionality, compatibility with other software already in use; and vendor reliability and support.

5.

What is the difference between verification and validation of an ES? Verification confirms that the system follows the rules obtained from the expert.

Validation confirms that the system solves the problem it was intended to solve.

Section 12.10 Review Questions 1.

Describe the major benefits of using ES. The list of benefits on pp. 564–565 of the book includes increased output and productivity, decreased decision-making time, increased process and product quality, reduced downtime, capture of scarce expertise, flexibility, easier equipment operation, elimination of the need for expensive equipment, operation in hazardous environment, accessibility to knowledge and help desks, ability to work with incomplete or uncertain information, provision of training, enhancement of problem solving and decision making, improved decision-making processes, improved decision quality, ability to solve complex problems, knowledge transfer to remote locations, and enhancement of other information systems. Some of these might not be considered “major” in the sense of this question, and their applicability varies from situation to situation, but they are all potential benefits of ES at least some of the time.

What you expect in terms of a description is up to you. I would encourage you, given the length of this list, to be specific about your requirements in this area. Your

students thank me in advance for urging you to request short descriptions if you assign the whole list.

2.

Describe some of the limitations of ES. Limitations of expert systems include: • Knowledge is not always readily available.

• It can be difficult to extract expertise from humans.

• The approach of each expert to a situation assessment may be different, yet correct. (In some situations it is possible to turn this to an advantage.)

• It is difficult, even for a highly-skilled expert, to abstract good situational assessments when under time pressure.

• Users of ES have natural cognitive limits.

• ES work well only within a narrow domain of knowledge.

• Most experts have no independent means of checking whether their conclusions are reasonable.

• The vocabulary, or jargon, that experts use to express facts and relations is often limited and not understood by others.

• Help is often required from knowledge engineers who are rare and expensive.

This can make ES construction costly.

• Lack of trust on the part of end users may be a barrier to ES use.

• Knowledge transfer is subject to a host of perceptual and judgmental biases.

• ES may not be able to arrive at conclusions in some cases. (In the opening vignette of this chapter, though, an ES that handled “only” 30+ percent of all cases was still useful. An ES must know when a case is beyond its ability and have a way to refer those to humans.)

• ES, like human experts, sometimes produce incorrect recommendations.

(The comment at the end of Review Question 1 about the word “describe” applies here as well.)

3.

Describe the success factors of ES. Success factors include these: • The level of knowledge must be sufficiently high.

• Expertise must be available from at least one cooperative expert.

• The problem to be solved must be mostly qualitative (fuzzy) and not purely quantitative (otherwise, a numeric approach should be used).

• The problem must be sufficiently narrow in scope.

• ES shell characteristics are important. The shell must be of high quality and naturally store and manipulate the knowledge.

• The user interface must be friendly for novice users.

• The problem must be important and difficult enough to warrant development of an ES (but it need not be a core function).

• Knowledgeable system developers with good people skills are needed.

• The impact of ES as a source of end-user job improvement must be considered.

•

The impact should be favorable. End-user attitudes and expectations must be considered.

• Management support must be cultivated.

• End-user training programs are necessary.

• The organizational environment should favor adoption of new technology.

• The application must be well-defined and structured, and it should be justified by strategic impact.

(The comment at the end of Review Question 1 about the word “describe” applies here as well.)

Section 12.11 Review Questions 1.

What are the benefits of deploying an ES on the Web? One of the ways expert systems provide benefits is by deploying knowledge to many users. The Web is one, and today in many situations the best, way to reach a large user community.

(Chapter 14 will discuss the relationship between the Web and intelligent systems in depth.)

2.

How can an ES help a decision maker use the Web to find relevant information?

The difference in effectiveness between expert and novice searchers on the Web is dramatic. This is especially true when the search is in the expert’s field of knowledge.

Expert systems whose rules include those an expert searcher might use to find information can help novice searchers find it more quickly than they would otherwise be able to.

3.

Visit exsys.com and run two of the demo systems. What comments do you have about them?

Open-ended answer depending on which systems are chosen. Expect students to comment on the polished user interfaces, which use a variety of input methods and do not require any technical expertise on the user’s part. (Some of the demos, such as the one that helps determine if an employee will successfully transfer his/her family to Indonesia, only show the user interface. Others generate real (or real-seeming) results: choosing a dinner restaurant in Albuquerque, choosing a visa type to enter the United States and more. If you wish to exclude demos that only show the interface, you should make this clear in advance.)

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Compare numeric and symbolic processing techniques and give an example to illustrate their differences.

Numerical processing involves the execution of tasks on computers where numbers and mathematical relationships are in the core of the processing. For example, preparing a payroll involves the computation of wages and deductibles. Symbolic processing involves attributes and non-algorithmic processing. For example, the production systems discussed in this chapter are ideal vehicles for symbolic processing, where information is stored as IF-THEN rules.

2.

Do you agree with the statement that using speech communication as the user interface could increase people’s willingness to use ES? Why or why not?

Open-ended answer. It depends on the student’s experience with speech recognition technology thus far. In 2006, many people are frustrated with it because of its unreliability and the difficulty of training the software to individual speech patterns and dialects. Also, people who want to use a system such as MySpace or eBay do not find its lack of speech communication a barrier, suggesting that it is not an significant factor in unwillingness to use ES either.

3.

It is said that powerful computers, inference capabilities, and problem-solving heuristics are necessary but not sufficient for solving real problems. Why? They are not sufficient because specific problem-related knowledge is required as well.

Each problem uses different knowledge and often requires its own approach.

4.

Explain how the Web improves the benefit–cost ratio of ES and enables systems that otherwise are not justifiable.

The Web makes it easier to disseminate knowledge and its access throughout an organization. Most people have Web browsers (or can get them easily) and are connected to the Internet. Thus it is relatively cheap to extend ES to more users over the Web. Since the benefit of a system is positively related to the size of its user community but its costs are largely fixed, enlarging its user community will improve its cost-benefit ratio.

(Chapter 14 will discuss the relationship between the Web and intelligent systems in depth.)

5.

Explain the relationship between the development environment and the consultation (i.e., runtime) environment.

See answer to Review Question 1 of Section 12.6.

6.

Explain the difference between forward chaining and backward chaining and describe when each one is most appropriate.

The difference was explained in the answer to Review Question 3 of Section 12.7.

Backward chaining tends to ask for less data than forward chaining, but to examine more rules. Forward chaining tends to ask for more data, but uses the data to eliminate possibilities and therefore to process fewer rules. (An expert system can combine both methods.)

The choice between backward and forward chaining should lean toward the one that narrows the tree of choices faster. When there are many rules that start from input data, only a few of which apply in any given situation, forward reasoning works well because the first step eliminates most of the possibilities. When rules near the top of the tree can be eliminated without reaching all the way down the tree, backward chaining works better.

Configuring a computer system to meet user requirements is a good forward chaining application. Here, all system components—processors, disk drives, cabinets, power supplies, communications gear—must be addressed, so every top-level rule always applies and every major branch of the rule tree must be used. It is more efficient to start with the requirements. They will eliminate groups of bottom-level rules as irrelevant in a particular case.

Backward chaining, on the other hand, is good for problem diagnosis. Here, a few top-level questions (“Is the warning light on?” “Does your back hurt?” “Are their brown spots on its leaves?”) isolates the problem to a set of related possibilities. The system can then ignore rules that have to do with other possibilities, along with all the inputs that those rules might require.

(This information is not from the text, which does not address this issue. If you assign this question, explain to your students that they will have to research the answer in other places.)

7.

What kind of mistakes might ES make and why? Why is it easier to correct mistakes in ES than in conventional computer programs? (a)

(b)

ES make mistakes due to lack of “common sense,” encountering problems that are outside their range of expertise, and errors made by the original expert(s) in providing rules to the system.

It is easier to correct mistakes in ES than in conventional computer programs because, in an ES, each rule is an independent “chunk of knowledge.” It can be changed without directly affecting other rules much like a row in a database table can be changed without affecting the content of other rows. In a conventional program, by contrast, the sequence of execution moves through the code in a controlled way. Any change to the code can affect the sequence in unpredictable ways and thereby cause errors. Extensive testing is required to make sure that a change in one area did not affect other areas.

8.

Review the limitations of ES discussed in this chapter. From what you know, which of these limitations are the most likely to still be limitations in the year 2100? Why? Most of the limitations relate to human factors: the difficulty of extracting knowledge from people, differences among different experts’ methods, users’ cognitive limits …

Human nature is unlikely to change significantly over the next 90 or so years. These limitations are therefore unlikely to change much either.

Acceptance issues may change, however. As expert system technology becomes more widely used and the management issues involved in implementing it become better understood, attitude factors will no longer pose an obstacle.

9.

An ES for stock investment is developed and licensed for US $1,000 per year. The system can help you identify the most undervalued securities on the market and the best timing for buying and selling the securities. Will you order a copy as your investment adviser? Explain why or why not.

Such a system is unlikely to be successful once it becomes generally available. The reason is that stocks it identifies as undervalued will be bought, thus raising their price until they are no longer undervalued. The first few people to purchase a stock so identified would make money. The rest would not. Unless I thought I could get to the market ahead of all the other thousands or millions of people who use this system, it wouldn’t work. (I might consider buying it to know which stocks to sell short at the end of the day, thinking that many of the people who followed the system’s recommendation will dump the stock as soon as they realize what happened and thus depress its price from its artificially inflated value, but that’s a separate issue.)

10.

Given the current status of the Web, discuss how it is changing the availability of ES and how it is being used to embed expertise in other systems.

The Web allows us to deploy ES directly, to provide expertise to users by running directly on the Web. The Exsys demos work that way (see Review Question 3 of Section 12.11). Help desk software, essentially ES, runs on the Web pages of many technical support groups. Ginesys Corp. has such software as a part of its K-Vision ES shell.

Furthermore, any time expertise is required for customer support (e.g., in advertising to help a customer choose a product to order or use), it can be provided over the Web by an ES. (See also the answers to Review Questions 1 and 2 of Section 12.11.)

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

Describe the role of business rules in Farm Bureau and why those rules are very important.

The business rules at Farm Bureau define the insurance policies the company wants to issue: which policies should be issued, what should be included in them and what the premiums should be.

These rules are important because writing insurance policies is Farm Bureau’s core business. Writing policies that are both profitable and competitive is vital to its survival.

2.

Describe the benefit of using the rule-based system at Farm Bureau and explain why the system can generate these benefits.

One benefit was an increase in the fraction of transactions that can be processed without human review from 10–12 percent to 60 percent (about 250,000 of 450,000 in 2005).

Another benefit, perhaps a result of the first, is a reduction of about 75 percent in transaction time. This has allowed Farm Bureau underwriters to focus on more important areas and customer service representatives to concentrate on building relationships with policyholders. That, in turn, presumably has a positive impacts on Farm Bureau’s revenue and profitability.

The system is able to generate these benefits because it updated the rules, combined and unified the rules its three predecessor firms used individually, and moved them out of legacy code into an expert system where they would be more manageable.

(For more on why they are more manageable, see part (b) of the answer to Discussion Question 7.)

3.

Evaluate potential tools for implementing such a rule-based system and list the criteria that you would use in tool selection.

My first decision would be to choose among custom programming, a shell and a packaged application. I would choose a shell, because the system is ordinary enough in its technical aspects not to require custom programming, and unique enough from a business standpoint that I can be certain a package will not exist. Next, I would use the factors on p. 563 to choose a specific shell.

4.

Find a proper application outside the financial services domain (such as manufacturing or retailing) and evaluate whether the rule-based system will generate the same level of benefits as those in the Farm Bureau system and explain why.

Open-ended answer, which will depend on the application area each student or group selects.

CHAPTER

13

Advanced Intelligent Systems

Learning Objectives for Chapter 13 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand machine-learning concepts Learn the concepts and applications of case-based systems Understand the concepts and applications of genetic algorithms Understand fuzzy set theories and their applications in designing intelligent systems Understand the concepts and applications of natural language processing (NLP) Learn the concepts, advantages, and limitations of voice technologies Learn about integrated intelligent support systems

CHAPTER OVERVIEW This is the second of three chapters in the “Intelligent Systems” module of the book. The previous chapter covered expert systems. Here we look at other aspects of intelligent technology, prior to seeing how it all plays out over the Web in the next chapter. While the specific types of intelligent systems discussed here are diverse and may seem unrelated, they should all be part of the DSS developer’s toolkit, so he or she will recognize their areas of applicability and be able to consider using them or learn more about them when such a situation arises.

CHAPTER OUTLINE 13.1 OPENING VIGNETTE: IMPROVING URBAN INFRASTRUCTURE MANAGEMENT IN THE CITY OF VERDUN A. PROBLEM B. SOLUTION C. RESULTS  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

13.2

MACHINE-LEARNING TECHNIQUES A. MACHINE-LEARNING CONCEPTS AND DEFINITIONS B. LEARNING C. MACHINE-LEARNING METHODS  Section 13.2 Review Questions

13.3

CASE-BASED REASONING (CBR) A. THE BASIC IDEA AND PROCESS OF CBR B. DEFINITION AND CONCEPTS OF CASES IN CBR C. BENEFITS AND USABILITY OF CBR D. THE PROCESS OF CBR E. USES, ISSUES AND APPLICATIONS OF CBR ⧫ Application Case 13.1 CBR Improves Jet Engine Maintenance, Reduces Costs F. SUCCESS FACTORS FOR CBR SYSTEMS G. TOOLS FOR BUILDING CBR ⧫ Technology Insights 13.2 Tools for Developing CBR Systems  Section 13.3 Review Questions

13.4

GENETIC ALGORITHM FUNDAMENTALS A. GENETIC ALGORITHM PROCESS AND TERMINOLOGY B. HOW A GENETIC ALGORITHM WORKS 1.

Limitations of Genetic Algorithms  Section 13.4 Review Questions

13.5

DEVELOPING GENETIC ALGORITHM APPLICATIONS ⧫ Application Case 13.3 Genetic Algorithms Schedule Assembly Lines at Volvo Trucks North America  Section 13.5 Review Questions ⧫ Technology Insights 13.4 Genetic Algorithm Software

13.6

FUZZY LOGIC FUNDAMENTALS A. FUZZY LOGIC APPLICATIONS 1.

Fuzzy Logic Applications for Manufacturing and Management 2.

Fuzzy Logic Applications for Business: Examples  Section 13.6 Review Questions

13.7

NATURAL LANGUAGE PROCESSING (NLP) A. TYPES OF NATURAL LANGUAGE PROCESSING 1.

Some Problems That Make NLP Difficult 2.

The Current NLP Technology 3.

Applications of NLP B. MACHINE TRANSLATION 1.

Translation of Content to Other Languages

 13.8

Section 13.7 Review Questions

VOICE TECHNOLOGIES A. VOICE RECOGNITION AND UNDERSTANDING 1.

Advantages of Voice Technologies 2.

Limitations of Speech Recognition and Understanding B. VOICE SYNTHESIS C. VOICE TECHNOLOGY APPLICATIONS 1.

Voice Portals  Section 13.8 Review Questions

13.9

DEVELOPING INTEGRATED ADVANCED SYSTEMS A. FUZZY NEURAL NETWORKS B. GENETIC ALGORITHMS AND NEURAL NETWORKS ⧫ Application Case 13.7 Hybrid ES and Fuzzy Logic System Dispatches Trains  Section 13.9 Review Questions Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises ⧫ End of Chapter Application Case: Barclays Uses Voice Technology to Excel  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

This chapter covers a range of topics under the general heading of “advanced intelligent systems.” They’re not necessarily more “advanced” than expert systems, but they are definitely not as common. Fortunately, this doesn’t mean they’re harder to learn.

Specifically, this chapter covers case-based reasoning, genetic algorithms, fuzzy logic, natural language processing, and voice technologies (which are related to, but not the same as, NLP). It closes with examples of how more than one intelligent technology can be used in the same system.

This chapter covers a series of topics with a common theme, rather than a single topic like the last few chapters. One opening vignette, therefore, can’t introduce the chapter as a whole. This one introduces case-based reasoning, the topic of Section 13.3, but not the other topics discussed in this chapter. You may want to provide your own introductions to them.

The opening vignette is followed by a general discussion of machine learning. The connection between machine learning and the rest of the chapter is that two machine learning methods (case-based reasoning, genetic algorithms) are discussed later on. However, another one of them (neural networks) was covered earlier in the book (primarily in Chapter 8), one

(statistical regression) is usually a topic for other courses, and some of the others are not discussed at all. Furthermore, some of the topics that come up later in the chapter (fuzzy logic, NLP and voice technologies) are unrelated to machine learning. Use this section to create a context for all these topics and explain their relationships.

Section 13.3, on case-based reasoning, should go smoothly if students read the opening vignette. You may want to connect this topic to that of knowledge management in Chapter 11.

Case-based reasoning is, after all, about extracting knowledge from unstructured documents.

Many of the issues discussed in that chapter apply here as well. Others, such as the need to motivate employees to enter documents containing knowledge into the knowledge base, may not apply since the documents needed for CBR will be in a narrow area and will often already be in machine-readable form.

Genetic algorithms seem a bit esoteric because they are phrased in terms of genes, chromosomes, mutations, generations and other words that make them sound like something out of a science-fiction novel. They are, in fact, much simpler than that. They create complex structures from very simple building blocks (much as nature does with us). The trick is to see the connection between the chromosome and the solution. If you replace the term “chromosome” with something like “solution description” (or solution vector, if your students lean toward mathematics) it may make them more approachable. The basic ideas aren’t difficult. You’re just trying different solutions looking for a good one, using the GA formalism as a way to figure out which solutions to try next.

Fuzzy logic is another field that suffers from a name that could perhaps have been chosen with more foresight. (Its originator, Lofti Zadeh, has said as much.) It’s not really an “advanced intelligent system,” but it is a form of mathematical reasoning that shows up in many aspects of decision support/BI and especially in intelligent systems, so it has to be covered somewhere.

This is as good a place as any. If for any reason you don’t cover this part of the book, you might want to go over this section anyhow.

The opening example in this section, that of fuzzy set membership for a tall man (despite the politically correct “person” in the section title, the example is specifically about men), can inadvertently create a misleading impression of how fuzzy set memberships work. Both the numbers in the table on p. 595 and the heights of the three membership curves in Figure 13.7 happen to add up to 100 percent. This is just a coincidence, but students may not realize this unless you point it out. We could add, say, a set for “very tall.” Now a 73"-tall man could have 100 percent membership in the set of tall men and also, say, 30 percent membership in the new set of very tall men for a total of 130 percent.

Also in this section, the book mentions (p. 595, second paragraph from the end) that a set membership value can be expressed in an expert system in the same form as a probability-based confidence factor. This is true, of course. However, most expert system shells would have trouble dealing with it in fuzzy logic terms. To use fuzzy logic in an expert system (often a good idea), it is necessary to choose a shell that supports it or to code the calculations by hand. Fortunately, the latter shouldn’t be hard to do.

Natural language processing has been a focus of AI work since its early days. Progress has been slow, largely for reasons discussed on p. 599. There has been recent progress in machine translation, fueled in part by U.S. interest in automatic translation from Arabic for document analysis and to support people working in Arabic-speaking areas. A trend is greater use of context in translating documents: instead of trying to analyze each word, a translation program looks for phrases in a large database of correctly translated work. A word that meant X in the

same context once is likely to mean X in a new document. It involves more than this, but this approach (though some purists deride it as “brute force”) shows promising results in practical situations. It could make a good topic for student projects or at least a Web search. (This approach is noted in Online File W13.3 as parallel corpora but is not discussed further.)

Two notes on this section: • The text gives a URL for more information on NLP in Wikipedia near the bottom of p. 598. The actual entry is without “The_:” en.wikipedia.org/wiki/Natural_Language_Processing. In December 2006 the URL as printed in the book leads to a redirection page, which sends the user to the page given above. If the redirection stops working, you may need the direct URL. • The text, near the top of p. 599, notes two aspects of NLP: understanding and generating natural language. However, the rest of the section concentrates entirely on understanding. If students want to learn more about generating natural language, or if you want to include it in your course, there are many resources on the topic on the Web and elsewhere—but, when all is said and done, it doesn’t have a great deal to do with DSS/BI. Speech recognition often goes along with NLP, but it is important for students to understand the difference. A system can recognize that a caller has said “one” or “two” without having any idea that these are numbers. Most students will have had some personal experience with voice recognition systems as used in automated telephone attendant applications. The likelihood of a DSS/BI specialist ever being called on to design one is small, so all they have to know here is that they can be useful parts of the DSS/BI user interface, especially from mobile devices.

The final section of this chapter is about integrating all these elements. The two examples it uses are combining neural networks with either fuzzy logic or genetic algorithms. The possibilities, however, are broader than that. Discussion Question 5 at the end of the chapter asks students to look at all possible combinations. While this may be a bit much to ask, their perspective as they leave this chapter should at least include the awareness that they exist.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 13.1 Review Questions 1.

Explain how CBR supports, rather than replaces, managers in the Verdun system.

The CBR system in this case is a true decision support, as opposed to decision making, system. The decisions are sufficiently unstructured that they cannot be programmed.

While the system can suggest a proposed solution, managers must make the final decision.

2.

Describe the CBR process in the Verdun system.

The process consists of the following steps: Step 1. Historical cases are entered into the system’s case library (repository).

Step 2. A new case is entered into the system for analysis.

Step 3. The system finds similar complaints in the library.

Step 4. The uses those similar cases to classify and prioritize the new complaint.

Step 5. The new complaint is routed to the proper place(s) for resolution.

Step 6. The system may present one or more possible solutions to the resolving manager(s).

Step 7. The manager, prior to resolving the new case, considers priorities and available resources.

Step 8. The new case and its resolution are added to the system’s case library.

3.

What were the specific capabilities and benefits of the CBR in the Verdun system?

Its capabilities were storage, management and retrieval of historical complaint information. and the ability to use the information it retrieves to route new complaints, prioritize them, and propose solutions to them.

Its benefits were improvements in the delivery, performance and coordination of municipal services. Specifically, interventions were reduced by grouping similar complaints (same type occurring in the same area at the same time) and complaints were “resolved faster and [more] frequently with fewer resources.”

4.

Why are historical cases a good knowledge source?

Because, presumably, historical cases were analyzed by responsible managers and resolved using appropriate prioritization and procedures. Using them as precedents therefore reflects those managers’ experience, those priorities and those procedures. (In selecting cases for the repository, it might be desirable to screen out cases which, in the opinion of some responsible person or people, do not meet these criteria.)

5.

Can an expert system (ES) be used instead of CBR? Why or why not?

In theory it could, if the information in the cases could be reduced to the form of rules whose antecedents could be evaluated on the basis of characteristics of a new case. As a practical matter, it would be difficult or impossible to do so.

Section 13.2 Review Questions 1.

Define machine learning.

“A family of methods that attempt to allow machines to acquire knowledge for problem solving by showing them historical cases” (pp. 577–578). Students may find other correct definitions in other sources.

2.

What is learning?

“A process of self-improvement and thus an important feature of intelligent behaviors” (p. 578). Students may find other correct definitions in other sources.

3.

What are the differences between supervised learning and unsupervised learning?

The major difference is that the outcomes of cases used in supervised learning are known, but those in unsupervised learning are not. In supervised learning, the system is to learn

how to replicate the human logic that went into generating the known outcomes. In unsupervised learning, the system is to figure out something we do not yet know.

4.

List several techniques for supervised learning and unsupervised learning.

Techniques for supervised learning include explanation-based learning, statistical regression, inductive learning, case-based reasoning, neural networks and genetic algorithms.

Techniques for unsupervised learning include neural networks (again), genetic algorithms (again) and clustering.

5.

List five machine-learning methods.

The shaded box on p. 579 lists eight areas. A case can be made that #5, natural language processing, is not a machine-learning method but rather an area to which machine learning methods can be applied. The remaining seven items are inductive learning, casebased reasoning, neural computing, genetic algorithms, cluster analysis, statistical methods and explanation-based learning. A correct answer can list any five of these (or, if you are tolerant of students who do not interpret instructions literally, six or seven).

Section 13.3 Review Questions 1.

Define CBR. A machine learning method that “adapts solutions used to solve old problems for use in solving new problems” (p. 580).

2.

Describe the four steps of the CBR process. Briefly discuss each step.

Step 1. Retrieve. Given a target problem, retrieve from a library of past cases the most similar cases that are relevant to solving the current case.

Step 2. Reuse. Map the solution from the previous case to the target problem. Reuse the best old solution to solve the current case.

Step 3. Revise. Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise the case.

Step 4. Retain. After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in the case library.

(There is more detail on these steps in Online File W13.1, which students should read, but the level of detail there is not required to answer this question.)

3.

List five benefits of CBR. The bullet list on p. 583 includes these eight: • Knowledge acquisition is improved. It is easier to build, simpler to maintain, and less expensive to develop and support knowledge acquisition.

• System development time is faster than when using manual knowledge acquisition.

• Existing data and knowledge are leveraged.

• • • • •

Complete formalized domain knowledge (as required with rules) is not required.

Experts feel better discussing concrete cases (not general rules).

Explanation becomes easier. Rather than showing many rules, a logical sequence can be shown.

Acquisition of new cases is easy (i.e., can be automated).

Learning can occur from both successes and failures.

A correct answer can list any five of these (or, if you are tolerant of students who do not interpret instructions literally, as many as all eight).

4.

Comment on the usability of CBR. The subsection headed “benefits and usability of CBR” does not specifically address this review question. However, it refers to several real-world applications of CBR, which collectively suggest that CBR is quite usable in practice. A Web search on terms such as “usability” and “CBR” will find many more examples that point to the same conclusion.

5.

List three success factors of CBR. The shaded box on pp. 585–586 lists seven factors: • Determine specific business objectives • Understand your end users and customers • Design the system appropriately • Plan an ongoing knowledge-management process • Establish achievable returns on investment (ROI) and measurable metrics • Plan and execute a customer-access strategy • Expand knowledge generation and access across the enterprise A correct answer can list any five of these (or, if you are tolerant of students who do not interpret instructions literally, six or all seven).

Section 13.4 Review Questions 1.

Define genetic algorithm.

“A program that attempts to find optimal solutions to problems when it is possible to specify the criteria that can be used to evaluate … solutions. Genetic algorithms are sets of computational procedures that conceptually follow steps inspired by the biological processes of evolution. Better and better solutions evolve from previous generations, until an optimal or near-optimal solution is obtained.” (p. 587) There are also definitions in sources such as http://en.wikipedia.org/wiki/Genetic_algorithm.

2.

Describe the learning process in genetic algorithms. Why is it similar to the biological learning process?

(Note that “learning” in this question is a misprint, to be corrected in the book’s second printing. It should read “evolution.”)

The learning process in genetic algorithms is based, after appropriate initialization, on four steps that are repeated until a “sufficiently good” solution is found: • Evaluate the fitness function for all currently proposed solutions • Select parents to reproduce in the next generation • Apply crossover to produce offspring • Apply random mutation to those offspring to produce the next set of proposed solutions This process resembles the biological evolution (see note at the beginning of this answer) process in that “fit” parents (by some biological or sociological definition) can reproduce, offspring contain a mix of the parents’ genes, offspring may have random mutations, and “survival of the fittest” repeats from generation to generation.

3.

Describe the major genetic algorithm operators.

Reproduction, in which high-scoring solutions are chosen as the basis for the next generation.

Crossover, in which each member of the next generation starts as a random combination of elements of the parent generation (usually bits from a bit-string representation of each solution).

Mutation, in which a (usually small) number of solution bits is randomly changed.

Section 13.5 Review Questions 1.

List major areas of genetic algorithm application.

Application areas for genetic algorithms noted in the text (p. 592) include dynamic process control, induction of optimization rules [the second “of” in this list item is a misprint], discovery of new connectivity topologies (e.g., connections in neural network design), simulation of biological models of behavior and evolution, complex design of engineering structures, pattern recognition, scheduling, transportation and routing, layout and circuit design, telecommunication, and graph-based problems (which may also be in one or more of the other categories).

A correct answer may include any number of these as well as other areas found in other sources. Which of them, from whatever source, are considered “major” is a matter for instructor judgment.

2.

Describe in detail three genetic algorithm applications.

3.

Open-ended answer. One application is described in sufficient detail to answer this question in Application Case 13.1 (p. 593). Others can be found in the references preceding that case or by a Web or library search.

Describe the capability of Evolver as an optimization tool.

Evolver (http://www.palisade.com/evolver) can solve six types of problems: • Recipe: a set of variables which can change independently. Like ingredients in a cooking recipe, they are combined to find the best mix.

• • • • •

Grouping: a collection of elements to be placed into groups. For example, place workers into groups to perform different jobs, or group stocks into equally valued bundles.

Order: an ordered list of elements. For example, the order to execute interdependent tasks.

Budget: recipe algorithm, but total is kept constant.

Project: ordering, but some elements must precede others.

Schedule: grouping, but assign elements to blocks of time while meeting certain constraints.

Section 13.6 Review Questions 1.

Define fuzzy logic and describe its characteristics.

Fuzzy logic is a branch of mathematics that deals with situations where a fact is not totally either true or false. Examples include: Does a car have “good” fuel economy? Is a pain “severe?” Is a book “interesting?” These are a matter of degree, not of probability.

Fuzzy logic operates on the basis of belief functions: the degree to which an item is considered to belong to a set. It provides a set of procedures for operating on belief functions to arrive at results that could otherwise only be arrived at by dividing a domain into a large number of small ranges and performing the calculation for each possible combination, a far more laborious process.

Fuzzy logic differs from probability in that fuzzy logic deals with inherently imprecise concepts, whereas probability deals with precisely defined concepts about which our knowledge is imprecise.

2.

What are the basic premises on which the fuzzy logic approach is based?

The two basic premises are (a) that characteristics of a situation are often better described by set membership functions (belief functions) than by yes/no truth values with probabilities, and (b) that approaching situations this way will improve our ability to deal with some types of problems.

3.

What are the major advantages of fuzzy logic? What are its major limitations?

Its advantages derive from the premises in the answer to the previous question. Some situations are better described by set memberships than by truth values, and describing them in this way can lead to simpler and more effective solutions than other approaches would.

Some disadvantages arise from problems with people supplying set membership information. Language is often vague, and people can have difficulty in supplying the definitions needed. It is complex to develop, requires considerable computing power (less of a problem now than in the past) and can be difficult to explain to users. (Another issue not mentioned in the text is that the name fuzzy logic, does not always go over well with businesspeople. It is in fact a precise way to deal with concepts that are not always precisely defined, but explaining its name can be necessary before one can even get to explaining its concepts. The term “continuous logic,” mentioned on p. 596 as sometimes used for it in consumer products, can help avoid this difficulty.)

4.

List some non-business applications of fuzzy logic.

The text mentions its applications in consumer products such as air conditioners, antilock brakes, toasters, cameras, camcorders, dishwashers and microwave ovens, as well as industrial areas such as beverage can inspection, matching golf clubs to customers’ swings, controlling the amount of oxygen in cement kilns, and others in industrial control. Other sources mention applications such as the graphics engine used in the Lord of the Rings films, which helped show huge armies with randomly generated but apparently orderly movements, and language filters on message boards and chat rooms to filter out offensive text.

5.

List some business applications of fuzzy logic.

The three mentioned in the text are strategic planning, real estate appraisal and bond evaluation. Students may find others in other sources.

Section 13.7 Review Questions 1.

Define NLP and list its major types.

NLP is “a subfield of artificial intelligence and linguistics that deals with analyzing, understanding, and generating the languages that humans prefer to use naturally in order to interface with computers in both written and spoken contexts, using natural human languages instead of computer languages” (p. 598).

The major types of NLP are natural language understanding and natural language generation.

2.

List some NLP applications.

Applications mentioned in this section include human-computer interfaces (abstracting and summarizing text, analyzing grammar, understanding speech and composing letters), and especially as front ends for other software packages such as database query; text mining; finding answers to questions; and machine translation (see next question).

3.

Define machine translation.

Automated translation of documents from one (human) language to another.

4.

List advantages and limitations of machine translation.

Advantages are speed, cost, and eliminating the need to have a person who understands both languages available when and where the translation is needed. This last is not an issue for translating documents such as reports and Web pages, but can be impractical when a person needs translation on an ongoing basis such as in the course of travel.

The disadvantages are that, as of the end of 2006, it doesn’t work very well.

Misinterpretations abound: for example, “Please pass the salt” can be interpreted and translated as “Please give the salt a grade of D or higher.” An amusing parlor game consists of sending a sentence in, say, English, to a machine translation program to render it in, say, Spanish, after which the output is translated back to English. The result seldom

resembles the original. A person, especially one who understands both of the languages in question, can often figure out where the process went off track.

Section 13.8 Review Questions 1.

Define voice recognition.

A process in which a computer interprets sound waves as words, but without necessarily interpreting their meanings.

2.

Define voice understanding.

Voice understanding is the combination of voice recognition (see previous answer) with natural language processing to interpret the meaning of the words that have been recognized. The net result is “an overall system that not only recognizes voice input but also understands it.”

3.

Define voice synthesis.

“The technology by which computers convert text to voice;” “the artificial production of human speech” (p. 603). The text also notes that voice synthesis is distinct from playing back pre-recorded voice (though selecting exactly which pre-recorded voice segments to play back can be non-trivial).

4.

List the advantages of voice technologies.

• Ease of access • Speed • Manual freedom • Remote access • Accuracy • Communicating while driving • Quick selection • Security • Cost–benefit These are discussed further on pp. 602–603.

5.

List the limitations of voice technologies.

The text mentions a limitation of speech recognition as its inability to recognize long sentences, at least within a reasonable amount of time. Speech recognition systems also have difficulty understanding speakers with regional or foreign accents. Overcoming these limitations requires extensive training of the recognition system to understand a specific individual.

6.

Describe voice portals.

A voice portal is “a Web site with an audio interface that can be accessed through a telephone call for getting information from the Internet, including in real-time” (p. 604).

Section 13.9 Review Questions 1.

What is a fuzzy neural network? Give an example of an application of a fuzzy neural network.

A fuzzy neural network is a network whose input and/our output is processed via fuzzy logic in addition to the neural network’s own activity. (Web searches on this topic should include the term “neuro-fuzzy,” both with and without the hyphen.)

An example of their application is international stock selection as described on p.

606. Students may find others in other sources. The concept has been used, for example, in predicting freeway traffic, data mining, computer vision/image processing, and predicting construction project duration.

2.

Describe how genetic algorithms can be integrated with other intelligent methods and give a sample application.

One method of integration is to combine genetic algorithms with neural networks. This is a natural match, because both technologies are ways a computer can learn by measuring its output against an external standard and modifying itself to improve its performance by that measure. Specifically, a genetic algorithm can find neural network interconnection weights faster than some other methods.

Genetic algorithms can also be used as components of larger systems. For example, it may be necessary to discover rules in a large database, with the rules to then be used in an expert or other system. Genetic algorithms can help with the rule discovery stage.

As a general principle, a large system is likely to embody diverse technologies to carry out parts of the overall task. Genetic algorithms should be in the developer’s toolkit as one of them.

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Machine learning is a discipline that investigates how computers can learn from existing data. Scholars disagree about whether machines can really learn. Some insist that computers do not learn and are only taught by humans. Do you agree? Please comment.

Since “scholars” disagree on this point, anything a student writes will reflect his or her personal opinion and how he or she defines “learning.” It should be evaluated based on cogency of argument and use of sources to back up opinions, not just feelings about “rightness” or “wrongness.”

2.

CBR produces new decisions based on past cases. Advocates claim that the process can capture the experiences of experts and alleviate problems of knowledge acquisition from human experts in rule-based systems. Do you agree? Why or why not?

It is difficult to answer this question with a simple “yes” or “no.” The process can surely capture some experiences of experts, but can it capture enough experiences, and enough about those experiences, to be useful for decision making? That will vary from situation to situation. Similarly, whether or no so doing can “alleviate” problems of knowledge acquisition from human experts depends on the nature and seriousness of those problems in a specific situation, and how well the case-based approach deals with those specific problems.

The “bottom line” is that there is a place for case-based reasoning and a place for conventional knowledge acquisition from human experts. The system developer should understand both and know where to use each, not try to make a case that either is always superior to the other. (Vendors of software that does one or the other already do that quite enough, thank you.)

3.

How can an investor use genetic algorithms to make a fortune on the stock market? Can a genetic algorithm-based system perform better than a neural network-based system? Why or why not?

(One answer to the first part is to fund or invest in a genetic algorithm software start-up, but that misses the intended point.)

The two conditions for applying genetic algorithms are that a solution must be characterized by known parameters that can be varied, and that a solution must have a measure by which it can be evaluated. Stock market prices meet these conditions. One could come up with a list of factors that might influence stock prices such as earnings per share, trends, ratios, trading volumes, etc., etc., and apply them to historical data. The genetic algorithm could then use historical data to figure out what combination of factors best predicts stock price changes. The investor could then apply it to current data in the expectation (or at least the hope) that it will continue to predict them in the future.

The problem with this approach is that the evaluation measure of the algorithm, ability to predict past stock market changes, does not guarantee being able to do so in the future. For example, the factors that will drive stock market performance in the future may not be those that drove it in the past, or may not be reflected in the list of factors at all. (Prior to 1973, an investor probably wouldn’t have included the price of oil as a factor in a list such as this. Today, it would be mandatory.)

A genetic algorithm and a neural network, given the same input parameters and asked for the same output, could ultimately produce the same results.

4.

Describe three advantages of fuzzy reasoning and provide an example to support each. If you disagree that any of these cases are actually advantages of fuzzy reasoning explain why.

• •

Fuzzy logic is an effective way to describe human perceptions of many characteristics of a situation that are not 100 percent true or false. Example: is a company’s inventory turnover “high” compared to the average for its industry?

Fuzzy logic allows a small number of formulas using set membership values to replace what would otherwise have to be a very large number of formulas, each

•

applying to a small range of input values. For example, using inventory turnover as a factor in a loan approval decision, one might (without fuzzy logic) assign ratios to groups with a different calculation for each group. Since all the other factors in the decision would also be assigned to groups, and since a five-point scale on each factor is not unreasonable, one ends up with (5 to the power of the number of factors) as the number of formulas needed. With fuzzy logic, far fewer would be needed.

Fuzzy logic is easy to apply to data obtained from sensors and other measuring devices. For example, a washing machine can measure the dirt in a load (generally done by measuring light transmission through the wash water at known points in the cycle) and use this number directly in a fuzzy logic calculation to decide about adding detergent, lengthening the wash cycle and so on.

I feel that all of these are advantages of fuzzy reasoning, so I have no disagreements to explain.

5.

Discuss the advantages and disadvantages of integrating multiple methods for developing complex intelligent systems. Describe all the possible integrations between CBR, ANN, genetic algorithms, fuzzy logic, and rule-based systems and assess their feasibility.

The advantages and disadvantages here are analogous to the advantages and disadvantages of integrating multiple technologies in any other system. The main advantage is that the system can exploit the strong points of all the technologies, covering a weakness in one by using another. Disadvantages are the complexity of the resulting system and, not unrelatedly, the cost of integration.

Since there are 20 possible integrations among the five types of intelligent systems in the question, describing all of them would be a very large project—and one we won’t undertake here. You may want to limit its scope if you assign this question.

6.

Discuss the logic of combining NLP with voice technologies.

Natural language processing involves interpreting strings in a human language. Human languages were first expressed, and are often most conveniently expressed today, as voice. Most people are used to making requests this way, so it is natural for them to make requests of computers this way as well.

7.

What kind of intelligence is needed in NLP? “The knowledge to analyze and then interpret the input. This may include linguistic knowledge about words, domain knowledge, commonsense knowledge, and even knowledge about the users and their goals” (p. 598).

Students may find other correct answers in other sources.

8.

Discuss the benefits and limitations of machine translation.

See answer to Review Question 4 of Section 14.7.

9.

Many companies push their customers to use text and/or voice automated call centers.

Why? Are there any benefits to the customers?

Companies do this because they can save money by having a call routed to the correct department and by having a computer present relevant information to the operator when a call is answered, rather than his/her having to ask for it and then wait for it to show up.

When a voice response call center front end is well implemented, these benefits are also visible to the customer as well in reduced waiting time and more informed operators. However, they are often poorly implemented so customers experience frustration and delays.

10.

Discuss the relationship between NLP, queries, and computer searches.

A query involves asking a computer to find or produce the information a person needs. It is easier for most people to express their needs in their own language than it is for them to learn a computer language. NLP enables them to do so. (Computer searches are a form of query.)

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

Go to fluencyvoice.com and investigate the capabilities of speech. Write a report about your findings.

Open-ended answer. Since Fluency Voice Technology provides speech recognition applications for call centers, students will find information about such applications, news of the field, “white papers” explaining its concepts and related material, as well as items such as company background and contact information that one would expect to find on any business Web site.

(If you assign this question to typical U.S. university students, it might be worth pointing out that “call centre” is correct U.K. spelling, not an error. Many will know this, but it will be news to some.)

2.

What does it mean that “callers can speak over the application at any point”?

That callers do not have to wait for the application to complete what it is saying before they can respond. In engineering terms, the application supports full-duplex communication.

3.

Relate the case to the three configurations of voice technologies described in this chapter.

The three configurations of voice technologies described in this chapter are voice recognition, voice understanding and voice synthesis.

The software from Fluency Voice recognizes customer choices and uses them to route callers to the appropriate department.

It does not attempt to understand what the caller says beyond this.

It also, at least as described here, does not use voice synthesis. It uses playback of pre-recorded voice segments, which is not the same thing.

4.

How does the Barclays system enable call routing?

It listens for callers to speak a choice based on a recorded message. An application from Fluency Voice then routes the call to the appropriate Barclays product team.

CHAPTER

14

Intelligent Systems over the Internet

Learning Objectives for Chapter 14 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the cutting-edge intelligent systems that run over the Internet Understand the concept and power of intelligent agents Learn various applications of intelligent agents Learn the concept of representing semantic knowledge over the Internet Learn the concept of recommendation systems over the Internet Understand methods for designing recommendation systems

CHAPTER OVERVIEW As the book has noted many times, the Web provides two things to intelligent systems: a common user interface via browsers and a common communication vehicle below it. Many types of specialized systems have evolved to take advantage of these capabilities. This chapter discusses several of them, including the large category of “intelligent agents” which we often use without realizing it. It also goes into some of the “glue” that binds them together, including XML and the related topic of Web services.

CHAPTER OUTLINE 14.1

OPENING VIGNETTE: NETFLIX GAINS HIGH CUSTOMER SATISFACTION FROM DVD RECOMMENDATION A. PROBLEM B. SOLUTION C. BENEFITS  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

271

272

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

14.2

WEB-BASED INTELLIGENT SYSTEMS  Section 14.2 Review Questions

14.3

INTELLIGENT AGENTS: AN OVERVIEW A. INTELLIGENT AGENTS: DEFINITIONS B. INTELLIGENCE LEVELS C. COMPONENTS OF AN AGENT  Section 14.3 Review Questions

14.4

CHARACTERISTICS OF INTELLIGENT AGENTS A. AUTONOMY OR EMPOWERMENT B. COMMUNICATION (INTERACTIVITY) C. AUTOMATING REPETITIVE TASKS D. REACTIVITY E. PROACTIVENESS (OR PERSISTENCE) F. TEMPORAL CONTINUITY G. PERSONALITY H. OPERATING IN THE BACKGROUND: MOBILITY I. INTELLIGENCE AND LEARNING  Section 14.4 Review Questions ⧫ Technology Insights 14.1: Intelligent Agenets, Objects, and ES

14.5

WHY USE INTELLIGENT AGENTS?  Section 14.5 Review Questions

14.6

CLASSIFICATION AND TYPES OF INTELLIGENT AGENTS A. CLASSIFICATION BY APPLICATION TYPE 1.

Organizational and Personal Agents 2.

Private and Public Agents 3.

Software Agents and Intelligent Agents B. CLASSIFICATION BY CHARACTERISTICS C. OTHER CLASSIFICATIONS  Section 14.6 Review Questions

14.7

INTERNET-BASED SOFTWARE AGENTS A. E-MAIL AGENTS (MAILBOTS) B. WEB BROWSING ASSISTING AGENTS C. FAQ AGENTS D. INTELLIGENT SEARCH (OR INDEXING) AGENTS E. INTERNET SOFTBOTS FOR FINDING INFORMATION F. NETWORK MANAGEMENT AND MONITORING G. E-COMMERCE AGENTS ⧫ Application Case 14.2: Fujitsu (Japan) Uses Agents for Targeted Advertising H. OTHER AGENTS

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

⧫ 

273

Application Case 14.3: Wyndham Uses Intelligent Agents in Its Call Center Section 14.7 Review Questions

14.8

DECISION SUPPORT SYSTEMS AGENTS AND MULTIAGENTS A. DSS agents B. Multiagents  Section 14.8 Review Questions

14.9

THE SEMANTIC WEB: REPRESENTING KNOWLEDGE FOR INTELLIGENT AGENTS A. DEFINITION OF SEMANTIC WEB B. XML AND WEB SERVICES ⧫ Technology Insights 14.4: A Sample XML Format C. THE LAYER CAKE OF THE SEMANTIC WEB 1.

Uniform Resource Identifiers 2.

The Resource Description Framework (RDF) 3.

Ontology 4.

Logic 5.

Proof and Trust D. APPLICATION OF SEMANTIC WEB SERVICES ⧫ Technology Insights 14.5: Advantages and Limitations of the Semantic Web  Section 14.9 Review Questions

14.10 WEB-BASED RECOMMENDATION SYSTEMS ⧫ Application Case 14.6 Amazon.com Uses Collaborative Filtering to Recommend Products A. TAXONOMY OF RECOMMENDATION MECHANISMS 1.

Profile Generation and Maintenance 2.

Profile Exploitation and Recommendation B. COLLABORATIVE FILTERING C. CONTENT-BASED FILTERING ⧫ Application Case 14.7 Content-Based Filtering at EuroVacations.com D. MOBILE APPLICATIONS OF RECOMMENDATION AGENTS  Section 14.10 Review Questions 14.11 MANAGERIAL ISSUES OF INTELLIGENT AGENTS A. COST JUSTIFICATION B. SECURITY C. PRIVACY D. INDUSTRIAL INTELLIGENCE AND ETHICS E. OTHER ETHICAL ISSUES F. AGENT LEARNING G. AGENT ACCURACY

274

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

H. I. J.

HEIGHTENED EXPECTATIONS SYSTEM ACCEPTANCE SYSTEM TECHNOLOGY  Section 14.11 Review Questions Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Internet Exercises ⧫ End of Chapter Application Case: Spartan Uses Intelligent Systems to Find the Right Person and Reduce Turnover  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

The connection between intelligent systems, in the sense of this chapter, and decision support can seem a bit tenuous. Sure, deciding which Web page to visit is a decision of sorts, and search engines can help make it, but that’s a stretch.

We could connect them via technology. DSS is also known (more or less) as “business intelligence,” some parts of artificial intelligence (e.g., expert systems) have decision support uses, and these systems are called “intelligent,” so there must be a link somewhere.

Those are stretches. That’s why we recommended that, if you teach a traditional DSS course and must cut something from the book to fit your course into fixed calendar confines, this part may be the least bad part to cut. (No part of it is “best” to cut, since “best” implies a degree of goodness.)

That said, if you kept Part V of the book, there’s value in this chapter for MIS students. It even has some DSS content, albeit only seven lines at the bottom of p. 637 and a genuinely informative graphic covering most p. 638. A better point of view, though, is to see the course as covering the spectrum of “how can computers help managers manage, help knowledge workers analyze, recommend?” That is, after all, the essence of what DSS is about. We should not limit our perspective to a narrow definition.

So, rather than treating agents, etc., as a separate topic, I suggest you present them as “here are these things we can send out on behalf of managers and knowledge workers to help them, they’re management support systems and tools in a broad but true sense of those words, and here’s how they work.” Then it all fits together. (Which is a good reason for not skipping Part V of the book, but since you’re probably covering it if you’re reading this, you already figured that out.)

How does the opening vignette fit into this framework, since the central system in it isn’t doing anything Netflix’s managers or knowledge workers would otherwise do? They wouldn’t get on the phone with a customer, study that customer’s likes and dislikes, study the rental patterns of hundreds or thousands of other customers, and try to find matching patterns, just to recommend movies to one person? Of course they wouldn’t. It wouldn’t be economical. Netflix

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

275

would go under in a week if they tried. However—and this is the key—it would be nice if they could do this. With intelligent agents, they can. Many of the tools in this chapter don’t support or replace what managers and knowledge workers do. Instead, they support or replace what managers and knowledge workers would like to do—if only they could.

The body of the chapter begins with something anyone taking a course that uses this book will have seen time and again: Internet search engines. Google and its competitors guard their exact methods jealously, but the general concepts are public. Much of the chapter takes their concepts and extends them, singly or in combination, to other applications. The thread of common characteristics will pop up time and again in it, though you may have to draw students’ attention to it once in a while.

The next several sections are devoted to intelligent agents. These are the building blocks of many intelligent systems. Students should understand the way they communicate via XML, the basic concepts of that language, RDF, etc., as they are used in many places besides here. (For example, standard dialogues using XML are replacing EDI in B2B e-commerce.) There are many introductions and tutorials on the Web that provide more detail than Section 14.9 in an accessible form. You might want to urge your students to read some. The Wikipedia entry on the subject, http://en.wikipedia.org/wiki/XML, contains more than enough references for this purpose. A search will, of course, turn up still more.

I find Sections 14.7 and 14.8, on the types and uses of Internet-based software agents, especially valuable. The reason is that an introductory course in an area such as this is not going to turn students into seasoned professional experts. Its purpose is to teach them enough about the subject for them to recognize where certain things are likely to work, be able to start evaluating their usefulness, and know enough of the basics to be able to learn more when the need arises.

These sections accomplish much of that for agents. In particular, they can sharpen students’ senses so they’ll recognize an agent application when they encounter one, instead of scratching their heads and wondering “hmmm … I wonder how we could make this work?” This is especially true in e-commerce (in the middle of 14.7; section 14.10 extends this discussion), where intelligent use of intelligent agents can create a competitive edge.

Finally, Section 14.11 wraps up the coverage with a discussion of managerial issues. As with similar discussions in earlier chapters, your management students will understand this but your computer science students may have a harder time. You are the best judge of your audience.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 14.1 Review Questions 1.

Describe why Netflix adopted the movie recommendation system and what problems the company may have if it did not do so.

Netflix adopted the movie recommendation system because it found that its customers all rented the same “box office hits.” They couldn’t stock enough of those, and movies that some customers would have preferred weren’t moving because those customers didn’t know about them.

276

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

If Netflix had not adopted this system or something much like it, its demand would not have matched its supply. That would have affected its profitability, perhaps to the point of bankruptcy.

2.

List the benefits of the CineMatch system. Are there any other benefits that you can foresee that are not described in the vignette?

The vignette describes four benefits: increased customer satisfaction and loyalty, broadened title coverage, better understanding of customer preference[s], and fast[er] membership growth.

Another benefit they might have received is increased leverage with studios as a result of being able to move less popular movies in volume. Studios have no trouble selling big hits. Doing the same for lesser titles can make a big difference to their bottom line.

3.

Describe how CineMatch makes recommendations. Review the techniques that you have learned in previous chapters and choose one that you think can be used to implement a similar one for online movie recommendations.

One approach that could be used is case-based reasoning. A customer’s rental history, and any other preferences the customer expresses (I don’t know what other data Netflix uses here) could be matched with other customers’ rental histories in the case repository.

Another technology could be expert systems. Rules such as IF rented Sound-ofMusic AND rented Chicago THEN suggest West-Side-Story could be derived from customer rental histories.

Third, fuzzy logic could be useful. Rental histories could be expressed as set memberships such as “likes action films,” “likes SF,” “likes musicals.” A renter who had rented 30 films, of which two were musicals, might have 10 percent membership in this last set; but one who got 15 musicals out of 30 might have 100 percent membership. (Set membership is not the fraction of movies of a genre someone rented; it characterizes how well a customer fits a category. Someone who rents musicals fully 50 percent of the time fits “likes musicals” well.) We’d still need a system to apply this information, but it could be simpler than it would have been if its rules were based on individual titles.

4.

Do you like getting recommendations when you visit a physical movie store such as Blockbuster? How about recommendations from an online rental store, such as Netflix?

Why do you like or dislike getting recommendations at these stores?

Open-ended, personal answer.

Section 14.2 Review Questions 1.

Describe Web-based intelligent systems and their unique features.

Web-based intelligent systems sit behind the familiar interface of a Web page, performing tasks that require intelligence (in the sense of the past two chapters) in a userinvisible manner.

There are several types of Web-based intelligent systems. Among the features common to all of them, which distinguish them from other types of systems, are their

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

277

Web-enabled user interfaces, their ability to communicate with each other through a Web-based protocol, their internal use of intelligent technology, and their invisibility to users (most of the time).

2.

Explain the functions of a search agent and give an example of one.

The function of a search agent is to find information on the Web or in a subset of the Web (a single domain, or even part of a domain—such as when, on eBay, you restrict a search for “Apple” to its computer section so as not to be swamped by plastic fruit, rugs with fruit designs, old Beatles records, and paintings of Eve in the Garden of Eden).

An example of a search agent is the eBay search shown in Figure 14.1. Most students will also be familiar with Google or one of its competitors. While people usually think of Google for Web-wide searches, it can also search within a single domain—when asked to do so by its user via the “advanced search” page, or when licensed for that purpose by the domain owner.

3.

Explain the function of a monitoring agent and give an example of one.

A monitoring agent takes information returned by a Web information agent, compares it with what was requested and what was found earlier, and sends updates to users.

An example of one is ExpertFlyer. Subscribers to its Premium Service ($9.99/mo. or $99.99/yr.) can tell it which flights they want frequent-flyer award seats on. (As most frequent flyers know, airlines limit the availability of such seats to minimize the impact of award tickets on revenue, making more available when a flight is less likely to sell out.

As flight time approaches and their estimates of paid ticket sales become more statistically certain, they often release additional award seats.) ExpertFlyer notifies the subscriber by e-mail when seats become available, improving subscribers’ ability to get those seats before others do.

Section 14.3 Review Questions 1.

Define IA. The text gives several definitions of intelligent agent (IA), such as “a computer program that helps a user with routine computer tasks.” Essentially, an IA is software that acts on the behalf of someone (or another program) with some level of autonomy, and within which some artificial intelligence is embedded. It functions like a human agent.

2.

Describe the different intelligence levels of software agents.

See answer to Discussion Question 10 at the end of this chapter. The examples included in that answer may be omitted from this one, since this phrasing of the question does not request them.

3.

Describe the major components of an agent.

• Owner: the user, process or other agent that created this one.

• Author: The agent’s creator. An agent may be further specialized by its owner.

• Account: A reference point to an agent’s origin, used for billing if necessary.

278

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• • • • • • 4.

Goal: how an agent decides if it has accomplished its task.

Subject description: parameters that define and limit the agent’s activity.

Creation: when the agent was set forth on a task Duration: how much time the task may take Background: additional supporting information as required Intelligent subsystem: code to give the agent its “intelligent” characteristics

Describe major features of intelligent agents.

• • • •

Reactivity: ability to perceive the environment and respond to changes in it Proactiveness: ability to take initiative to meet their goals Social ability: ability to interact with other agents and perhaps with humans Autonomy: ability to control their own actions while working independently

Section 14.4 Review Questions 1.

Describe what the autonomy of IA means.

The ability of an intelligent agent to act on its own, without direct human supervision.

2.

List the major characteristics of IA. The major characteristics of intelligent agents are: • Autonomy or empowerment • Communication (interactivity) • Automates repetitive tasks • Reactivity • Proactiveness (or persistence) • Temporal continuity • Personality • Operating in the background: Mobility • Intelligence and Learning

3.

Define mobile agent and describe the difference between mobile agents and multiagents.

A mobile agent can transport itself across different system architectures and platforms.

Essentially, it is a roving piece of software that operates on different computers. These are often used in e-commerce.

A multiagent (or multi-agent, hyphenated) is a system with multiple cooperating agents. These can span different platforms, but do not transport themselves; a different component of the multiagent operates on each. (A fuller discussion of multiagents will come in Section 14.8.)

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

279

Section 14.5 Review Questions 1.

List the major benefits of IA. Some major advantages of intelligent agents are: • They can be used to handle the information overload by assisting in Web searches, database searches, and e-mail “sifting.”

• They can provide decision support.

• They can perform repetitive office activities.

• They can perform mundane personal activities.

• They can perform search and retrieval (see the first item).

• They can function as domain experts.

• They can operate in negotiation support systems (NSS) and electronic commerce.

2.

Describe why information overload is a by-product of the information age.

There are several reasons. One is that information age technologies facilitate the creation of information, or (if the information itself is not new) putting into a form where it can be sent easily to others. A second is that these technologies make it easy to replicate information, so a memo that would once have been sent on paper to four people is now sent electronically to forty. A third is that they permit so much more of the available information, which we would otherwise have ignored, to reach us, seemingly forcing us to look at it. Intelligent agents can help here by filtering what we see.

3.

List the reasons for the success of intelligent agents.

Because they help with decision support, including front-line decision support; with repetitive office activities; with mundane personal activity; with search and retrieval; and in providing domain expertise.

4.

Give an example of repetitive office work for which intelligent agents can help automate the process.

Creating a chart in a spreadsheet program or a merge document in a word processing program.

Section 14.6 Review Questions 1.

List and describe the types of agents.

There are several ways to classify agents into types. One is by the taxonomic tree of Figure 14.2, reproduced below. Another is by application area: workflow and business process management, distributed sensing, retrieval and management, e-commerce, human-computer interaction, virtual environments and social simulation. A third is by characteristics of agency, intelligence and mobility. Others are possible as well.

280

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture.

2.

[[ANA: We were unable to view this picture]] Define organizational agent and give an example of one.

An organizational agent performs tasks on behalf of a business process or a computer application. (Contrast: personal agent.)

An example of an organizational agent would be an agent that searches incoming customer e-mails for certain keywords and issues replies when they are found.

3.

Define personal agent and give an example of one.

A personal agent performs tasks on behalf of individual users. (Contrast: organizational agent.)

An example of a personal agent would be a user setting e-mail sorting rules to route e-mail from family members directly to a folder to be read after working hours.

4.

Describe the differences between software agents and intelligent agents.

According to some experts, an intelligent agent, to truly deserve the name, must be able to function at level 3 of the agent intelligence hierarchy. The term “software agent” is used to make it clear that most of today’s agents, which function at level 2, do not have this capability.

Section 14.7 Review Questions 1.

Describe the function of a FAQ agent and give an example of one.

FAQ (Frequently Asked Questions) agents guide people to answers to FAQs. Usually these are narrow, domain specific questions such as printer diagnostics, etc. Given a natural language question, a FAQ agent searches large numbers of FAQ files and based on terms found in the input.

An example of an FAQ agent as used by a U.K. furniture retailer is shown in Figure 14.4.

2.

Describe how agents that compare prices work.

Agents that compare prices, sometimes known as shopbots, are programmed with lists of sites that may sell the type of item in question. When given a specific request, they send a

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

281

query to all the sites with enough information to identify that item (such as the ISBN of a book). They organize the replies and present them to the requestor.

3.

List and describe the six steps in electronic shopping.

Step 1. Need identification: Providing information about a newly available product or service, or a price change of an existing product or service, within a category previously stated as being of interest Step 2. Product brokering: identifying specific products or services within a previously identified category, providing information (e.g., customer reviews) to help select one of the identified products Step 3. Merchant brokering: identifying potential suppliers for a previously chosen product or service, providing information (e.g., customer reviews) to help select one of the identified suppliers Step 4. Negotiation: selecting options for the product or service; determining price and delivery terms and conditions Step 5. Purchase and delivery: arranging payment, checking the transaction for errors and completing it Step 6. Product/service evaluation: handling service and support after the sale, providing feedback

4.

For each step identified in Question 3, describe two potential agent applications.

These are included in the “description” part of the above answer.

5.

Describe a sample of a workflow agent.

One example (text, p. 636) is FlowMark from IBM. It provides a graphical interface to manipulate objects that define and capture the activity steps of any business process (e.g., handling a claim, approving a line of credit, registering a patent). An activity can be carried out by a program or a person. The user defines the process by drawing connectors among activities and specifying rules for when each is to be carried out.

6.

Identify two applications of intelligent agents in mobile commerce.

The fifth bullet item on p. 636 answers this question. One application is WAP-based services: e-commerce sites designed to be viewed on a device such as a mobile phone, typically used when one is away from one’s computer. A second is recommendation systems, which can take advantage of location information often available from mobile phone users with no need for action on their part, to recommend businesses of interest near their present location.

Section 14.8 Review Questions 1.

Define DSS agent and give an example of one.

A DSS agent is an agent, defined above, whose purpose is to help with decision making or problem solving. One example of a DSS agent is an agent to monitor supplier price

282

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

changes, which can support decisions as to which potential supplier to order from at a given time.

2.

Define multiagent system.

A multiagent system is “a computer-based environment that contains multiple software agents to perform certain tasks” (p. 638, recast here in the singular).

3.

Define distributed artificial intelligence (DAI).

Distributed artificial intelligence is “the study of distributed but centrally designed artificial intelligence systems. It involves the design of a multiple-agent distributed system with a problem to solve or a task to accomplish” (p. 638, rewritten as two sentences).

4.

Describe the difference between DAI and multiagent systems.

See the first part of the answer to Discussion Question 8 at the end of the chapter.

Section 14.9 Review Questions 1.

Describe the semantic Web and explain the differences between the semantic Web and XML. The semantic Web extends the current Web, with the aim of providing a universal medium for information exchange, by providing a content presentation and organization standard so that Web-enabled applications can share content and meaning.

XML, an extension of HTML, is one of the tools used to implement the semantic Web. Its role is to provide a syntax for structured documents without restricting their meaning. The semantic Web as currently under development could not exist without it, it is central to how the semantic Web works, but they are not identical.

2.

Define XML and give an example of it.

XML is a language that provides a text-based means to describe and apply a tree-based structure to information. Information is expressed as text, interspersed with markup that indicates its separation into a hierarchy of character data, container-like elements, and attributes of those elements. The interpretation of XML data rests on mutual understanding of element names and syntax. Several standards have emerged to create such a mutual understanding in specific application areas.

An example of XML follows:

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

283

<recipe name="bread" prep_time="5 mins" cook_time="3 hours"> <title>Basic bread</title> <ingredient amount="3" unit="cups">Flour</ingredient> <ingredient amount="0.25" unit="ounce">Yeast</ingredient> <ingredient amount="1.5" unit="cups" state="warm">Water</ingredient> <ingredient amount="1" unit="teaspoon">Salt</ingredient> <instructions> <step>Mix all ingredients together, and knead thoroughly.</step> <step>Cover with a cloth, and leave for one hour in warm room.</step> <step>Knead again, place in a tin, and then bake in the oven.</step> </instructions> </recipe>

3.

What are the building blocks of Web services?

From the bottom up, they are: the transport layer, the XML messaging layer, the service description layer, and the publication and integration layer.

4.

What are the elements in the layer cake of Web services?

(The book asks about “the layer cake of Web services.” This misprint will be corrected in the second printing.)

Seen as a layered system, the semantic Web starts with the four layers of Web services. From the bottom, these are the transport layer, XML messaging layer, service description layer (WSDL), and publication/discovery/integration layer. It continues up with a unifying data model such as RDF, a language with defined semantics, and ontologies of standardized terminology.

The specific phrase “layer cake of the semantic Web” refers to a conceptually similar but not entirely identical presentation. As described by those who first used the term (citations in text) its lowest level consists of data represented in Unicode1 and uniform resource identifiers (URIs, not to be confused with Web URLs). XML is used above them to render the data. Above it are RDF, as in the first description, and then a four-layer architecture to support information exchange among agents: ontology, logic, proof and trust.

5.

What are the advantages of using Web services?

They’re easy to understand, they enable easy resource integration, they save development time and cost, they allow automatic updating of content, they provide for easy resource reuse, they enable enhanced search mechanisms, and they make it possible to create virtual communities.

1

A 16-bit character that can represent, with minor exceptions, every character in every written human language.

284

6.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

What are the limitations of using Web services?

They have limited power to represent complex relationships, they lack some supporting tools, they allow users to create incomplete ontologies, they permit inaccurate content, and they raise security concerns.

7.

Describe a potential application of Web services.

MusicBrainz, described in the text starting on p. 645, is one example (and what most students, if they’re like mine, will choose.) It’s about semantic Web services, which fits the subject of this section but is not specifically required by the question.

Others can be found easily. The CIO article at http://www.cio.com/archive/040102/real.html describes three applications of Web services that do not involve the semantic Web: at Nordstrom’s, at the Life Time Fitness health club chain, and at the CTSnet.org consortium of medical associations.

Section 14.10 Review Questions 1.

Define recommendation agent and describe the role of these agents in e-commerce.

A recommendation agent is “an intelligent system that can identify customers’ interests from existing usage data and make suggestions accordingly” (p. 648). The restriction to specifically usage data can be relaxed; other ways of identifying customer interests are possible.

The role of recommendation agents in e-commerce is to suggest products users might be interested in, hopefully enhancing customer loyalty and achieving higher conversion/retention rates.

2.

Briefly illustrate the mechanism for product recommendations.

The process involves two major components: generating user profiles and using them to create recommendations. There are several steps in each component and a choice of ways to approach each step. Responses may vary accordingly, but should always reflect these two major parts. (The next two questions address some aspects of the lower-level options.)

3.

Describe why a recommendation system needs a user profile and how a user profile can be built and maintained.

A recommendation system needs a user profile in order to know something about the user and therefore be able to make useful recommendations.

Initial profiles can be built initially from a history of user transactions with the site. In some situations users will be willing to add data, such as when a traveler enters a list of favorite destinations into an airline site for fare sale tracking. It is also possible to augment them with information from third parties, such as subscriber lists of magazines whose coverage suggests subscribers’ interests.

Maintaining a profile means keeping it updated over time as the user’s interests evolve and new information is obtained. It can be maintained by tracking where within a site users have been, what products they have searched for, how long they stayed on a

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

285

particular Web page and which products they ordered. If users entered any data, they should be able to change it.

(Maintenance needs can be affected by how the profile is organized. Information that a person has shopped for gifts for a three-year-old could be stored in that form, enabling a recommendation system to continue to suggest gifts for this age group.

Amazon.com, however, stores the information that this user shopped for gifts for someone born in, say, 2004. When he or she returns in 2009, the recommendation system will suggests gifts for five-year-olds.)

4.

Describe content-based filtering and collaborative filtering. Compare their advantages and limitations.

Content-based filtering offers recommendations on the basis of similarities among products, whereas collaborative filtering uses similarities among users’ purchase patterns.

Collaborative filtering works well when there is similarity in choices across users.

It can, for example, recommend science fiction books to people who bought computer books even though there is no content similarity between the categories, because many people who buy computer books also buy science-fiction books2. However, it may give poor recommendations to users with unusual tastes (such as programmers who prefer historical romances) and is unable to recommend new products that no, or few, users have purchased so far.

Content-based filtering may work well with products that have had few purchases.

However, it can be difficult to capture characteristics that influence purchase of a product, such as its aesthetics.

Section 14.11 Review Questions 1.

List and describe major managerial issues related to the use of IA. Major managerial issues related to the use of IA are: • Cost justification: ability to demonstrate ROI • Security: ensuring that agents do not come back with a virus or Trojan horse, etc.

• Privacy: many users view preference tracking as an invasion of privacy • Industrial intelligence and ethics: agents can be involved in practices such as cyber-spying • Agent learning: the more agents are used, the more they learn, but this must be managed • Agent accuracy: inaccurate agents could potentially turn customers away • Heightened expectations: agents may not necessarily provide everything a user expects • System acceptance: some agents may not interface well with varied architectures • System technology: agents require powerful and highly scalable platforms

2

This example was made up for purposes of this answer.

286

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

2.

Describe major concerns in cost justification of intelligent systems.

“Standalone agents that perform complex tasks … can be quite expensive [due to] the tremendous amount of research and development that goes into creating them. In addition, they may be custom programs that require extensive programming time.

Justifying agents is like justifying other DSS. The benefits are often intangible, and the results are hard to quantify, but if you do not invest in the technology, your competitor will, and you may be left behind.” (p. 654)

3.

Describe major privacy concerns in using intelligent systems.

(This question, as phrased, is broader than the content of this section or even of this chapter. If you would like answers to be more focused than the wording of the question requires, you may want to make this clear. The answer below focuses on issues raised here.)

Intelligent agents work on the basis of information about users. Many users are concerned about companies storing their information to be used for the companies’ own advantage. Even those who do not object, for example, to Amazon.com storing the history of their purchases with it and their usage of its site may be concerned that this information, if combined with equally valid information stored by other firms, could create a total profile of the person that would disclose more about him or her than the person wants any one company to know. While the opportunity to opt out of information storage is often available, it can be easy to overlook or even hard to find if one is looking for it.

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Relate the Internet to today’s information overload problem and discuss how IA can be used to alleviate the problem.

See answer to Review Question 2 of Section 14.5. The Internet exacerbates the problems listed in the first part of that answer. As the book says (p. 625), “Thousands of new systems and even more new users bring new sources of data onto the Web every minute.”

2.

Spam control programs have become popular due to the rapid increase of junk mail on the Internet. Discuss how spam control can be done by intelligent agents and whether there are other tasks that are suitable for the application of e-mail agents.

Intelligent agents identify spam on the basis of characteristics. Some review its content against a list of words that are typically associated with spam. Some let users identify spam during a training period, after which they are left to do it on their own. (The user can generally decide when the agent has achieved sufficiently high accuracy.) Some server-based agents rely on users to identify spam, after which they consider identical messages sent to other users to be spam as well.

Spammers, unfortunately, know what agents do and counter by developing spamming methods that bypass their capabilities. Embedding spam content in a graphic

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

287

foils content-based detection methods. Varying each message by adding random text foils methods that compare messages to identified spam. It is an ongoing battle, with each side attempting to outdo the other. (As this is written in late 2006, the spammers unfortunately seem to be winning.)

Other e-mail tasks that are suitable for the application of e-mail agents are filtering of e-mail for actions that can be taken automatically based on sender, content, or other visible message characteristics singly or in combination. For example, messages containing the name of a user’s current project could be put into a folder for that topic (if its name is sufficiently distinctive that the probability of its showing up in other contexts is negligible).

3.

Distinguish between a search engine and a metasearch engine. What roles can intelligent agents play in improving search performance?

A search engine searches the Web or a portion of the Web, based on terms its user supplies. A metasearch (or hyphenated: meta-search) engine may not search anything.

Instead, it sends search requests to several search engines and combines the results of their searches into a single answer.

Intelligent agents can improve performance by filtering, combining and providing similar topics of interest to the search.

A popular meta-search agent not mentioned in the text is Dogpile, http://www.dogpile.com . It queries, as of this writing, Google, Yahoo! Search, MSN search, Ask.com, and others. Its results listing combines references to the same page returned by multiple search engines, indicating which ones returned each suggested page and giving highest rankings to those returned by multiple agents.

4.

What is the difference between a browsing agent and a search (indexing) agent? What are the situations in which each is more appropriate for decision support?

Browsing is a user-driven process. The user scans sites and stops when he/she finds something of interest. A browsing agent, typically running in the background on the user’s computer, can help: as the user moves from page to page, the agent learns what the user is interested in and can provide links to related pages or other advice.

Searching is an automated process, started with terms provided by the user but running on its own after that. A search agent, typically server-based, checks many sites and returns relevant results to the user.

Strictly speaking, an indexing agent is not a search agent. An indexing agent travels the Web, noting page contents and indexing them for later use by search agents.

Since search agents can’t function without prior indexing, and this sort of indexing is pointless unless the index will be used by a search agent, the two are closely linked and often thought of as a unit.

Searching, hence a search agent, is more appropriate when a user doesn’t know where to start getting information. Browsing, hence use of a browsing agent, is more appropriate once a user has found a reasonable starting point and wants to explore from there.

288

5.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Personalogic and similar agents attempt to learn about people’s preferences. Explain how such information can be used in marketing and advertising.

At the individual level, once a person’s preferences are known, a “market of one” exists.

Products of probable interest to that individual can be offered, and existing products can be customized or targeted. (Note that advertising can be highly personalized today, using media such as Web pages and mobile telephones.)

In more broadly based marketing and advertising programs, this information can be aggregated to present an overall picture of what the target market will find attractive.

(While PersonaLogic (officially with an embedded upper-case “L”) is not discussed in the text, other preference-learning agents are; specific information about PersonaLogic is not needed to answer this question correctly; and in any case information about it is easy to find on the Web.)

6.

What role can IA play in e-commerce? List 10 possible applications of intelligent agents in ecommerce.

Need identification, product brokering, merchant brokering, negotiation, purchase and delivery, product service and evaluation (the six listed thus far were also in the answer to Review Question 3 of Section 14.7), fraud detection, learning about customer preferences, information sharing (these three are listed on pp. 634–635), advertising and payment support (listed under “other” on p. 634). (This comes to eleven.)

7.

Some news Web sites allow the user to indicate preferences by selecting the categories of interest and then offering news reports that fit the reader’s interests. Explain the pros and cons of this service and similar agents in reducing information overload.

Information overload is not solely a function of the amount of information one gets. It also reflects the amount of unwanted information one receives and must wade through en route to the far smaller collection of information one really wants. Intelligent agents such as those discussed here can reduce this type of overload by reducing the amount of unwanted information one receives while letting the desired information through, even prioritizing it for the user.

Such services have advantages, the most important of which is the reduction of information overload discussed above. Their major disadvantage is that a user can come to rely on them and ignore their limitations, thus missing out on important information that a more laborious means of scanning the news would have found. This disadvantage can be handled by not relying on them exclusively.

8.

Distinguish between DAI and multiagents. What are the advantages and limitations of multiagent systems?

DAI, Distributed Artificial Intelligence, is an umbrella term that covers all AI systems which are centrally designed but execute in a distributed fashion.

Multiagents are a form of DAI. In a multiagent system, an overall task is decomposed into subtasks and each subtask assigned to a specialized agent. Each agent is independently designed and operates independently, having its own goals which can differ, or even conflict with, the goals of other agents in the system.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

9.

289

Why is negotiation so important in e-commerce? List potential applications of intelligent agents in the negotiation process and the advantage of using IA in negotiation.

Negotiation is so important in electronic commerce for the same reason that it is important in non-electronic commerce: All commerce (business) is based on a buyer and a seller reaching an agreement. Unless there is a significant power mismatch between the two, such as when a supermarket sells a box of cereal to an individual, there is opportunity for negotiation in reaching it. (Even in that situation, there may be opportunities for discounts on damaged items or on large quantities.)

Applications of IAs in the negotiation process generally amount to either monitoring it, so a person can take action based on events in the marketplace, or automating it. Agents that automate negotiations generally do so within parameters set by their users, such as a maximum price.

Advantages of using IAs include their being fast, being always available and at work, and not having their behavior affected by emotional factors.

10.

Review the four levels of IA intelligence. Provide a sample application of intelligent agents at each of these levels.

Level 0 (lowest) agents retrieve documents for a user under straight orders. Sample application: Web browsers. The user must specify the URLs of the documents. These help navigate the Web by handling the mechanical side, but do not use meaningful intelligence.

Level 1 agents provide user-initiated search facilities for finding relevant Web pages. Sample application: search engines such as Google, AltaVista, and Infoseek.

Information about pages, titles, and word frequency is stored and indexed. The user provides key words which the search engine matches against indexed information.

Level 2 agents maintain user profiles to identify relevant information they find as they monitor the Web, then notify users of what they find. Sample application: WebWatcher, a tour guide for the Web developed at Carnegie-Mellon University. Agents at this level may be called semi-intelligent.

Level 3 agents add learning and deductive components to user profiles to help users who cannot formalize a query or specify a target for a search. Sample applications: DiffAgent (from CMU) and Letizia (from MIT). Agents at this level are referred to as learning or (truly) intelligent agents.

11.

Why is the semantic Web useful for developing a friendlier environment for intelligent agents? What is the difference between the semantic Web and semantic networks and other mechanisms for representing information over the Internet?

The semantic Web is useful for this purpose because it provides a standard for presenting and organizing content. This allows independently developed agents to communicate flexibly, so they can cooperate with each other.

The semantic Web differs from a semantic network in that a semantic network is simply a representation of knowledge as a directed graph. While semantic networks have been used in information systems (notably for representing natural language) they are not first and foremost designed for that purpose and do not offer a standard representation that independently developed modules such as agents can use for data interchange.

290

12.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

What is the basic architecture of Web services?

See answer to Review Question 3 of Section 14.9.

13.

Recommendation systems are gaining more popularity in e-commerce. Discuss the pros and cons of using the recommendation mechanism in an electronic shop. Do you like the recommendation mechanism of amazon.com? Why or why not?

Pros include the ability to make recommendations to customers that will lead them to buy items they might otherwise not have bought. They will also become more loyal as they recognize that the ability of the system to make meaningful recommendations depends on their purchasing from that shop, and will—since they appreciate these recommendations—tend to use that shop to increase its knowledge of their needs.

Cons include the cost of developing and running the system, the perception of some customers that it threatens their privacy, and the fact that they are not useful with all customers.

The question of whether or not one likes the recommendation mechanism of amazon.com is personal, with no single correct answer. (You may find some students who aren’t familiar enough with it to answer this question meaningfully.) Personally, I do, though its logic can be faulty: I bought a book from them because it was written by my brother, but it inferred that I’m interested in its subject and continues to suggest books on that topic even though I haven’t bought another in years.

14.

In a recent article, O’Reilly proposed the future Web as Web 2.0. Visit oreillynet.com to find the article “What is Web 2.0?” and related articles, and discuss the role intelligent agents can play in the new platform.

One of the central concepts of Web 2.0 is the harnessing of collective intelligence. This requires cooperation among multiple Web entities. Web services make this cooperation possible. (Amazon even provides its Web services in two forms. One, which uses the Simple Object Access Protocol (SOAP), is intended for major partners. The other, which uses XML, is for everyone else. The cited article quotes the firm as saying the simple service accounts for 95 percent of the usage.) (http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web20.html is a direct link to the referenced article.)

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

Describe the nature of the hiring decisions at Spartan and reasons that an intelligent system is suitable in this case.

The hiring decisions at Spartan Stores involves multiple-criteria decision making: the store wants employees who are competent, who interface well with customers and provide good service to them, and who are not likely to leave quickly. Among the management concerns with the earlier hiring process were inconsistency across locations,

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

291

cost (as measured in management time devoted to hiring) and less-than-ideal results due to insufficient attention to applicants’ crucial qualities.

An intelligent system was suitable in this case because the factors Spartan management wanted considered in this hiring decision could be measured by such a system. This provided the benefits of speed, consistency and savings in management time that are usually associated with automation.

2.

3.

List the benefits of the system at Spartan. Are there any other benefits that you can foresee that are not described here?

System benefits noted in the article include reduced turnover, increased efficiency of the hiring process, improved management visibility into the hiring process and better integration of the hiring process with other aspects of company operations.

The list of other possible benefits is open-ended.

What kind of knowledge structure seems to be appropriate for the development and operation of the intelligent hiring system? Review the techniques that you have learned in previous chapters and explain why you picked a particular technique and think it is appropriate.

If there is a sufficient base of information about previously hired people, some of whom were successful and some of whom were not (based on performance evaluations or some other measure), we should choose a method that takes advantage of this information.

Case-based reasoning, neural networks and genetic algorithms would all fall into this category. CBR would be a good choice if the nature of the available information lends itself to this type of matching with applicant data. If this is not the case, the choice between the other two can be made on the basis of developer familiarity and preference.

These methods are appropriate because they take advantage of existing data, rather than trying to define an a priori profile of the desired candidate, possibly introducing bias due to prejudices or preconceptions of the people who define that profile.

4.

How do you feel about being picked or rejected by a system instead of interviewing with a human manager? What issues can you foresee in the system at Spartan?

The answer to the first part of this question is personal. However, one can reasonably suppose that some people will not like this. These will tend to be those who are rejected, rather than those who are selected, since a selectee is unlikely to care why that happened.

This could lead to issues if the person objects and pleads that he or she would have been chosen in a personal interview. It may be necessary to develop well-thought-out answers to deal with this problem.

(Based on my understanding of U.S. law, this system is legal unless it has an adverse effect on the hiring opportunities of a protected class of people—for example, by asking questions that are not job-related, and whose answers are more likely to be known by people of some ethnic or religious backgrounds than others.)

292

5.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Can the applicant recommendation system be implemented as a Web service? What are the pros and cons for implementing it as a Web service as compared with implementing it as a traditional rule-based system?

The big advantage of implementing a system as a Web service is ease of communicating with it. Since this system uses input from other systems (such as the psychological screening system) and sends information to other systems (such as the WOTC eligibility assessment system) this sounds like a good fit. It would, however, be necessary for those other systems to have Web service interfaces as well.

(A system implemented as a Web service can still be rule-based internally. It might not be a “traditional rule-based system,” depending on how one defines “traditional,” but it could function much the same inside.)

CHAPTER

15

System Development and Acquisition

Learning Objectives for Part VI ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the process of building or acquiring a support system Describe the options of building versus buying systems Understand the selection criteria of development options Describe implementation issues related to MSS Describe integration issues related to MSS Describe MSS impacts and their future

Learning Objectives for Chapter 15 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the basic concepts of systems development Discuss the major steps in developing a decision support system (DSS) and management support system (MSS) application Describe the major MSS applications and list their major functionalities List the major MSS application development options, along with their benefits and limitations Describe the four phases of the system development lifecycle: planning, analysis, development, and implementation (PADI) Understand prototyping and throwaway prototyping and why MSS are typically developed using these methods Discuss various MSS application outsourcing options, including the use of an application service provider (ASP) and utility computing Describe some major MSS software packages and MSS application suites Describe various methods for connecting an MSS application to back-end systems and databases Discuss the value and technical foundation of Web services in integrated applications Understand the service-oriented architecture (SOA) and its relationship to MSS Describe the criteria used in selecting an outsourcing vendor and package Describe the factors that lead to MSS success or failure 293

294

⧫ ⧫

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Discuss the importance of project management and the skills a good project manager needs to have Understand the learning process that occurs during MSS development

CHAPTER OVERVIEW So far, the book has presented1 the concepts, components, and tools of decision making and decision support, business intelligence, intelligent systems … This chapter describes how to assemble the pieces to make them work. It covers the full range of options, from custom programming to contracting for “software-as-a-service” and everything in between. It also discusses several underlying concepts in the general area of systems integration, criteria for vendor selection and other areas that are important to understand before setting forth to develop a new MSS (or, indeed, any other new IS).

CHAPTER OUTLINE 15.1

OPENING VIGNETTE: OSRAM SYLVANIA THINKS SMALL, STRATEGIZES BIG TO DEVELOP THE HR INFONET PORTAL SYSTEM A. PROBLEM B. SOLUTION C. RESULTS  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

15.2

WHAT TYPES OF SUPPORT SYSTEMS SHOULD YOU BUILD? A. INTRODUCTION TO MSS DEVELOPMENT B. TYPES OF SUPPORT SYSTEMS 1.

Infrastructure 2.

Portals 3.

Specific Applications 4.

Tools and Tool Kits 5.

Platforms  Section 15.2 Review Questions

15.3

THE LANDSCAPE AND FRAMEWORK OF MANAGEMENT SUPPORT SYSTEMS APPLICATION DEVELOPMENT A. STEP 1: PLANNING, IDENTIFYING, AND JUSTIFYING MSS B. STEP 2: CREATING AN MSS ARCHITECTURE C. STEP 3: SELECTING A DEVELOPMENT OPTION D. STEP 4: INSTALLING, TESTING, CONNECTING, AND DEPLOYING MSS APPLICATIONS E. STEP 5: OPERATIONS, MAINTENANCE, AND UPDATING F. MANAGING THE DEVELOPMENT PROCESS

1

Some phrasing unabashedly borrowed from p. 565. Why mess with a good thing?

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual



295

Section 15.3 Review Questions

15.4

DEVELOPMENT OPTIONS FOR MANAGEMENT SUPPORT SYSTEMS APPLICATIONS A. IN-HOUSE DEVELOPMENT: INSOURCING 1.

Development Options for In-house Development B. METHODS USED IN IN-HOUSE DEVELOPMENT 1.

System Development Lifecycle 2.

Rapid Application Development 3.

Prototyping 4.

Buying Applications 5.

Leasing Applications 6.

Software-as-a Service C. OUTSOURCING DEVELOPMENT OPTIONS 1.

Outsourcing 2.

Application Service Providers (ASP) 3.

Utility (On-Demand) Computing 4.

A Hybrid Approach to MSS Development  Section 15.4 Review Questions

15.5

PROTOTYPING: A PRACTICAL MANAGEMENT SUPPORT SYSTEM DEVELOPMENT METHODOLOGY A. ADVANTAGES AND LIMITATIONS OF PROTOTYPING ⧫ Technology Insights 15.1: How to Make Prototyping Work 1.

Advantages of Prototyping 2.

Disadvantages and Limitations of Prototyping B. THE PROTOTYPING DEVELOPMENT PROCESS 1.

The Throwaway Prototyping Development Process 2.

Collaboration in Prototyping 3.

Evaluation in Prototyping C. IMPLEMENTING PROTOTYPING D. SUCCESSES AND FAILURES IN PROTOTYPING ⧫ Technology Insights 15.2: How Companies Speed MSS Deployment 1.

Prototyping Failures E. DATA WAREHOUSE VENDORS: SUCCESS AND FAILURES ⧫ Technology Insights 15.3: Data Warehouse Vendors: Success and Failures  Section 15.5 Review Questions

15.6

CRITERIA FOR SELECTING A MANAGEMENT SUPPORT SYSTEMS DEVELOPMENT APPROACH A. IN-HOUSE VERSUS OUTSOURCING MSS DEVELOPMENT ⧫ Technology Insights 15.4: To Buy or to Build  Section 15.6 Review Questions

296

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

15.7

THIRD-PARTY PROVIDERS OF MANAGEMENT SUPPORT SYSTEMS SOFTWARE PACKAGES AND SUITES A. SPECIALIZED AND FUNCTIONAL SOFTWARE PACKAGES B. MSS SUITES 1.

MicroStrategy 8 2.

Hyperion System 9 BI+ 3.

BusinessObjects XI 4.

Microsoft BizTalk Server 2004 ⧫ Application Case 15.5 Floriculture Partnership Streamlines RealTime Ordering 5.

Oracle’s MSS Products 6.

IBM’s WebSphere Commerce Suite  Section 15.7 Review Questions

15.8

CONNECTING TO DATABASES AND OTHER ENTERPRISE SYSTEMS A. CONNECTING TO DATABASES B. INTEGRATING MSS APPLICATIONS AND BACK-END SYSTEMS C. MIDDLEWARE  Section 15.8 Review Questions

15.9

THE RISE OF WEB SERVICES, XML, AND THE SERVICE-ORIENTED ARCHITECTURE A. THE NEED FOR INTEGRATION ⧫ Application Case 15.6 Lincoln Financial Excels by Using Web Services  Section 15.9 Review Questions

15.10 USER-DEVELOPED MSS ⧫ Application Case 15.7 End-User Development Using Wikis A. USER-DEVELOPED MSS: ADVANTAGES AND RISKS B. REDUCING THE RISKS OF END-USER COMPUTING C. TEAM-DEVELOPED MSS  Section 15.10 Review Questions 15.11 MANAGEMENT SUPPORT SYSTEMS VENDOR AND SOFTWARE SELECTION A. MSS DEVELOPMENT TOOL SELECTION 1.

Hardware Selection 2.

Software Selection 3.

Selecting Vendors and MSS Software Packages  Section 15.11 Review Questions 15.12 PUTTING TOGETHER A MANAGEMENT SUPPORT SYSTEM A. MSS IMPLEMENTATION ISSUES  Section 15.12 Review Questions Chapter Highlights

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

297

Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Internet Exercises ⧫ End of Chapter Application Case: A Fully Integrated MSS for Sterngold: An Old Dental Manufacturer Adopts New IT Tricks  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

The opening vignette of this chapter shows a successful development effort. It’s nice for students to see successful development efforts, but be sure to remind them that not every project turns out this way! One purpose of this chapter is to improve the odds that the students’ future projects will succeed too.

The Online File for this chapter contains information that is directly related to Review Question 5 of this vignette. In general, its Online File is a true extension of the text. Students will find helpful information there for studying this chapter. Specific connections are: • • • • • • • •

Online File W15.1: Section 15.1, Review Question 5 Online File W15.2: Less directly connected to specific content Online File W15.3: Section 15.4, Review Question 7 Online File W15.4: More depth for Section 15.5 Online File W15.5: More depth for Section 15.5 Online File W15.6: Less directly connected to specific content Online File W15.7: More depth for Section 15.9 Online File W15.8: Section 15.11, Review Question 1

The two sections after the opening vignette address two management issues: What are we going to build, and how are we going to build it? Most students who use this book will have seen these questions before, at a high level in their introductory MIS course and most likely in more detail in other courses such as Systems Analysis. Try to relate these topics (and the rest of the chapter, for that matter) to what they (should) already know. The emphasis in this course shouldn’t have to be on fundamental concepts, though the text covers them, but on what’s different about MSS/DSS. Section 15.2, by the way, probably won’t take much time. It basically says “here are types of systems you may have to work with.”

Sections 15.4 through 15.6 continue the theme of applying concepts students should already know to MSS/DSS. Real understanding requires more than just knowing how MSS/DSS are different, though. It requires knowing why they’re different. Issues such as strategic impact, frequency of use, the nature of the user community, and so on matter here. Understanding the

298

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

implications of having, for example, a management versus a clerical user community is part of what an educated IS professional should know.

Section 15.7 takes a quick look at a few MSS packages of various kinds. The purpose is not to compare them: for one thing, they’re not directly comparable; for another, the descriptions are not parallel. The purpose is to give the flavor of what’s out there. I find discussions such as this to be good jumping off points for discussing one or two of the packages mentioned. There’s no time to do them all in detail.

Sections 15.8 and 15.9 have a common theme: making the pieces work together. This recognizes the reality that fewer and fewer MSS are built as monolithic entities. Students may not finish these sections as experts on database connectivity or whatever, but they should be in a position to appreciate the concept and to jump in as junior team members without having to be told what these are all about.

(If you covered part V of the book, by the way, you’ll see some overlap between Section 15.9 in this chapter, and Section 14.9 there, in areas such as Web services. You may want to cross-reference the two as a way to reinforce the unity of the field.)

End-user computing (Section 15.10) and vendor selection (Section 15.11) are, once again, topics that students have probably seen before in a general context. As with the earlier sections, you—and they—should be asking “What’s different?” here. (Not much will be different in enduser computing, by the way, because nearly all its non-trivial applications are MSS/DSS to begin with. It’s hard to imagine an organization using this approach for its new billing or inventory management system.)

The end-of-chapter case, about dental materials supplier Sterngold, is about an MSS that is at the same time more than an MSS. It’s also an e-commerce system and a few other things besides. This shows how MSS capabilities are being integrated more and more into other systems, to the point that it can be hard to tell the difference. Questions students might think about, which are not in the text, might be: What, exactly, was it about the approach they took that enabled Phase I of the system to be completed in less than three months, versus two or three years if it had been done in-house? Is this an apples-to-apples comparison? How much of the time savings could they have obtained with in-house development if they had adopted the methods Surebridge used? Would this have been practical? Why or why not?

(Regarding this case: Surebridge was acquired by NaviSite in June 2004 and no longer operates under its own name. The URL http://www.surebridge.com now redirects to http://www.navisite.com.)

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 15.1 Review Questions 1.

What was the strategic business need in this case? What were the benefits of the completed HR InfoNet system? Explain.

The strategic need was to improve benefits administration at Osram Sylvania. (They chose to achieve this by creating an interactive Web portal, but that was not the underlying need.)

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

299

Students might move up the management perspective ladder and reply that the strategic need was to improve employee satisfaction, or even higher up that it was to reduce turnover. I feel these are valid answers as well. If you want a particular perspective, you might want to make this explicit.

The tangible benefit was a 251 percent ROI over three years. This reflects savings of $500,000 per year, or $1.5 million over the period, and presumably expenses of about $600,000 including development, enhancements and operation for that period. (The case doesn’t give this number, but the ROI figure implies it.)

Intangible benefits include shorter recruiting cycle time, higher HR productivity (which could perhaps be quantified), improved employee satisfaction because of userfriendly benefits administration (which presumably leads to improved morale, hence lower turnover) and most likely others.

2.

Why was it important to have an IT champion, a functional business (HR) champion, and an executive champion involved in the project?

Each has a different role. The IT champion makes sure the IT staff on the project understand its importance and its user community. The functional business champion enlists support for the project, whose benefits are still conjectural but whose costs (financial and impact on the time of employees in that area) are here today, within his or her part of the company. The executive champion ensures that the necessary resources (financial, management time, etc.) are available. Absent any of these, any IS project is less likely to succeed than it is if all are present.

3.

Who were the users of the project? What decisions did the system assist the users in making?

The primary user community was Osram Sylvania employees. The system assisted them in making decisions about benefit choices. (It also helped them carry out decisions such as enrollment changes, but that is beyond the scope of this question.)

A secondary user community was Osram Sylvania management (in that role; of course they are employees as well). It helped them make decisions about hiring, benefits plans, bonus programs and other personnel-related decisions.

4.

How were the users involved in the system development? How was management involved?

As far as one can tell from the case, users were not involved in system development.

Their involvement began when the first small part of the system was rolled out. As each part of the system went into use, the development team used user feedback to plan future parts.

Management was involved via the three roles discussed in Question 2 above and as users of the system. Some management uses of the system, such as hiring, were in addition to benefits administration uses that applied to all employees.

300

5.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Do you feel that if the development team had “thought big”—that is, tried to design and develop a total solution over a long period of time—it would have succeeded? What could have gone wrong? Do you think the team knew this in advance?

There is no single right answer to this question; it’s a matter of opinion. Personally, I think their chances of success would have been lower. The need to continue to spend money and take up management time over a year or more, with no visible results, could have led to project cancellation. Not involving users in development was tolerable with quick and frequent roll-out of small pieces, giving an opportunity for regular feedback, but could have been disastrous with an extended schedule. The existence of tangible and intangible benefits from the first roll-out on was, I feel, a major factor in continued management support of the project.

6.

What technology issues, behavioral issues, and implementation issues had to be worked through in developing the HR InfoNet system?

The major technology issue was the choice of a development platform. The team selected Lotus Notes with a Domino server. They also had to select kiosk hardware (not further specified in the case) and software (standard browsers with special security features).

Finally, as they made these decisions, they were concerned with scalability and integration with their existing systems.

Behavioral issues included motivating employees, both regular employees with benefit concerns and hiring managers, to use the system.

Implementation issues included the need for employee training. The case mentions that hourly employees were given network IDs and training. Perhaps office workers already had network IDs and enough computer literacy to minimize, if not totally avoid, a need for training.

7.

What implementation approach was adopted? Why? In-house development, or “insourcing” in the terminology of Section 14.4, using a purchased package that was customized to Osram Sylvania’s needs.

8.

Comment on how success breeds success in MSS development.

“Success breeds success” refers to the benefits of even a small success. Having a visible success leads management to believe that it can be done again (and hence greater willingness to allocate resources to it), leads users to support development activities with their time, and improves development team morale. All these improve the likelihood that future projects will be successful.

Section 15.2 Review Questions 1.

Distinguish between in-house developed and vendor-built systems.

The difference is in whether programming, testing, etc., are done by a firm’s employees (including, for this purpose, contract programmers operating under the firm’s supervision and functioning for all practical purposes as an extension of its staff) or by an outside firm operating independently.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

301

Once a system is developed, it will usually be impossible to tell who built it.

2.

List the different types of support systems. (“Supported” in the text is a misprint for support, to be corrected in the second printing.)

Types of support systems include infrastructure, portals, specific support applications, tools and toolkits (sometimes written as two words), and platforms.

3.

Define infrastructure and list some MSS examples.

Infrastructure is the hardware and/or software that underlies an application. In this book this is usually a decision support or management support application, but the concept is general.

Examples of MSS infrastructures include data warehouses and business intelligence systems, knowledge management systems, enterprise information systems, and portals.

Section 15.3 Review Questions 1.

List and briefly describe the major steps in developing an MSS application.

The major steps in developing an MSS application are: Step 1. Planning, identifying, and justifying MSS Step 2. Creating an MSS architectureStep 3. Selecting a development option Step 4. Installing, testing, connecting and deploying MSS applications Step 5. Operations, maintenance, and updating

2.

Define the various types of testing used during the MSS application development process.

• • • •

Unit testing: testing each module individually Integration testing: testing all modules acting together Usability testing: testing the user experience interacting with the system Acceptance testing: confirming that the application meets its business objectives

Answers found in other sources may differ slightly from this list, though they’ll follow the same general lines. Students may find the terms “alpha testing,” which corresponds to usability testing carried out by members of the development organization, and “beta testing,” in which the usability testing is extended to a small group of willing customers or end users. They may also find definitions of “acceptance testing” that focus more on contractual issues than on meeting objectives.

Section 15.4 Review Questions 1.

Define in-house development (i.e., insourcing).

In-house development is the development of MSS by programmers employed by the organization that will use the system, in order to meet its unique requirements. It may

302

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

involve custom programming, use of externally developed components, integration and/or customization of those components, or a combination of these.

2.

List some of the pros and cons of using packaged MSS applications.

Advantages of packaged applications include: • Many different types of off-the-shelf MSS packages are available.

• They save time and money (compared to in-house development).

• The company need not hire programmers specifically dedicated to an MSS project.

• The company knows what it is getting before it invests in the MSS. • The company is neither the first nor the only user.

• The vendor updates the MSS frequently Disadvantages of packaged applications include: • MSS packages may not exactly meet the company’s needs.

• Packages may be difficult to modify, or may require huge business process changes.

• The company may experience loss of control over improvements and new versions.

• Off-the-shelf MSS applications can be difficult to integrate with existing systems.

• Vendors may drop a product or go out of business.

3.

Define outsourcing. Is outsourcing a viable option for MSS provision?

Outsourcing is the use of outside contractors to develop an application. (This does not include the use of contract programming staff to augment a firm’s employees.) It is a viable option when many applications must be built quickly and specialized expertise, which the sponsoring organization does not have, is needed.

(Some people confuse outsourcing with sending work to regions with low wage scales. The two concepts overlap, saving money through this mechanism is one reason for outsourcing, but are not the same. A company can obtain low labor rates by setting up its own operation in another region without outsourcing. Outsourcing, conversely, can be to a company across the street. Sending work to low labor-rate areas is often called offshoring to make the distinction clear.)

4.

Describe the major forms of MSS application leasing.

The two major forms of leasing an application are to lease it to run on one’s own computers, and to lease its use while running on the user’s behalf, but on the provider’s computer.

5.

List some of the alternative leasing (and hosting) options.

Two leasing options are in the answer to the previous question. Additional options include “software-as-a-service,” for short-term rental of software, typically accessed via a browser; and using an application service provider (ASP) to manage the entire process of providing one.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

6.

303

What are the major factors driving a switch to software-as-a-service?

The major factors driving the switch to software-as-a-service are reducing the risks involved in acquiring new software, influencing product and service quality via an ongoing relationship with vendors, and being able to change usage commitments as business circumstances change.

7.

Define utility computing and describe its benefits.

Utility computing refers to the use of a pool of computing power on an as-needed, or ondemand, basis. (It is also called on-demand computing for this reason.) Its benefits include the availability of resources when they are needed, without having to pay for them when they are not needed; and the added reliability that comes with the provisioning of extra resources for occasional use.

(Utility computing also has drawbacks, if it didn’t it would be more widely used, but this question doesn’t ask about them.)

8.

List the major in-house development methods.

In-house development methods discussed in this section include the traditional system development life cycle (SDLC; “life cycle” can also be written as one word, but the acronym reflects both parts); rapid application development (RAD) and prototyping.

Students may also mention end-user development (or computing), discussed later in this chapter in Section 15.10.

Section 15.5 Review Questions 1.

Define prototyping. What are the advantages and limitation of prototyping is MSS development?

Prototyping is a system development method in which users provide direct feedback on user-visible aspects of a system under development, rather than approving specifications and learning later how the resulting system will look and behave in use.

Advantages of prototyping include short development time, short time until user feedback is obtained, improved user understanding of the system and low cost.

Limitations of prototyping include losing the benefits of the SDLC. These benefits include a thorough understanding of a system’s benefits and costs, a detailed description of the business’s information needs, an easy-to-maintain information system design, a well-tested information system, and well-prepared users. (Ways to minimize or avoid these are beyond the scope of this question.)

2.

Describe the prototyping development process.

The prototyping process begins with planning and preliminary analysis, much as in the SDLC. Next, it iterates through analysis, design and programming (implementation), adding functionality in each iteration along with user feedback on what has been done so far and making changes to reflect that feedback. Eventually, the system evolves to a stable, comprehensive state.

304

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

The above description applies to evolutionary prototyping. An alternative approach, throwaway prototyping, uses the prototype as a substitute for the paper specification of the traditional SDLC but does not incorporate it into the eventual system.

3.

Should prototyping be used on every MSS development project? Why or why not?

“Every” is a strong word. The advantages of prototyping apply to all MSS, as do its drawbacks. However, their relative importance varies with the situation. In most MSS it is important to reflect user needs and to deploy a system quickly, so prototyping is a good choice. Where formal agreement on what a system will do is important, as when a system is developed by another firm under a fixed-price contract, it is less appropriate (and in other situations that could be used as examples here).

4.

Why is collaboration between developers and users important to the success of prototyping?

The essence of prototyping is rapid feedback from users about the proposed functionality and interface of the new system. When there is no collaboration, this feedback cannot happen effectively.

Section 15.6 Review Questions 1.

List five of the major criteria to consider when deciding whether to buy or lease an MSS application.

The text lists flexibility, information requirements (of users), user-friendliness, hardware and software resource requirements, installation effort, maintenance requirements, vendor quality and track records, estimated costs, personnel needs, ability to adapt to new technologies, scalability, sizing, performance, reliability, and security. A correct answer can include any five of these (or more, if you are willing to take the question’s requirements less literally.)

2.

Define latency.

Latency is “the time required to complete an operation” (p. 688).

Students who have studied IT hardware may be used to a different definition: that latency is the time required until data transfer, or the important part of any other operation, can begin. For example, in a disk drive, latency is seek time plus rotational delay. Overall performance depends on latency and transfer rate in ways that are beyond the scope of this note.

3.

Define throughput.

Throughput “measures the number of operations performed in a given period of time” (p.

688).

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

4.

305

Describe in-house MSS development versus outsourcing of MSS development.

These concepts were described in the answers to Review Questions 1 and 3 of Section 15.4.

Section 15.7 Review Questions (The first three answers below use only the information in the text. More information on these products is available from the respective firms’ Web sites and other sources. If you ask about these products, consider asking students to research them beyond the text.)

1.

Describe the functionalities of MicroStrategy 8.

MicroStrategy 8 is an MSS suite that provides scorecards and dashboards, reporting, OLAP, advanced analysis, and alerts and proactive notification through a single Web interface.

2.

Describe the functions supported by Hyperion System 9 BI+.

The functions it supports include reporting (both operational and financial), dashboards (which can be personalized), and analysis (interactively over the Web, with several kinds of analyses available, or by data transfer to Excel).

3.

Describe the basic business systems in Microsoft’s BizTalk Server.

Microsoft BizTalk Server is a server software product that links applications through shared data and can automate business processes by activating a series of applications according to predefined rules. Its major systems are Business Activity Monitoring and Business Activity Services.

(The current version is BizTalk Server 2006. It has enhancements over BizTalk Server 2004, but the concepts have not changed. An excellent resource for BizTalk Server 2006 is the downloadable white paper at: http://www.microsoft.com/biztalk/techinfo/whitepapers/understanding.mspx .)

4.

Describe the key MSS suites provided by Oracle.

(This answer uses information from http://www.oracle.com/bi . Some of its content is more recent than the text. It may therefore differ from it slightly.)

Oracle’s major MIS product, Oracle Business Intelligence, is available as two different suites: Oracle BI Standard Edition and Oracle BI Enterprise edition. The Standard Edition includes Discoverer, an end-user interface to access databases and create dashboards; a spreadsheet add-in to navigate OLAP data from within Excel, Warehouse Builder for rapid design and deployment, BI Beans to build Java-based applications, and the Reports reporting tool. Enterprise Edition builds on these basic capabilities, extending them across the enterprise via portals, RSS feeds, workflow management software, enhanced support for non-Oracle databases, and more.

In addition, Oracle offers a number of BI applications. These include Oracle Daily Business Intelligence, discussed in the text. They also include Oracle Corporate

306

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Performance Management and other applications obtained through Oracle’s purchase of PeopleSoft and Seibel.)

Section 15.8 Review Questions 1.

Describe the basic elements of a multitiered application architecture.

A multi-tiered (sometimes without a hyphen) application architecture consists of four elements: • Client (Web browser), to handles the user interface • Web server, to deliver Web pages, collect user input and pass data to/from the next tier • Application server, to perform the application’s business logic using data from the next tier • Database server(s), in which data are stored and which process data requests

2.

List the ways in which an MSS application can be connected to back-end databases and other transaction processing systems.

Several technologies can be used to connect to back-end databases: • A commercial MSS suite may have its own integration capabilities.

• Web scripting languages can be used.

• Specialized application servers can have tools to make the job easier.

The major way to connect to transaction processing systems mentioned in the text through the use of Enterprise Application Integration (EAI) products.

3.

Define middleware and describe its attributes.

Middleware is a generic term for software to connect an application to multiple databases, here a data warehouse. It hides application distribution from databases and platform heterogeneity from applications, provides standards for application cooperation, and supports common services.

Section 15.9 Review Questions 1.

List some reasons it is difficult for applications running in different environments on different computers to communicate with one another.

It can be difficult for such applications to communicate because of platform differences, because of changes in their environments, and because of security barriers.

2.

Define Web services.

A Web service is “a software system identified by a uniform resource identifier (URI), whose public interfaces and bindings are defined and described using XML” (World Wide Web Consortium as cited on p. 696). Their full definition does not require that XML be used, but in common usage the term applies to those that use SOAP-formatted XML envelopes (SOAP originally stood for Simple Object Access Protocol, but the

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

307

original term was officially dropped in July 2003) and whose interfaces are described by WSDL (Web Services Description Language).

Section 15.10 Review Questions 1.

Define end-user computing.

“The development and use of computer-based information systems by people outside the formal information systems area” (p. 697). Used here as synonymous with end-user development, though in other contexts “development” implies a higher level of technical skill.

2.

What are the advantages and risks of user-developed MSS? Advantages of user-developed MSS include: • Rapid response and short delivery time, no wait for IS to schedule and carry out development • No need for formal user requirements specifications: the user knows what he or she needs • Reduction in implementation problems • Potential cost savings Risks of user-developed MSS include: • Poor quality due to a tendency to ignore controls, testing procedures, and documentation • Use of substandard or inappropriate tools and facilities, also leading to poor quality • Risks associated with the development process (inability to develop workable systems, developing systems that generate erroneous results) • Data management risks (loss of data; use of stale, inappropriate, or incorrect data).

• Security risks due to users’ unfamiliarity with security measures.

• Lack of documentation and maintenance procedures

3.

List some control issues related to end-user computing.

This question can be read in two ways: who should control end-user computing, or what controls are required for end-user computing? Since this question is not answered in the text, students will need to find the information elsewhere, and are equally likely to come up with sources that interpret it either way. If you prefer one interpretation or the other, you may want to make this clear.

If we interpret it as who should control EUC, reasons for its proliferation may include a desire to take control away from the IT department. Over-control by users can result in duplication of applications, incompatibility, lack of integration, and low-quality systems in several respects (including security, documentation, verification and more).

Valuable resources within IT are then diverted to support amateur users who produce badly written systems of little strategic value. Much of the MIS literature, which of course tends to be written from the IT professional’s standpoint, therefore emphasizes a need for IT departments to manage EUC through restrictions on users: standards, auditing

308

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

and control mechanisms which seek to make end users behave like IT professionals.

However, over-control by the IT department alienates end users and leads to conflict.

(Much of this answer is based on N. McBride and A. T. Wood-Harper, “Towards useroriented control of end-user computing in large organizations,” Journal of End User Computing, Jan. 2002.)

If we take it as what should be controlled in EUC, the list may include development standards, database access, documentation, testing and verification, choice of tools, and more. The list is potentially endless.

Section 15.11 Review Questions 1.

List the major steps in selecting an MSS application vendor and package.

1.

Identify potential vendors.

2.

Determine the evaluation criteria.

3.

Evaluate vendors and packages.

4.

Choose a vendor and package.

5.

Negotiate a contract.

6.

Establish a service-level agreement.

As noted in the text, Online File W15.8 has more on each step.

2.

What factors make the selection of MSS software difficult?

Issues related to the application and the requirements: • At the time of selection, information requirements and outputs are not completely known • Required capabilities may change from application to application • Portions of a large MSS may call for different tools Issues related to products: • There are hundreds of software packages on the market • Software packages are updated very frequently, so earlier knowledge is no longer valid • Prices change frequently • Commercially available evaluations are subjective and often superficial, especially for MSS-related features of tools or packages that are primarily used in non-MSS applications Issues related to the selection process itself: • The decision involves multiple criteria, some of which may be intangible and/or conflicting • Several people may be involved on the evaluation team (group decision making!)

• Technical, functional, end-user, and managerial issues must all be considered • Multiple-vendor issues, such as the desirability of staying with a few vendors and the limited availability of open-system environments to mix and match products, must be considered

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

309

Section 15.12 Review Questions 1.

Why is integration critical to the success of MSS project development?

Integration has always been important to MSS, since it has always used databases that were originally developed for other purposes. It is especially critical now that MSS tools have migrated to the Web and that collaboration and communication tools are readily available to decision makers.

2.

What are the issues and trends that affect the development of MSS projects?

Several issues and trends continue to affect MSS applications. They include: • Managers are more readily accepting MSS tools, techniques, and methods.

• Artificial intelligence tools and methods (e.g., expert systems, neural networks, genetic algorithms, fuzzy logic) are being embedded in MSS and in enterprise applications.

• Web technologies continue to enable new developments in MSS/BI—from data, information and knowledge access to direct communication and collaboration.

• GSS continue to proliferate through collaborative computing.

• Computer technology continues its fast evolution. Capabilities are increasing dramatically, and costs are decreasing. This leads to greater capabilities being embedded in MSS. • Enterprise resource management (ERM)/ERP systems, though expensive, are proliferating. These systems often provide and incorporate MSS methods for improved decision-making.

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Discuss the advantages of leasing an MSS application over purchasing one.

“Leasing an MSS package can result in substantial cost and time savings. In cases in which extensive maintenance is required or in which the cost of buying is very high, leasing is very advantageous, especially for small to medium enterprises. Leasing is also advantageous when a company wants to experiment with an MSS package before making a heavy up-front purchase investment, protect its own internal networks, quickly utilize the application, or rely on experts to establish a major project, usually with a Web site” (p. 676).

2.

What type of development approaches would be most appropriate for developing MSS? What type of development approaches would be most appropriate for developing very large systems? Why? What other factors need to be considered?

Prototyping is usually the most appropriate development method for MSS because users and managers are involved in every phase and iteration; learning is explicitly integrated into the design process; time is saved by bypassing the need for formal information requirements definition; and if offers low cost, short development time, short user

310

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

reaction time, and improved user understanding of the system. These are important when a system will have relatively low usage frequency (compared to most transaction processing systems) but a high impact on decisions that are important to the “bottom line” and will be used primarily by managers and knowledge workers, who are used to having some control over their work environment. (There are several varieties of prototyping; the above generally applies to all.)

Very large systems often have different priorities and hence may suggest different development method choices. The formal SDLC, while it may be cumbersome, can be necessary when details must be worked out in advance, when usage frequency is high so performance considerations matter, when financial data are involved, or where fixedprice development contracts are part of the project.

3.

A large manufacturing company elects to use an MSS suite to develop its MSS application. How would the company determine whether to outsource the suite or run it in-house?

“Large companies may choose outsourcing when they want to experiment with new MSS technologies without a great deal of upfront investment or a need to rely on experts” (p.

678). Absent a factor such as these, large companies would do development work inhouse.

(This question asks specifically about a large company. Large companies have inhouse developers they can use if they want to. Answers that discuss factors that apply to companies of any size reflect sloppy reading and do not answer the question. The added fact that it is a manufacturing company does not, in this case, affect the answer. It is common in business situations for a key point in any decision to be less than obvious and to have information that is not needed for the decision. This question offers you an opportunity to teach this lesson.)

4.

Why is the traditional SDLC an inappropriate methodology for developing most MSS? See the answer to Question 2 above. The tradeoffs for most MSS favor higher user involvement and rapid application delivery even at the expense of higher development cost. The SDLC offers the opposite set of trade-offs, so it is more appropriate for other types of applications.

5.

Explain why prototyping is also known as evolutionary development. Why is it appropriate for MSS? Prototyping is also known as evolutionary development because (in most variations on prototyping; there are exceptions) features and functions are added to the prototype until it evolves to become the complete application.

Its appropriateness for MSS is discussed in the answer to Question 2 above.

6.

How does an iterative process secure more user input than conventional development approaches?

An iterative process requires user feedback at every stage, not just at a formal “requirements definition” point in the development process. Also, the interactive nature

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

311

of feedback in an iterative process is more conducive to user involvement than written specifications are.

7.

How can a user develop a better understanding of a decision-making problem by using the iterative process?

As users work with each version of the system being developed, they are forced to decide what they like about that version and what needs changing. This forces them to think about the decision-making problem in ways they probably never did before and leads to better understanding of it.

8.

What are the disadvantages of letting an MSS evolve from a small prototype rather than having complete specifications for the MSS? See the last part of the answer to Review Question 1 of Section 15.5.

9.

A firm decides to make its MSS more dynamic by tying its application to a back-end data warehouse. What are some of the ways in which the firm could accomplish this task?

See the first part of the answer to Review Question 2 of Section 15.8.

10.

Why is component reuse important? What are its advantages and disadvantages?

Component reuse is important because it improves productivity by not “reinventing the wheel” at considerable effort and expense each time software to perform a given function is needed.

The advantages of component reuse correspond to the reasons for its importance.

Another advantage, not mentioned in the text, is that a programmer who knows his or her work may be reused is likely to be more careful in programming, to write cleaner code with fewer errors, to test more thoroughly, to document better and in general to produce a higher-quality program.

Disadvantages of component reuse include: • Reuse standards must be developed, published, and enforced. This has a cost.

• Components must be designed for reuse. This increases their development effort.

• Components must be documented for reuse. This is a cost, though it should be small if they would be properly documented for maintenance in any case.

• A component repository is needed and must be managed, which is a cost.

• Reused components may not be as well suited for their new purpose as a custom-designed component would have been, resulting in a lower-quality application.

• Some programmers take reuse as infringing on their professional freedom.

For these and similar reasons, reuse in practice has been limited to two ends of the size spectrum: very small routines whose function is fundamental to many systems, and total functional units that can be used for a new task or customized for it easily. Attempts to reuse components of intermediate size have generally not been successful.

312

11.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Discuss how to select MSS software. What makes this selection so difficult?

The steps in selecting MSS software are listed in the answer to Review Question 1 of Section 15.11. They are discussed further in Online File W15.8. Factors that make the selection difficult are listed in the answer to Review Question 2 of that section. The list is taken from pages 700–701 of the text, with the items organized into three categories.

12.

You have decided to use a third-party application to develop and deploy a Web-based MSS. Create a checklist for determining which third-party MSS application products will best meet your application requirements.

Open-ended answer. It will depend on the type of MSS a student has in mind. (You may want to specify this more precisely than the question does in order to get consistent, comparable answers.)

(This question asks about how well the product meets the application requirements. The choice will also depend on factors such as vendor stability, vendor strategic direction, vendor support, price, etc., which do not fall under this heading.)

13.

Identify the major reasons utility computing tools may become the next big thing in MSS applications development.

Utility computing is poised to expand as several technologies intersect: Web services, grid computing, provisioning. It has the potential to deliver access to specialized resources such as supercomputers, new revenue streams for commercial data centers, new application pricing models based on metered use, and an open computing infrastructure for companies with limited IT maintenance budgets. These tools provide flexibility in IS development, from in-house and self-managed projects to fully outsourced projects and everything in between. Their offering something to everyone—users, vendors of various sorts—is an indication that their use will expand.

(Information to answer this question can be found in Online File W15.3.)

14.

Discuss the reasons user-developed MSS can be of poor quality. What can be done to improve the situation?

Reasons are listed in the second part of the answer to Review Question 2 of Section 15.10.

The situation can be improved by careful monitoring and assistance, providing guidance, and a process whereby the IS department could validate the system and license its use to a given level of the organization.

15.

End-user systems developers usually work for managers whose IT knowledge is limited.

Discuss the types of problems this situation could lead to and suggest possible ways of dealing with these problems.

This type of situation could lead to conflict between manager and subordinate if the manager feels that the subordinate is “wasting time” instead of doing his or her “real job,” while the subordinate feels that the development work contributes to the “real job.”

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

313

This difference of opinion could be reflected in the subordinate’s performance evaluation and hence raises, promotions, etc.

These problems can be dealt with in several ways. One is by educating the manager: not to the point of becoming an IT expert or even a “power user” such as the developer in question probably is, but to the point of appreciating the reasons for enduser development and recognizing that it is not a waste of time on that person’s part.

Another possible approach is for the developer to make sure that the manager is kept informed about all his or her development activities and knows why they are an essential, or at least a helpful, part of his or her job. A third possibility is for a high-level executive, or even a more senior manager, who believes in the benefits of end-user development to have a chat with the manager in question.

(Other correct answers to both parts of this question are possible.)

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

Sterngold used an outsourcer to build a custom-made MSS application. What questions or issues should a CIO consider when he or she faces the question of whether to buy an MSS application, build it, hire an ASP to provide it, or do without it entirely?

In the case of Sterngolds’ MSS, the question had a simple answer. They did not have the capability to build it in-house (and did not want to staff up temporarily for that purpose) and it was too specialized for an ASP to want to offer it. (The ASP business model depends on having a large potential customer base for most applications.) Therefore, they had no choice. If they wanted this system, they had to outsource its development.

However, the question goes beyond Sterngold and asks about CIOs in general.

(With just 90 employees, only one of them in IT, Sterngold is unlikely to have had anyone with this title.) Issues that a CIO should consider in this situation are best addressed in a specific sequence: First, it should be determined if the application is sufficiently popular that packages and/or ASPs exist for it. If they do, custom software will not be justified except for the largest firms or those with the most unusual needs.

If only one of these options is available, it should probably be used.

If both ASPs and packages are available, their total cost of ownership (TOC) should be compared for the firm’s planning horizon. The cost of using a package should include any component integration costs. The cost of using an ASP should include estimated price increases.

If custom software is required, the alternatives of insourcing and outsourcing should be compared. A key consideration is the availability of in-house resources. If the firm does not have the required expertise today but expects to use it in the future, a hybrid team of company employees and outside experts, charged with technology transfer as part of their engagement, can work well.

After having determined the most cost-effective way to obtain the application in question, the CIO can decide if it’s worth doing at all. A decision before this point would be made without crucial information. This choice should be made earlier only if it is clear that all options are so expensive that the application cannot be justified no matter what, or

314

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

conversely when at least one alternative is found that is inexpensive enough to make the application cost-justified.

2.

Use of an outside vendor enabled Phase I of the MSS of Sterngold to be finished in less than three months rather than the two to three years that would have been required had it been done in-house. Should fast deployment be a major consideration in the decision to build, buy, or lease an MSS application?

Yes. The value of using an MSS, to an organization, is high compared to its development cost.

3.

Critical to the Sterngold MSS project was the need to integrate the ordering system with the back office (i.e., order fulfillment, inventory, accounting, payment). Why is integration important to the success of MSS? See the answer to Review Question 1 of Section 15.12.

4.

Sterngold created a team to implement the MSS application. Explain why the implementation of an integrated MSS requires a study of existing processes and redesign of the processes that needed alteration.

Implementing an integrated MSS, or any new system, requires study of existing processes because: • The new system should reflect business needs. Existing processes also reflect those needs: perhaps imperfectly or not as effectively as today’s technology permits, but they do. Studying existing processes is one way to learn about what these needs really are. This may not be the same as what they are supposed to be.

• No system operates in a vacuum. The new system will have to interface with other systems that support other processes. It is important to know what these are.

In some cases it will be found that existing processes can be improved. This possibility may arise from new insights or new technologies. In other cases a process will have to be modified to work with the new system; it may not be improved, but it will be different (and hopefully not worse). As a third possibility, some components of an MSS package may include inherent processes that a firm must adopt in order to implement the package. (That was not an issue in the current case.) In all these situations, processes should be redesigned before system development work is done.

(A cynic who has worked with MIS outsourcing firms might also suspect that maximizing billable hours had something to do with the use of a team approach. That would be uncharitable, but students should be aware that it happens. If they’re on the client side of this type of work, they should be on their guard for it.)

CHAPTER

16

Integration, Impacts, and the Future of Management Support Systems

Learning Objectives for Chapter 16 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Understand the need for systems integration for management support systems (MSS) Describe the difficulties in integrating systems Describe major models of MSS integration Describe MSS integration with enterprise systems Describe organizational impacts of MSS Learn the potential impacts of MSS on individuals Describe societal impacts of MSS List and describe major ethical and legal issues of MSS implementation Define digital divide and discuss how to close it Provide an overview of the future of MSS

CHAPTER OVERVIEW This, the last chapter in the printed portion of the book, “wraps it all up.” (Online Chapters 17 and 18 come after this one in the numbering sequence but don’t follow it in terms of topic sequence.) It has two main parts: MSS integration, covered in sections 16.2–4 and the end-ofchapter case, and the broader impact of MSS, sections 16.1 and 16.5–11. (Section 16.12, a look into the future, is outside this classification.) When students finish this chapter, they should have a well-earned sense of accomplishment!

CHAPTER OUTLINE 16.1

OPENING VIGNETTE: ELITE CARE SUPPORTED BY INTELLIGENT SYSTEMS A. PROBLEM B. SOLUTION

317

318

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

C. D.

RESULTS  Questions for the Opening Vignette WHAT WE CAN LEARN FROM THIS VIGNETTE

16.2

SYSTEMS INTEGRATION: AN OVERVIEW A. TYPES OF INTEGRATION B. WHY INTEGRATE? C. LEVELS OF MSS INTEGRATION 1.

Embedded Systems 2.

Integration and the Web  Section 16.2 Review Questions

16.3

TYPES OF MANAGEMENT SUPPORT SYSTEMS INTEGRATION A. MODELS OF INTEGRATING ES AND DSS 1.

Intelligent Systems Attached to DSS Components 2.

An ES as a Separate Component in a DSS B. ISSUES AND CONFIGURATIONS IN MSS INTEGRATION ⧫ Technology Insights 16.1: Integrated Fleet Management Systems Improve Performance 1.

Feedback Loops in Integrated Systems  Section 16.3 Review Questions

16.4

INTEGRATION WITH ENTERPRISE SYSTEMS AND KNOWLEDGE MANAGEMENT A. INTEGRATION WITH ERP AND SCM SYSTEMS B. INTEGRATION WITH KMS 1.

Integration of KMS with Other Business Information Systems 2.

Integrating DSS and Knowledge Management 3.

Integration of Intelligent Systems and Knowledge Management 4.

Other Knowledge Management-Related Integration C. INTEGRATION APPROACHES ⧫ Technology Insights 16.2: An Oracle BI Integrated System  Section 16.4 Review Questions

16.5

THE IMPACTS OF MANAGEMENT SUPPORT SYSTEMS: AN OVERVIEW  Section 16.5 Review Questions

16.6

MANAGEMENT SUPPORT SYSTEMS IMPACT ON ORGANIZATIONS A. NEW ORGANIZATIONAL UNITS B. ORGANIZATIONAL CULTURE C. RESTRUCTURING BUSINESS PROCESSES AND VIRTUAL TEAMS 1.

Simulation Modeling and Organizational Restructuring D. THE IMPACTS OF ADS SYSTEMS E. OTHER ORGANIZATIONAL IMPACTS  Section 16.6 Review Questions

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

16.7

MANAGEMENT SUPPORT SYSTEMS IMPACTS ON INDIVIDUALS A. JOB SATISFACTION B. INFLEXIBILITY, DEHUMANIZATION, STRESS, AND ANXIETY 1.

Job Stress and Anxiety C. COOPERATION OF EXPERTS  Section 16.7 Review Questions

16.8

AUTOMATING DECISION MAKING AND THE MANAGER’S JOB A. THE AFFECT OF MSS ON MANAGERS’ ACTIVITIES AND THEIR PERFORMANCE B. CAN MANAGERS’ JOBS BE FULLY AUTOMATED? 1.

Can Business Analysts’ Jobs Be Fully Automated?  Section 16.8 Review Questions

16.9

ISSUES OF LEGALITY, PRIVACY, AND ETHICS A. LEGAL ISSUES B. PRIVACY 1.

Collecting Information About Individuals 2.

The Web and Information Collection 3.

Homeland Security and Individual Privacy C. ETHICS IN DECISION MAKING AND SUPPORT 1.

Non-Work-Related Use of the Internet  Section 16.9 Review Questions

319

16.10 INTELLIGENT AND AUTOMATED SYSTEMS AND EMPLOYMENT LEVELS A. THE IMPACT OF MSS ON EMPLOYMENT LEVELS ⧫ Application Case 16.7: Robots  Section 16.10 Review Questions 16.11 OTHER SOCIETAL IMPACTS OF MANAGEMENT SUPPORT SYSTEMS, THE DIGITAL DIVIDE A. REPRESENTATIVE POSITIVE EFFECTS OF MSS 1.

Work in Hazardous Environments 2.

Opportunities for the Disabled and Elderly 3.

The Changing Roles of Single Parents and Home-Bound People 4.

Improvements in Health Care Delivery 5.

Aids for the Consumer 6.

Qualify of Life at Home and in the Workplace 7.

Law Enforcement and Homeland Security Improvement B. POTENTIAL NEGATIVE EFFECTS 1.

Computer Crime and Internet Fraud 2.

Too Much Power 3.

The Dangers of the Web 4.

Blaming the Computer Phenomenon 5.

Social Responsibility C. THE DIGITAL DIVIDE

320

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual



Section 16.11 Review Questions

16.12 THE FUTURE OF MANAGEMENT SUPPORT SYSTEMS  Section 16.12 Review Questions Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises ⧫ End of Chapter Application Case: An Intelligent Logistics Support System  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

How to wrap up your course is a personal decision. A book can support your approach, but specific content is less important at this point in the course than it was earlier.

This chapter provides two types of content you can use either to wrap up the course or on their own prior to your wrap-up.

One part of the chapter, Sections 16.2 through 16.4, is about integration. This may not be the first time your students have seen this concept. Aside from their likely exposure to it in earlier courses, with at least a brief encounter in their introductory MIS course, it was mentioned several times earlier in this course—most recently in later parts of Chapter 15. However, this chapter covers it in more depth and from a more general perspective. An approach of “now you know about the pieces, here’s how they go together” works well for this content (which includes, as noted earlier, the end of chapter case as well).

The other part of this chapter is about the MSS impact on people, individually and in groups. You may have pointed this impact out as opportunities arose throughout the course. If you did, you may not feel it needs much more now. If you didn’t, though, better late than never.

The different types of impact that are covered in Sections 16.5–11 may open students’ eyes to the many ways that MSS—or any information system, for that matter—affect peoples’ lives. It will be up to them during their careers to minimize their negative impacts. Students may have gravitated to MIS because of an interest in technology, but focusing solely on the technical side at work means shirking their professional responsibility.

If you plan to downplay the material on MSS impacts because you feel you covered these issues enough throughout the course, skip the opening vignette too. It’s related to this part of the chapter. If you do cover this vignette, though, its Review Questions provide opportunities for discussions that go into the issues they raise more deeply. For example, take the first: on identifying the devices that support decision making. It’s a natural extension to ask what decisions each one supports, who makes that decision, how MSS can help make that decision better: in short, how many of the course topics fit into this situation. By the time you reach this chapter, students should have a good perspective on these issues.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

321

Sections 16.2 through 16.4 form a coherent unit on systems integration. How much attention this topic requires depends in part on how much it gets in your students’ other courses.

You’re the best judge of that. If you go through these sections quickly, pick out areas that are specific to MSS/DSS/BI and focus on them. You can tell your students what you’re doing and why; they’ll appreciate both your respect for what they already know and how this shows the unity of the MIS field.

(If your students need additional background in enterprise systems to fully appreciate the issues in Section 16.4, online Chapter 17 is an excellent resource.)

The second part of the chapter, on the impacts of MSS, opens with an outline section (16.5) that previews the rest of the part. The major content is in the remaining sections (through 16.11).

These sections can be covered in almost any order. I personally like to start with impacts on individuals (7–9), move up to organizations (6) and finally society (11), putting legal/privacy/ethical issues (10) with either individuals or society as I feel at that moment.

However, there is no “right” order here.

From a teaching point of view, I find it helps to introduce real-life examples in this area.

The book presents good frameworks, but in this area something has to make them come alive.

Scanning the headlines, recent issues of Computerworld or InformationWeek, the Technology section of cnn.com or any other sources you prefer will turn up plenty for this purpose.

Also, many of the students in the typical undergraduate DSS course will start their professional careers a few weeks after you cover this chapter. They care deeply about how MSS will affect them and will enjoy an active discussion of this topic. (Once the graduating seniors break the ice, students who don’t fit that category will chime in too.)

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section 16.1 Review Questions 1.

Identify the devices that support decision making at Elite Care.

Devices that support decision-making include: 1.

Biosensors attached to each resident’s bed.

2.

Movement sensors (wearable computers).

3.

Panic buttons to call for help.

4.

Touch-screen Internet access in every room.

5.

Video conferencing using Webcams.

6.

Regulated appliances such as climate control and lights. (A case can be made that these don’t support decision making, at least not significantly. Maybe there’s failure notification.)

2.

What are the benefits of automatic monitoring over manual monitoring? Are there any limitations?

Automatic monitoring can detect problems that might not be detected or reported with manual methods. For example, patients may be reluctant to indicate to a person that they have an infection and require additional medication. It can determine when medication is

322

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

actually needed rather than administering it on a rigid schedule. In addition, while computer monitoring intrudes somewhat on patient privacy, it may intrude less than monitoring by another person would.

One limitation of automatic monitoring is that it is limited to monitoring the specific items detected by sensors. A caregiver who has experience with patients, insight, common sense, and the ability to communicate directly might pick up problems that are not specifically monitored for.

3.

What were the privacy concerns at Elite Care?

People are sensitive about information on their physical and mental health. It must be safeguarded by health care providers. Aside from ethical aspects of privacy in this context, there are also (in the United States, where the facility in the case is located) legal requirements that providers must meet.

4.

The Elite Care system uses both wireless and wireline networks. Can decision support be improved this way? How? Decision support was improved by the combination of network methods. Wireless networking is essential for personal monitoring; people would not consent to it if they had to be tethered to a wire. Wired networks are more cost-effective than wireless, yielding improved response time for a given cost for fixed locations with no drawbacks when mobility is not needed.

Section 16.2 Review Questions 1.

List several types of integration.

Functional integration, which can be subdivided into (a) creating a unified DSS from multiple components and (b) integrating DSS with other IS, and physical integration.

2.

Describe the need for MSS integration.

3.

Integration is needed in order to achieve any of several needs: • Implementing MSS • Increasing the capabilities of MSS applications • Enhancing the capabilities of non-MSS applications (including enhancing them with intelligent tools) • Enabling real-time decision support • Facilitating system development • List the levels of integration.

Across MSS and within MSS (to take advantage of multiple MSS technologies).

4.

Describe integration of MSS with non-MSS systems.

MSS can be used to enhance the capabilities of non-MSS systems. This recognizes the fact that systems whose primary purpose is not decision making often have secondary,

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

323

yet important, uses in that area. Examples include adding business analytics to ERP systems, to help understand the data which the ERP system already provides, or adding intelligent agents to an electronic auction system to manage the bidding process on a user’s behalf.

5.

Define embedded system.

An embedded system, in the context of this book, is an intelligent component that is part of a larger system and is invisible to its users.

Two notes on this question: • The word “nontransparent” eight lines up from the bottom of p. 713 is a misprint. It should read “transparent.” This will be corrected in the second printing.

• Students with CS/EE backgrounds may have seen this term used for a microprocessor embedded in a larger system such as a microwave oven or automobile for control purposes.

Section 16.3 Review Questions 1.

Describe an IDSS and name the types of DSS. This question in the book contains a misprint. The second printing will replace it with this:

1.

Describe intelligent DSS and what types of intelligent systems can be attached to them to help decision makers.

An Intelligent DSS, or IDSS, is a DSS that integrates expert system technology.

Intelligent systems, most likely expert systems, can be attached to a DSS for several purposes. These include adding intelligence to its database, using an intelligent system to manage its model base, improving the user interface, serving as a consultant to the builders of the DSS, and serving as a consultant to its users on various aspects of the decision or on using the DSS itself.

2.

How can different MSS tools share in the decision-making process?

The decision process consists of several stages. MSS can support all of them, as this book has stressed from the beginning. However, the information and analysis needs of each stage are different. So, therefore, are the MSS best used at each stage.

The book gives (p. 718) one example: using business analytics to analyze data up to a point where expertise is needed. At that point an expert system can be used. Students should be able to come up with other examples as well.

3.

Relate feedback loops to integration.

Feedback occurs when the output from a system becomes input to the system. For feedback to be meaningful, the output must come from one subsystem with the input going to another—or there would be no reason to go outside the subsystem in question.

Therefore, for a feedback loop to exist, the overall information system must consist of at

324

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

least two non-trivial subsystems. Integration is how these are combined into the overall system. Therefore, some form of integration is a requirement for feedback to be meaningful.

4.

List and describe some integration issues and factors.

Issues listed in the text (table, p. 717) include justification, feasibility, architecture, infrastructure, outsourcing, supporting the project, support of business partners, development processes and tools, data issues, legal and privacy, connectivity, Web-based integration, and introducing new technologies. The right-hand column of the table describes each one briefly.

Section 16.4 Review Questions 1.

Describe MSS integration with SCM and ERP software.

SCM and ERP software create and store the data which many types of MSS use.

Combining the two of them is therefore a natural concept. It was first approached by third-party vendors, as they modified their products to understand the data structures of major ERP/SCM packages. More recently, ERP and SCM software vendors have begun integrating analytical capabilities into their products. In SCM, the combination supports optimization of, and solving problems in, the supply chain.

(Marketing-oriented students may find vendor strategies in this area, with traditional suppliers of MSS tools selling better functionality and suppliers of SCM/ERP packages selling a single source of software and from-the-bottom-up integration, to be a potential project topic.)

2.

Describe the various modes of MSS integration with knowledge management.

KMS can be integrated with MSS in several ways: • Integrating the KMS with other information systems, such as Lotus Notes • Integrating the KMS with DSS, often with historical knowledge being used as a basis for future decisions • Integrating the KMS with intelligent systems, a natural move since human and artificial intelligence use much of the same basic information

3.

How do vendors support MSS integration?

There are four common approaches in use: • Second-generation ERP, SCM, etc., systems incorporate limited BI capabilities.

• More advanced ERP systems incorporate a full set of MSS capabilities, making “one-stop shopping” practical with fewer compromises in functionality. (See note to question 1 of this section. Suppliers of these systems argue that the compromises are minimal today and will disappear with their next release.

Suppliers of stand-alone MSS argue that even a small compromise can have a large effect on the business because management decisions are leveraged, and that when integrated packages catch up to where they are today, they will be still further advanced and will thus maintain a lead over time.)

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• •

4.

325

Middleware tools allow users (or their outsourcers) to assemble complete systems from “best-of-breed” components that come from several vendors.

Web services accomplish much the same purpose as middleware, but are a different approach to solving the same problem.

Describe the Oracle BI architecture in general terms.

The Oracle BI architecture is centered around an Oracle relational database.

Input to the database comes from Oracle applications, at the bottom of the figure, and from other data sources shown at the left.

The architecture supports report generation, queries and easy construction of other OLAP applications via the Oracle developer suite.

The database can be accessed directly for data mining. It can also be accessed by a variety of BI applications via application servers and/or portals.

A general description of an architecture should not reflect specific products used as examples in the figure (e.g., the Oracle 10g database release, specific BI applications in the top layer). It should be phrased in terms of more general architectural concepts.

Section 16.5 Review Questions 1.

How can MSS affect particular individuals and jobs, or the work structure of departments and units within an organization?

The effects of MSS on individuals and their jobs can be positive or negative. Positive effects include job enrichment via MSS in four of the five core job dimensions: identity, significance, autonomy and feedback. Use of MSS can also create satisfying jobs in the IS field. Other jobs, however, can become more routine, dehumanized and stressful.

The effect of MSS on the work structure within organizational units can result from the use of group support tools, as discussed in Chapter 10. These can assign work within the group, manage the flow of work among its members, allow parts of a task to proceed in parallel rather than serially if physical paper must be moved along, help group members coordinate their activities, and keep track of task status as well as helping in other ways. However, some people might prefer the flexibility of less programmed control methods.

The broader effects of MSS on organizations are the topic of the next question.

(While this question is listed as a review question, it cannot be answered from the content of this section. It simply states that MSS can have these impacts and moves on. It is more of a discussion question. Some information in the above answer is from Section 16.7.)

2.

How can MSS affect organizational structures, entire industries, communities, and society as a whole?

MSS can affect organizational structures in several ways. For one, its use in an organization can lead to the creation of new organizational units such as a management support or BI department. Its use can lead to business process restructuring; this can have far-reaching organizational effects, such as empowering employees at relatively low levels to deal with problems directly and reducing both the number of managers and the

326

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

number of layers of management. It can improve productivity within a firm and across multiple firms in a supply chain.

The effect of supply chain restructuring and similar changes can extend to entire industries. For example, the airline industry has changed dramatically as a result of MSSbased optimization tools and improved understanding of travelers and their travel patterns.

MSS can affect communities and societies by leading to a more informed electorate, increased participation in civic life, better decision making on the part of public officials and others, cost savings in governmental activities and more.

(Like the previous question, this is more of a discussion question than a review question since it is not answered in the section. Some of the above answer is from Section 16.6.)

3.

What are the major stimuli for change in the MSS environment?

Strategy (what people see as possible and want to do, “pull”) and technology (what becomes possible, “push”).

4.

List some of the major social impacts of MSS. Areas in which MSS will have a social impact include (from p. 725): • Changing role of home-bound people • Computer crime and fraud • Consumers • The digital divide • Employment levels • Opportunities for the disabled • Quality of life • Work in hazardous environments Others are discussed in the last paragraph of the answer to Question 2 above.

There are arguments that the effects of MSS on employment levels will be positive (by creating MSS-related jobs and by stimulating the economy through increased productivity) and that it will be negative (by eliminating the need for some jobs.) At this time the eventual outcome is still uncertain, as is the eventual employment impact of automation overall.

The one thing we know for a certainty, at least for the foreseeable future, is that technology with all its implications is here to stay.

Section 16.6 Review Questions 1.

Describe new organizational units that are created because of MSS. New organizational units created because of MSS are those that support the use of MSS directly. For example, there could be units created to support knowledge management (see, for example, the opening vignette of Chapter 11), BI or other aspects of MSS. These are typically at the corporate level, though in a very large organization they could be at the divisional level as well. They usually involve creating or adopting MSS technology, training people in it, and encouraging its use.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

2.

327

Relate MSS to cultural organizational changes.

The relationship between MSS and changes in organizational culture can go in either or both of two directions: influence of MSS on culture and influence of culture on MSS. Culture influences MSS by affecting its diffusion and use. Since managers and knowledge workers who use MSS typically have some freedom of choice in how they do their jobs, cultural attitudes toward MSS affects how, and how much, they will use it.

MSS influences culture by changing work and communication patterns. In one example cited in the text (p. 727), the use of Lotus Notes made employees more cooperative and willing to share.

When MSS empower lower-level employees, their new sense of authority can also affect the organizational culture as they come to expect similar authority in other parts of their work.

3.

How can MSS affect restructuring of business processes?

A business process is, in plain words, how an organization gets something done. MSS, by changing how people work, affect how things are done and thus affect business processes. Some of these changes are small: the structure of the process does not change, but some of its steps are done better than they were pre-MSS. In other cases the changes are larger, with steps being redefined, combined, and/or rearranged for greater effectiveness. That is what business process restructuring is about.

4.

Describe the impacts of ADS systems.

ADS (Automated Decision Support) systems are expected to have the following impacts: • Reduction of middle management • Empowerment of customers and business partners • Improved customer service (e.g., faster reply to requests) • Increased productivity of help desks and call centers

Section 16.7 Review Questions 1.

How can MSS affect job satisfaction?

Both positively and negatively. Some jobs may be enriched by MSS, but others may become more routine and less satisfying. Computer-based information systems may reduce managerial discretion in decision making and lead to managers being dissatisfied.

Conversely, one study showed that IT has had a positive effect on four of the five core job dimensions: identity, significance, autonomy, and feedback. (No significant effect was found on the fifth, skill variety.) Another found that employees who are empowered

by ADS systems are more satisfied with their jobs.

2.

In what ways may MSS affect inflexibility, dehumanization, stress, and anxiety?

•

Inflexibility: MSS may, but do not necessarily, reduce managers’ and knowledge workers’ control over how they carry out their work. They may enforce procedures and work flow that is not what the employees would choose and that,

328

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

•

•

• • • • •

3.

while equally good, is also not necessarily better. However, a good MSS design does not have these drawbacks.

Dehumanization results from removing the human element that was present in the system before an MSS was introduced. Removing human discretion is often deliberate, as when an organization wants to standardize its responses in similar situations so they do not depend as much on the person who handles the situation, his/her mood at that moment, etc. How this is perceived by the affected employees depends in large part on how it is presented to them by their management. If it is presented as “you can’t change what the computer says,” there may be resentment. If it is presented as “we have this problem, it’s not good for any of us, here’s what we’re doing to deal with it” it will probably be accepted more readily.

Stress: An increase in workload and/or responsibilities, even a change without a net increase, can trigger job stress. Computerization has led to changed and increased workloads on some employees, perhaps brought on by downsizing and redistributing work. Those workers may feel overwhelmed and stressed.

Anxiety: This usually manifests itself as information anxiety. Its manifestations include: Frustration with not being able to master computers as well as others.

Frustration with the quality of information available on the Web. It may be out of date or incomplete.

Frustration due to having too many online sources of information.

Frustration due to guilt associated with not being better informed or being informed too late (e.g., “How come everyone else knew before we did?”).

Describe the issue of experts’ cooperation in MSS. Systems such as knowledge management systems and expert systems cannot exist unless experts share their knowledge. Experts who are unwilling to do so may choose not to share what they know or, if they are placed in a situation where they must provide knowledge, may provide incomplete or incorrect knowledge to the system or knowledge engineer. Possible thoughts leading to this attitude include: • The computer may take my knowledge and replace me • The computer may make me less important • Why should I tell the computer my secrets? What will I gain?

• The computer may reveal that I am not as great an expert as people think To deal with such situations, management must motivate (and should perhaps compensate) the experts. An example of compensating experts for sharing their knowledge in a KMS is found in the opening vignette of Chapter 11.

Section 16.8 Review Questions 1.

List the impacts of MSS on decision making.

MSS can change the manner in which many decisions are made and thus change managers’ jobs. MSS has been found to improve the quality of decision making in

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

329

organizations. It does this by helping them gain more knowledge, experience, and expertise. It can give them time: to get out of the office and into the field or to spend more time planning activities instead of putting out fires. Specific potential impacts include: • Less expertise (experience) required for making many decisions.

• Faster decision making because of information availability and automation of some decision-making phases.

• Less reliance on experts and analysts to provide support to top executives; managers can do it by themselves with the help of intelligent systems.

• Power can be redistributed among managers.

• Support for complex decisions saves time and improves quality.

• Information needed for high-level decision making is expedited or even selfgenerated.

• Automation of routine decisions or decision-making phases may eliminate some managers.

2.

List the impacts of MSS on other managerial tasks.

Recognizing that decision making is arguably the most important part of a manager’s job, it is still not the only thing managers do. Managers communicate; MSS can help them do this. Managers motivate; MSS can help them do this (especially, but not only, via communication.) Managers plan; MSS can help with this as well.

Many middle managers spend a great deal of time approving subordinates’ decisions. MSS can reduce this aspect of their jobs, either by automating some of these decisions (ADS) or by providing the subordinates with the information they need to make the decision correctly, empowering them to do so, and thus eliminating the need for management approval before the fact. (Empowerment is not the same as anarchy, since the overall quality of an employee’s decisions can be a legitimate factor in his or her performance evaluation.)

3.

Explain the issues related to completely automating managers’ jobs.

Some aspects of managers’ jobs, such as making or approving routine decisions, can be automated. Whether or not this amounts to “automating managers’ jobs” depends on how their overall jobs are structured. If this is the entire job, it can indeed in principle be eliminated. If there is more to the job than this, it probably can’t.

In most practical situations this is part of the job but not the whole job. By eliminating this part of the job, managers’ (especially middle managers’) workload is reduced, thus reducing the number of middle managers an organization needs. A reduction in the number of middle managers, possibly leading to fewer management levels through broader spans of control, is a common result of MMS.

Section 16.9 Review Questions 1.

List some legal issues of MSS. Legal issues of MSS include these:

330

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• • • • • • 2.

What is the value of an expert opinion in court when the expertise is encoded in a computer?

Who is liable for wrong advice (or information) provided by an ES? For example, what happens if a physician accepts an incorrect diagnosis made by a computer and performs an act that results in the death of a patient1?

What happens if a manager enters an incorrect judgment value into an MSS and the result is damage or a disaster?

Who owns the knowledge in a knowledge base?

Should royalties be paid to experts who provide knowledge to an ES or a knowledge base? If so, how much should they receive?

Can management force experts to contribute their expertise?

Describe privacy concerns in MSS. Privacy is “the right to be free from unreasonable personal intrusions” (p. 734). This right is not absolute, and the definitions of “unreasonable” and “personal” are neither objective nor fixed. Modern information technology, especially MSS whose purpose is to collect, consolidate and analyze information, provide many opportunities to invade personal privacy by collecting information on individuals. Companies who see a business advantage in collecting such information must, ethically, balance this against the privacy interests of the individuals whom the information is about. In some cases their freedom to collect personal information may be constrained by law. In others, their sense of ethics may guide their actions, as may the fear of public backlash if knowledge of their activities leaks out.

3.

Explain privacy concerns on the Web. The Internet and the Web offer many opportunities to collect information about individuals. In most cases each particular item is public knowledge, but combining them can create an overall picture of the person that goes beyond the purpose of any single data element. This consolidation is a source of concern to privacy experts and to many individuals. It is sometimes justified in the name of security, though others question the degree to which this justification is correct. Ways in which information can be found on the Web, some of them legitimate and others illegal or at best borderline, include: • Reading an individual’s newsgroup postings • Looking up an individual’s name and identity in an Internet directory • Reading an individual’s e-mail • Wiretapping communication lines and listening to employees • Conducting surveillance on employees • Asking an individual to complete Web site registration • Recording actions as an individual navigates the Web, using cookies or spyware

1

This scenario can be subject of further discussion. For example, what if it can be demonstrated that following the recommendations of this system would save the lives of 99 patients who would otherwise die, while resulting in the death of one who would otherwise live? Is a judge or a jury, deciding a case involving that one patient out of 100 who died unnecessarily, likely to be swayed by this fact (or even to believe that it is a fact)?

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

4.

331

List ethical issues in MSS. Ethical issues that could arise in the context of an MSS implementation include: • Electronic surveillance • Ethics in DSS design • Software piracy • Invasion of individuals’ privacy (see previous answer) • Use of proprietary databases • Use of intellectual property such as knowledge and expertise • Exposure of employees to unsafe environments related to computers • Computer accessibility for workers with disabilities • Accuracy of data, information, and knowledge • Protection of the rights of users • Accessibility to information • Use of corporate computers for non-work-related purposes (see pp. 736–737) • How much decision making to delegate to computers

5.

Describe an ethical framework for MSS. One possible framework, discussed in Online File W16.4, involves four dimensions: privacy, accuracy, property and accessibility. (One could reasonably add transparency: the concept that whatever is done should be done openly.) These can be broken down further as follows: Privacy • What information about oneself should a person be required to reveal to others?

• What kind of surveillance can an employer use on its employees?

• What things can people keep to themselves and not be forced to reveal to others?

• What information about individuals should be kept in databases, and how secure is the information there?

Accuracy • Who is responsible for the authenticity, fidelity, and accuracy of information collected?

• How can we ensure that information will be processed properly and presented to users accurately?

• How can we ensure that errors in databases, data transmissions, and data processing are accidental and not intentional?

• Who is to be held accountable for errors in information, and how is an injured party compensated?

Property • Who owns the information?

• What are the just and fair prices for its exchange?

• Who owns the channels of information?

• How should one handle software piracy (i.e., copying copyrighted software)?

• Under what circumstances can one use proprietary databases?

332

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

• • •

Can corporate computers be used for private purposes?

How should experts who contribute their knowledge to create expert systems be compensated?

How should access to information channels be allocated?

Accessibility • Who is allowed to access information?

• How much should be charged for permitting access to information?

• How can accessibility to computers be provided for employees with disabilities?

• Who will be provided with equipment needed for accessing information?

• What information does a person or an organization have a right or a privilege to obtain, under what conditions, and with what safeguards?

Section 16.10 Review Questions 1.

List arguments as to why MSS may lead to massive unemployment.

From Table 16.4, p. 738: • Benefit–cost advantage of computers increases with time.

• Less skillful employees are needed.

• Shifting displaced employees to services is getting difficult.

• Many employees lost their jobs in the 1990s.

• Hidden unemployment exists in many organizations.

• Millions of help-desk employees will be replaced by intelligent agents.

• Electronic commerce will cause millions of intermediaries and agents to lose their jobs.

• Unemployment levels in certain countries are high and are increasing.

• There is an upper limit to customer consumption.

2.

List arguments as to why MSS will probably not lead to massive unemployment.

From Table 16.4, p. 738: • New occupations and jobs have always been created by automation.

• There is much less unemployment in countries that use more automation2.

• Work can be expanded to accommodate everyone.

• Conversion to automation is slow, so the economy can adjust.

• Many tasks cannot be fully automated.

• There will always be some areas where people are better than machines.

• People will work less but will have more money.

• E-commerce reduces the cost of many goods and services; thus their consumption will increase, resulting in more buying and more jobs.

You can use this as an opportunity to explain the “correlation implies causation” fallacy in reasoning. While use of MSS may be correlated with lower unemployment, it does not follow that it causes lower unemployment. The causality may be in the other direction (low unemployment forces increased use of MSS), or both may be due to a third factor (economic prosperity, caused by unrelated factors, leads to lower unemployment and provides more resources for MSS development).

2

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

3.

333

Describe how robots may influence employment levels.

Robots may replace people in jobs that are physically hazardous, boring, involve primarily observation, and (for now) do not require creativity or “common sense.”

Section 16.11 Review Questions 1.

List benefits of MSS to individual people.

Potential benefits include: • Eliminating need to work in hazardous environments • Providing employment opportunities for people with physical limitations, including the elderly3 • Ditto for the able but home-bound, including single parents with child care responsibilities • Improved health care delivery • Helping people perform undesirable tasks, or those requiring skills not everyone has • Improved quality of life due to increased leisure time • Improved physical security

2.

List some negative effects of MSS.

3.

(“Affects” in the printed question is a misprint, which will be corrected in the second printing. The corresponding section heading on p. 741 is correct.)

Potential negative effects include: • Aspects of computer crime and Internet fraud • Concentration of power in a few individuals, who may not always use it appropriately • “Web addiction” and other dangers of the Web, including predatory activity • Blaming the computer to cover up human misdeeds • Socially irresponsible organizational computer use • Define digital divide and relate it to MSS. The term digital divide refers to the gap between the “haves” and the “have-nots” of the digital world: those who have access to, and know how to use, computer and communications technology and those who don’t. Not having such access limits one’s access to information, to shopping opportunities that can potentially save money, and to job opportunities; his or her ability to communicate with friends and associates; and more. It can be a barrier to full participation in 21st-century society.

The digital divide is related to MSS because MSS expands the use of information technology into the activities of ordinary managers and even people throughout society.

By making use of technology more an inherent part of 21st-century life, MSS have the potential of increasing the impact of the digital divide on the disadvantaged side.

3

Students should realize that those a 20-year-old thinks of as “elderly” may not see themselves the same way.

334

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

However, some technologies used in MSS offer promise of closing the divide. For example: voice technologies, an aspect of intelligent systems, can facilitate access by the visually impaired.

Section 16.12 Review Questions 1.

List five technological directions of MSS. Of the MSS developments listed in the bullets on pp. 742–744, the following can be considered to be technological directions. Many are related to the Web: • MSS are becoming Web-based • BI is being combined with other Web-based enterprise systems • Web-based advisory services that provide self-diagnosis will mushroom • More complex MSS applications will arrive, so more complex problems will be solved • The trend toward making MSS more intelligent will continue, using especially intelligent agents, ES, case-based reasoning, neural networks and fuzzy logic • Advancements in mobile, wireless, and pervasive computing will require use of more, and more diversified, intelligent devices • MSS will be more available for dissemination via application service providers; the trend toward utility computing will accelerate their use • The semantic Web will increase the use of MSS • The use of voice technologies and natural language processing will increase the use of MSS • Groupware technologies for remote collaboration and communication will become easier to use, more powerful, and less expensive, making electronic group support a viable initiative even in small organizations • Decision support tools for e-commerce, currently limited to recommending products and brands and providing comparisons, will expand to include more and better applications.

• ADS systems such as those for pricing optimization will explode as they tend to serve specific industries (e.g., retailing, banking).

• Grid computing will be used for analysis of very large amounts of data.

A correct answer can list any five of the above items, or more if you tolerate a less-thanliteral interpretation of the question’s requirements.

(Some items overlap the categories of the three Review Questions for this section and were included in more than one answer. Some items can be placed in categories differently, depending on how you or the respondent interprets them.)

2.

List some organizational developments in the future of MSS. Of the MSS developments listed in the bullets on pp. 742–744, the following can be considered to be organizational developments: • MSS will play a larger role in strategic management • More complex MSS applications will arrive, so more complex problems will be solved

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

335

•

Frontline decision support technologies that support CRM will become an integral part of IT in most medium-sized and large organizations.

• MSS will continue to be integrated with ERP to provide better SCM, including manufacturing planning and control.

• More and more companies will initiate formal knowledge management programs.

Some will sell the knowledge accumulated in their knowledge bases to others.

• With the greater usage of wireless technologies and the ability to access the Web with wireless devices, employees will be able to access MSS and knowledge bases anytime and from anywhere. Therefore, MSS usage will increase considerably.

• As groupware technologies for remote collaboration and communication become easier to use, more powerful, and less expensive, electronic group support will become a viable initiative even in small organizations • ADS systems will spreading across and within industries, empowering and sometimes replacing employees.

A correct answer can include as many of the above items as you feel “some” calls for.

(Some items overlap the categories of the three Review Questions for this section and were included in more than one answer. Some items can be placed in categories differently, depending on how you or the respondent interprets them.)

3.

List developments related to intelligent systems.

Of the MSS developments listed in the bullets on pp. 742–744, the following can be considered to be related to intelligent systems: • Intelligent systems are contributing to the fights against terrorism and fraud • The trend toward making MSS more intelligent will continue, using especially intelligent agents, ES, case-based reasoning, neural networks and fuzzy logic • Advancements in mobile, wireless, and pervasive computing will require use of more, and more diversified, intelligent devices.

• Natural language-based Internet search engines will proliferate. This will facilitate MSS construction by reducing costs.

• The use of voice technologies and natural language processing will increase the use of MSS • Large numbers of experts will offer expertise on the Internet • More and more companies will initiate formal knowledge management programs.

Some will sell the knowledge accumulated in their knowledge bases to others.

• Intelligent agents will roam the Internet and intranets, assisting decision makers in monitoring and interpreting information. These agents will alert decision makers and suggest alternative courses of action.

(Some items overlap the categories of the three Review Questions for this section and were included in more than one answer. Some items can be placed in categories differently, depending on how you or the respondent interprets them.)

336

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Explain the following statement: “Integration of a DSS and an ES (or other intelligent system) can result in benefits during the construction (development) of the systems and during their operation.” Give an example of benefits in development and benefits in operation of these systems.

There are several types of DSS-ES synergy that can be obtained during the construction and operation of a system.

During construction, the requirement to look at the decision situation from both the DSS and the ES viewpoints can lead to increased understanding and a better overall solution.

ES tools can also provide expertise that will improve the development process, resulting for example in shorter development time, a better database design, etc.

During the operation of the system, DSS and ES can provide complementary capabilities. For example, the DSS can provide results which the ES can analyze or base a recommendation on.

2.

Discuss the potential benefit of Web services to MSS integration.

Potential benefits of Web services include: increased functionality by adding intelligent software agents and/or a computational engine, a more flexible and user-friendly systems environment, easier integration of data, and easier integration of electronic markets.

3.

One ES can be used to consult several DSS. What is the logic of such an arrangement?

What problems may result when two or more DSS share one ES? The logic of such an arrangement is based on economics. If the problems of all the DSS are in the same domain, then one ES can incorporate expertise that applies to all the specific problems, thus reducing development and maintenance costs. (Operational cost would not be affected, as there is no reason to believe it would be more expensive to run Expert System A once and Expert System B once than to run the combined Expert System C twice.)

4.

Explain how the addition of an ES capability can improve the chances of successfully implementing a DSS. An ES’s explanation facility increases user confidence in the total DSS. Also, an ES can deal with incomplete information, i.e., it provides lets the system be used in situations where the DSS would be inoperable on its own. Both these features will tend to increase user satisfaction and thus smooth the implementation phase of the DSS.

5.

Review current journals and surf the Web to identify a system that you believe is an MSS integration. Analyze the system according to the integration frameworks suggested in this chapter.

Open-ended answer, which depends on what each student finds.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

6.

337

Discuss why the Web facilitates the integration of BI and ERP. Recent developments in Web services have improved their capabilities to provide seamless integration. This provides a user-friendly mechanism to integrate ERP and BI.

7.

Some say that MSS in general, and ES in particular, dehumanize managerial activities, and others say they do not. Discuss arguments for both points of view.

Regarding MSS in general, see the second bullet point in the answer to Review Question 2 of Section 16.7.

ES imitate the reasoning of a human expert. They thus have, on the surface, greater potential to replace human activity than do other types of MSS. Managers and knowledge workers who must do what an ES tells them to do might find their jobs dehumanized.

However, experience has not shown that they do this. There will always be situations beyond the capabilities of any expert system. Their use has typically freed managers and knowledge workers from dealing with repetitive, run-of-the-mill situations so they could concentrate on these “more interesting” cases. While this has reduced the number of knowledge workers required, this has usually not been a problem either, since organizations seldom have enough competent people for these jobs; ES have simply made it possible for them to handle their workload with the number of people they can find.

8.

Discuss how enterprise systems relate to the integration issue.

See answers to Review Question 1, Section 16.2, and Review Question 2, Section 16.4.

9.

Should managers who use ES instead of a human assistant be paid more or less for their jobs? Why? (This is a matter of opinion. Students may take any of three possible positions. Here’s mine:) A manager’s job is to make the best possible decisions. Managers should want to use the best available tools in doing so. Any reasonable employer will support this.

Managers should be evaluated and compensated on the basis of the results they achieve, not the technology by which they achieve them. Paying managers more or less on this basis makes as much sense as paying managers more or less for their work a century ago based on whether they did nor didn’t use a telephone.

Paying them more sends the wrong message, since it would imply that there is something special about ES that deserves higher pay.

Paying them less sends the wrong message, since it will encourage managers not to use them.

Thus, paying them on the same scale regardless of their ES use is the only rational policy.

338

10.

11.

4

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

The U.S. Department of Transportation in a large metropolitan area has an ES that advises an investigator about whether to open an investigation of a reported car accident.

(This system, which includes 300 rules, was developed by T.J. Nagy at George Washington University.) Discuss the following questions: a.

Should the people involved in an accident be informed that a machine is deciding the future of an investigation?

b.

What are some of the potential legal implications of this?

c.

In general, what do you think of such a system?

a.

The phrasing “a machine is deciding” is unfairly inflammatory. The ES recommends. It does not decide. The decision is made by a person. This person should be expected to use all available information and advice, human and other.

Therefore, there is no need to make a big deal of it or to inform people proactively that an ES is used. However, the department should explain this if asked.

b.

Although some people may decide to sue the Department of Transportation, they will have a weak case unless they can show that the decision itself was flawed.

The issue will be whether or not an investigation should have been opened, not how investigating authorities reached their decision. In fact, it can be argued that such a system reduces the chances of using an improper decision method, since (to the degree that its recommendation becomes the final decision) it removes improper influence factors, such as the standing or connections of a driver, from consideration.

c.

It should be used. It will add uniformity to the decision-making process by removing the emotional factors that often attend car accidents and help optimize the use of scarce investigative resources.

Diagnosing infections and prescribing pharmaceuticals are the weak points of many practicing physicians (according to E.H. Shortliffe, one of the developers of MYCIN). It seems, therefore, that society would be better served if MYCIN (and other ES) were used extensively, but few physicians use ES. Answer the following questions: a.

Why do you think ES are little used by physicians?

b.

Assume that you are a hospital administrator whose physicians are salaried and report to you4. What would you do to persuade them to use ES?

c.

If the potential benefits to society are so great, can society do something that will increase doctors’ use of ES?

a.

Several reasons. Fear that the computer may take over is one of them. Lack of trust in the system is another. As time passes, more and more physicians will probably use ES for second opinions. Could an ES replace a physician? For

I am always surprised at how many university seniors, even in management programs, are not familiar with the use of “report to” as it is used here. If you assign this question, explain that it means a subordinate relationship in an organization, not writing reports for the other party to read.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

339

example, could a nurse who is familiar with medical terminology use MYCIN to perform a diagnosis? For an interesting view, refer to a challenging book by Maxman, J., The Post-Physician Era, Wiley, New York, 19765.

12.

b.

The administration could require physicians to use ES. In such a case, conflicts would arise. However, if the physicians in question have much job mobility, the results may not be what the administrator wanted.

It would be better to provide motivation. System training, and perhaps some fraction of system usage, could meet part of the physicians’ continuing education requirement for licensing if the relevant laws permit this. If not, other forms of professional recognition, professional development funds, etc., could be effective.

Alternatively, one could start with a small, self-selected pilot group. If they get good results, word will get around and the others will in time move over.

The final few holdouts can be dealt with more directly.

c.

While physicians have tremendous power, there is already a surplus of physicians in many areas, and they are sure to fight attempts to automate their jobs, they can still be motivated—as witness their adoption of other technologically based tools over the past several decades. If an ES improves the accuracy of diagnosis, physicians can be liable for damages if they do not use it. Its use can also be promoted by professional associations. If it is seen as an adjunct to the physician’s diagnostic arsenal, not as a replacement, it will be more readily accepted. As physicians learn to trust it more they will leave more and more simple cases to it.

In a developing country or in a rural area where there is a shortage of medical doctors, medical ES may be more readily acceptable. (Otherwise, why would telemedicine be such a hot topic?)

Discuss the potential impacts of ADS systems on various types of employees and managers.

See answer to Review Question 4 of Section 16.6.

13.

Discuss the potential benefits of integrating KMS into a DSS. The knowledge base can provide information about the problem which the decision maker would otherwise have to provide—and might be unable to, or unable to without considerable effort. It can also provide information about potential solutions that have or haven’t worked in the past, and about people who might be able to be of assistance. All these can enhance the DSS and the decision.

14.

How can intelligent systems help in closing the digital divide?

Some technologies used in MSS offer promise. For example: voice technologies, an aspect of intelligent systems, can facilitate access by the visually impaired. Robots can

5

The author of this IM has not read this book. The recommendation was in the IM of the 7th edition. It is retained here with the thought that the author of that IM knew what he or she was doing.

340

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

improve accessibility by people with mobility limitations. Intelligent agents can reduce the level of computer literacy required in order to achieve certain desirable results, such as lists of job openings, on the Web.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

What kind of integration is provided by the ILSS in this case?

The ILSS integrates an expert system and a neural network. The expert system in this case provides input to the neural network. The two together provide single-system user visibility.

2.

What are the benefits of this system?

It was found to make decisions that are 30 percent better than those made by humans.

(The measure reflected in the figure of 30 percent and the basis for the finding that it was cost-effective are not specified in the text.)

Given that ATM Transportation Ltd. is said to employ 90 planners making this sort of decision, and the reasonable supposition that competent planners are rather thin on the ground, this system could enable ATM’s planners to focus on the more difficult parts of their job while letting the system handle standard cases.

3.

How is decision support provided in this system?

The system is used to make three decisions: which carrier and flight to select, which of its available container types to use, and what to load into each container. These decisions are interrelated, as a decision to put item A into container type B on flight C of carrier D means that item A need no longer be considered for other shipments, but that other items that fit the parameters of remaining space in the container, the carrier and the flight schedule should be considered for being added to that container along with item A.

Enterprise Systems

CHAPTER ONLINE

17

Learning Objectives for Chapter 17 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Describe the concepts, definitions, and issues in enterprise information systems (EIS) Discuss the capabilities and characteristics of (Web-based) enterprise information portals Describe supply-chain management (SCM) issues and how EIS handle them Describe material requirements planning (MRP), enterprise resource planning (ERP), and SCM solutions Discuss customer relationship management (CRM) concepts and issues Describe the emerging EIS of product lifecycle management (PLM) and business process management (BPM) Describe how EIS have improved frontline decision making by providing access to realtime data

CHAPTER OVERVIEW This chapter covers enterprise systems: SCM, ERP, CRM and so on. These are the systems that make today’s large organizations tick; these are the systems that justify million-dollar expenditures on mainframes and storage-area networks, yet these are also systems that university students typically have little feel for because they are not exposed to them directly. They are not necessarily DSS/MSS/BI systems themselves (though this chapter discusses the connections frequently) but understanding them is important for understanding how DSS/MSS/BI fit into the overall corporate IS picture.

CHAPTER OUTLINE W17.1 OPENING VIGNETTE: ERP INCREASES PRODUCTIVITY AT NORTHERN DIGITAL, INC. A. PROBLEM B. SOLUTION

C.

RESULTS 

Questions for the Opening Vignette

W17.2 ENTERPRISE INFORMATION SYSTEMS (EIS): CONCEPTS AND DEFINITIONS ⧫ Technology Insights W17.1: CIO Spending Plans: Now and Later  Section W17.2 Review Questions W17.3 ENTERPRISE INFORMATION PORTALS A. BENEFITS, CAPABILITIES, GOALS AND COSTS OF ENTERPRISE INFORMATION PORTALS 1.

Benefits and Goals of Enterprise Information Portals 2.

Types of Corporate Portals 3.

Corporate Portal Applications 4.

Integration of Portals with Enterprise Systems 5.

Costs of Portals B. USAGE AND APPLICATIONS OF PORTALS C. ENTERPRISE INFORMATION PORTAL EXAMPLES ⧫ Technology Insights W17.2: The Enterprise Information Portal Market ⧫ Application Case W17.3: New Jersey Gets Aggressive About Its E-Government Portal  Section W17.3 Review Questions W17.4 ORGANIZATIONAL DECISION SUPPORT SYSTEMS (ODSS) A. ODSS DEFINITIONS AND CONCEPTS B. THE RELATIONSHIP OF ODSS TO GSS AND EIS ⧫ Application Case W17.4: ODSS in the Egyptian Cabinet  Section W17.4 Review Questions W17.5 SUPPLY AND VALUE CHAINS AND DECISION SUPPORT A. DEFINITIONS AND BENEFITS 1.

Supply-Chain Management (SCM) Definitions 2.

Benefits of SCM B. THE COMPONENTS OF THE SUPPLY CHAIN C. THE VALUE CHAIN AND VALUE SYSTEM 1.

The Value Chain ⧫ Application Case W17.5: John Deere Truly Optimizes Inventory Through SCM 2.

The Value System D. DECISION MAKING AND THE SUPPLY CHAIN ⧫ Application Case W17.6: PBS Television’s Brave New SCM  Section W17.5 Review Questions W17.6 SUPPLY-CHAIN PROBLEMS AND SOLUTIONS A. ORDER FULFILLMENT AND LOGISTICS

⧫

B.

C.

Application Case W17.7: Aviall’s Web-Based Logistics SCM Hits the Spot TYPICAL PROBLEMS ALONG THE SUPPLY CHAIN ⧫ Application Case W17.8: Nike’s Forecasting System Runs into Oblivion SOLUTIONS TO SUPPLY-CHAIN PROBLEMS ⧫ Application Case W17.9: How Littlewoods Stores Improved Its SCM  Section W17.6 Review Questions

W17.7 MATERIAL REQUIREMENTS PLANNING (MRP), ENTERPRISE RESOURCE PLANNING (ERP), ENTERPRISE RESOURCE MANAGEMENT (ERM), AND SUPPLY CHAIN MANAGEMENT (SCM) SYSTEMS A. THE EVOLUTION OF COMPUTERIZED SUPPLY-CHAIN AIDS: FROM MRP TO ERP B. WHY INTEGRATION? 1.

Integrating the Supply Chain C. ENTERPRISE RESOURCE PLANNING (ERP) D. SECOND-GENERATION ERP 1.

How Is Integration Done in Second-Generation ERP? ⧫ Application Case W17.10: How U.S. Companies Are Succeeding with ERP E. ERP PROBLEMS AND FAILURES ⧫ Application Case W17.11: Hershey’s Eventual Sweet Success in ERP ⧫ Technology Insights W17.12: Notable ERP Implementation Failures F. A GUIDE TO SUCCESSFUL ERP IMPLEMENTATION ⧫ Technology Insights W17.13: Avoiding ERP Implementation Failure  Section W17.7 Review Questions W17.8 CUSTOMER RELATIONSHIP MANAGEMENT (CRM) SYSTEMS A. DEFINITIONS, CONCEPTS, AND BENEFITS 1.

Benefits of CRM ⧫ Application Case W17.14: The Chicago White Sox Bat 1000 with CRM 2.

The Major Shortcoming of CRM B. MARKETING, CUSTOMERS, AND CRM 1.

Communication with the Customer 2.

The Value of a Customer and How to Serve the Customer C. CRM TOOLS AND TECHNOLOGIES 1.

BI Tools 2.

CRM and Data Mining 3.

CRM Software D. CRM IMPLEMENTATION PROBLEMS AND ISSUES

E.

⧫ Application Case W17.15: Cigna HealthCare Insures Failure MEASURING CRM SUCCESS  Section W17.8 Review Questions

W17.9 EMERGING EIS: PRODUCT LIFECYCLE MANAGEMENT (PLM) AND BUSINESS PROCESS MANAGEMENT (BPM) A. PLM SYSTEMS 1.

Characteristics and Concepts of PLM 2.

PLM Software and Its Capabilities 3.

PLM Benefits 4.

PLM Implementation Issues 5.

PLM Vendors B. BUSINESS PROCESS MANAGEMENT (BPM) SYSTEMS 1.

BPM Software Capabilities 2.

BPM Benefits 3.

BPM Implementation Issues 4.

BPM Vendors  Section W17.9 Review Questions W17.10 FRONTLINE DSS A. FRONTLINE DECISION MAKING B. FRONTLINE SYSTEMS  Section W17.10 Review Questions Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises ⧫ End of Chapter Application Case: How Levi’s Got Its Jeans into Wal-Mart  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

         

Ideally, students should learn about enterprise information systems in their introductory MIS course. If yours did, you may be able to skip this chapter. That’s why it’s online instead of being bound with printed copies of the book. More likely, though, they looked at EIS briefly but don’t remember much; they should probably read this chapter on their own as a refresher. The Chapter Highlights on pages W-117, 118 are an excellent guide to the content they should have a feel for, whether you’re reading them to decide about covering this chapter or they are to decide if they understand its concepts sufficiently.

If you cover this chapter, you should teach it in the same way as any other. It is organized in the same way as the other chapters and is not intended for self-study any more than they are.

Being online does not change this!

The sequence number of this chapter, 17, is irrelevant. This material is needed for discussions of integration with EIS in places such as Section 16.4. It is also useful in understanding data warehouses in Chapter 5 (where does the information in them come from?) and many other parts of the book. If you have to cover it at all, I’d cover it early. There’s a reason EIS are usually in the intro course, after all. The connections to decision support here are an extra bonus for this course. (It might seem that Section W17.10, about the connection between business analytics and frontline systems, has to be covered after BA in Chapter 6, but the BA content of this section is minimal enough not to require that.) If you’re converting from the 7th edition, where it was Chapter 8, you might as well leave it where you’re used to it. The only place you’ll have to be careful if you cover it early in the course is in your choice of questions to ask. Some assume more of the rest of the book than others.

The ERP system in the introductory vignette of this chapter is unusual because it involves a very small (90 employees; that’s “very small” for EIS) company. That helps make the point that EIS are not just for General Motors and Boeing, but students should be aware that NDI is not typical and that the other examples throughout the chapter will be in larger organizations.

(Application Case W17.4 on p. W-128 is from the other end of the size spectrum: a national government.)

(By the way, one must register to access the case study cited at the end of the vignette.

It’s free.)

Section W17.2 is just two paragraphs long. Its most important content is the Technology Insights box on p. W-125. Clearly, much high-priority spending involves EIS such as CRM, SCM, and so on. Students might not notice, however, that many other items (such as storage management and storage) are there only or largely because of EIS. Were it not for EIS requirements, these wouldn’t matter either. In other words, the impact of EIS on corporate IT spending is even larger than it seems at first.

Next come two sections on topics that your students are less likely to have covered in their intro MIS course than ERP and so on. Fortunately, neither corporate portals nor ODSS are difficult concepts.

The chapter now moves to topics that your students will have ideally seen before.

Sections W17.5 and W17.6 are on supply chains and supply chain management. The concepts are taught in many general business courses and most intro MIS courses. If your students have seen them before, you might want to see how they were taught and relate these sections to what they (should) already know. If you can show the value chain figure, for example, from their intro MIS book as part of your discussion of this chapter, it will help them see how this all fits together.

Sections W17.7 on ERP and its predecessors, W17.8 on CRM, and W17.9 on PLM and BPM are likewise topics that were almost certainly covered in your students’ intro MIS course.

As long as your students understand what these are about, it doesn’t much matter where they learned it.

Section W17.10 relates frontline systems to DSS. If you cover this chapter before you get into the heart of the DSS content of the book, your students may not fully appreciate the DSS issues here. No matter. The point of this chapter, as regards DSS, is that EIS help people make decisions. If students get that much out of this section it will have served its purpose. Indeed, you may want to come back to this point later in the course, when you encounter places that discuss DSS as mostly for managers and other knowledge workers. You’ll be able to point to this section as the exception that proves the rule.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section W17.1 Review Questions 1.

For a small company like NDI, why is an ERP system useful?

Small manufacturing companies face the same problems as large ones—inventory management, order cycle time, production planning, etc. At 90 people and $20+ million/year revenue, NDI is past the point where manual methods can do the job. The planning capabilities of their ERP system, which are the focus of this case, allowed NDI to use its inventory twice as effectively as before.

Until relatively recently small companies did not use ERP because it was expensive and difficult to implement. These reasons do not reflect lower potential usefulness; they were simply barriers to obtaining that usefulness. As the barriers have dropped in the past several years, small companies have begun to use it. The conventional wisdom, “ERP is only for big companies,” no longer applies.

2.

Identify the supply-chain segments that NDI’s ERP supports; be specific.

(Students may want to study Sections W17.5 and W17.6 before answering this question.)

The system supports the middle and upstream parts of the supply chain, focusing on inventory management plus production planning. There is no indication in the case that it goes further upstream to include supplier interaction, or that it has any downstream (customer-focused) aspects.

3.

Identify the improvements (i.e., value added) to various parts of the NDI supply chain.

(Students may want to study Sections W17.5 and W17.6 before answering this question.)

The major improvement was a doubling of inventory turns, allowing NDI to support twice the revenue as before with no increase in inventory. Absent the system, the required inventory would presumably have doubled. We are not told what the inventory value is, but for each $1 million NDI did not have to tie up in inventory, if their cost of capital is 15 percent, they saved $150,000/year.

Improved production planning reduced order cycle time for its flagship product (not further defined in the case) from 4 months to four weeks. The value of that is increased customer satisfaction, dealing with the “customer satisfaction was at risk” situation as the case opens.

4.

Go to intuitivemfg.com and report on the capabilities of the company’s ERP product.

Open-ended answer. It should start with the concept that their offering provides “Complete integration of planning, materials management and procurement, manufacturing and financial business processes [which] reduces costs and improves productivity, quality and control within your operation.” It can go considerably deeper than that. Business-savvy students will go beyond pure features and also note that the package is tailored to medium-sized (“mid-market”) firms like NDI.

Section W17.2 Review Questions 1.

Define EIS. An Enterprise Information System (EIS) is an information system that serves at least two functional areas of an organization. (The term stood for Executive Information System during the 1980s and most of the 1990s. As those systems became used by more and more people the meaning of the initials changed, with attempts such as “Everyone’s Information Systems” before stabilizing here.)

2.

List typical EIS. A veritable “alphabet soup,” the most common manifestations of which are ERP (Enterprise Resource Planning), SCM (Supply Chain Management), CRM (Customer Relationship Management), and PLM (Product, or Process, Lifecycle Management).

Other correct answers are possible.

3.

Describe the importance of EIS. Obtaining maximum value from information is important to any organization. That means sharing it among all those who can use it. The people who need information, especially but not only managers and knowledge workers who use it for decision making, are often not in the same functional part of the organization as those who create it. That means information must be shared across functional areas. Using an information systems which by its nature crosses department lines is a conceptually simple way to do this, though it is not the only possible way.

Section W17.3 Review Questions 1.

Define corporate portal and list the purposes of corporate portals.

A corporate (or enterprise) portal is a Web-style interface to information provided by enterprise information systems.

The main purpose of an enterprise portal is “to give each user a personalized and integrated view of business information, applications, and services” (p. W-121).

(Students may correctly find and list other purposes as well.)

2.

List the types of corporate portals and describe them briefly.

Types of corporate portals include: • Suppliers’ and other partners’ portals: for suppliers to manage their own inventories online, view what they have sold to the portal owner and for how much, see the portal owner’s inventory levels to ship when a reorder level is reached, and collaborate with its staff.

• Customer portals (for both businesses and individual customers): to view products and services, place orders which they can later track, view their accounts, pay for products and services, and arrange for warranty and delivery.

• Employee portals: for training, dissemination of company information, discussion groups, and self-service activities in the personnel area (e.g., change of address forms, tax withholding forms, expense reports, class registration, tuition reimbursement forms). (Employee and supervisor portals can be combined as workforce portals.)

• Executives’ and supervisors’ portals: to enable managers and supervisors to control the entire workforce management process from budgeting to workforce scheduling.

• Functional portals: Portal functionality can vary from simple information portals that store data and enable users to navigate and query those data to sophisticated collaborative portals. Categories of functional portals include BI portals, intranet portals and knowledge portals.

3.

Describe the major benefits of corporate portals.

“Distributing information more effectively, encouraging collaborative work, managing content and information, integrating with enterprise applications, supporting customers, supporting suppliers and partners, improving Internet administration, and reducing training costs” (p. W-121).

4.

Discuss integration and implementation issues.

Open-ended answer. It should mention Web services as an integration approach and the fact that many EIS products have portal modules.

Section W17.4 Review Questions 1.

Define ODSS. Several definitions are possible. One is “a combination of computer and communication technology designed to coordinate and disseminate decision making across functional areas and hierarchical layers in order that decisions are congruent with organizational goals and management’s shared interpretation of the competitive environment” (p. W127, source cited in text).

Students may find or create other correct definitions. These should reflect: • Focus on an organizational task, activity, or decision that affects several organizational units or corporate problems.

• Cutting across organizational functions or hierarchical layers.

• Involving computer-based technologies, perhaps also communication technologies.

2.

List the major characteristics of ODSS. See last part of previous answer (the three-item bullet list).

3.

Relate ODSS to GSS. Since an ODSS focuses on a task that affects multiple organizational units, it is inherently designed to support a group activity. That places it within the general area of group support systems. The distinguishing feature of an ODSS within GSS is that an ODSS is designed to provide decision support directly, whereas other GSS can focus on collaboration and other group activities.

4.

Relate ODSS to enterprise systems.

An ODSS focuses on a task that affects multiple organizational units. EIS connect multiple organizational units. That creates a natural alignment between the two. The ODSS is a type of EIS that provides decision support. Other aspects of EIS support transaction processing and more general information needs.

Section W17.5 Review Questions 1.

Define supply chain and list the components of a supply chain.

Supply chain refers to the flow of materials involved in providing a product to its consumer.

An earlier view of the term looked at things from the viewpoint of a company in the middle of the chain, typically a manufacturer. The supply chain was seen as including only flows leading to that company—i.e., its suppliers. Downstream activities were called the demand chain. Since every company is a supplier to others downstream from it, however, the term “supply chain” is now used for the entire chain.

Components of a supply chain begin with the extraction, collection or growing of raw materials, continue through their processing into finished materials, components and subassemblies; their assembly into the finished product, and the distribution of that

product (perhaps through intermediaries) to the eventual customer, including storage and transportation as needed throughout. (Services and digitized products may not have physical supply chains, but they have conceptual ones.)

2.

Define value chain and value system.

The value chain is the series of activities performed by a firm to transform its inputs into an output having a higher value. These tend to follow the flow of the supply chain, but the two are not identical. (See Discussion Question 2 at the end of the chapter on this point.)

The value system is the combination of a firm’s value chain with those of its suppliers, partners and customers.

3.

Define SCM and list its benefits.

SCM, or supply chain management, refers to the activities involved in planning, organizing and coordinating the activities of the supply chain to deliver materials effectively and cost-effectively at all its stages. The term is also used to refer to software packages that support these activities.

4.

Define extended supply chain.

A firms’ extended supply chain includes its entire supply chain from raw materials on, even if those go through several other firms before reaching the firm in question, and similarly in the downstream direction. The term is used in contrast to a firm’s immediate supply chain, which includes only those with which the firm interacts directly—i.e., its immediate suppliers but not their suppliers.

5.

How can decision support software improve supply-chain operations?

Creating an effective and cost-effective supply chain requires many decisions: what to buy, from whom, when, under what terms and conditions. Decision support software can help with all of these. It can also connect firms so they can develop joint approaches that reduce overall costs, sharing the savings, in situations where neither firm by itself would have adopted that approach since it would not benefit by doing so unilaterally.

Section W17.6 Review Questions 1.

List and describe inventory problems along the supply chain.

Inventory problems fall into a few main categories: • Not enough. This risks customer dissatisfaction as goods cannot be delivered (insufficient finished goods or retail inventory) or manufactured (insufficient materials).

• Too much. This ties up capital, which has a cost, and runs the risk of inventory losing value in areas with fast-moving technologies or fashion dependencies, or because it is used in a product which is no longer in demand.

• •

2.

Wrong place. Snowblower retailers could have been out of stock the during record December 2006 snows in Denver, while all their inventory was in Massachusetts—which enjoyed its warmest early winter in decades.

Wrong stage. Inventory can usually be held at more than one point in the supply chain: at a tire supplier or at a car factory, at a regional warehouse or at a store.

There is usually an optimum allocation to each stage. Finding it can be difficult when the two are not under one ownership so their economic interests do not coincide. The firm with the greatest economic power may maximize its own benefit but decrease overall value.

Describe other supply-chain problems.

Other supply-chain problems, which can impact inventory but are not inventory problems as such, include: • Uncertainty in demand forecast; • Uncertainty in cost forecasts; • Uncertainty in technology forecasts. These three factors force firms to balance the cost of carrying more inventory than they think they should (in case demand is higher than expected, costs become higher than expected, or technology advances more slowly than expected) against the risk of having too much (in case the opposite situations occur).

• Unforeseen quality problems; • Unforeseen delivery delays. These two factors argue for carrying additional inventory. The cost of this “insurance” must be balanced against the cost and likelihood of such problems.

• Obtaining accurate data on supply chain status; • Integrating supply chain information into other information systems. These two are problems with information about the supply chain, not with the supply chain itself, but lack of proper information makes it impossible to deal effectively with the others.

3.

Describe some solutions to inventory problems.

Non-IT solutions include vertical integration, the just-in-time approach, outsourcing during demand peaks, finding multiple sources of supply or conversely using fewer suppliers (ordering more from each to improve bargaining power), improving the supplier relationship (including creating partnerships), reducing intermediaries, and optimizing any and all aspects of the supply chain.

Technology-based solutions include the use of extranets to link customers with suppliers, using e-commerce to improve ordering.

All of these, including the solutions which are not inherently technology-based, can be enhanced by appropriate use of technology. For example, the just-in-time approach, originally developed with manual methods, now usually uses IT to send information about what is needed. Any solution that involves optimization can benefit from information technology, as can those that involve decisions of nearly any type.

Section W17.7 Review Questions 1.

Define MRP and MRP II. MRP stands for Material Requirements Planning. It uses the production schedule and each product’s bill of materials to develop production and/or ordering plans for materials and subassemblies.

MRP II stands for Manufacturing Resource Planning. The “II” (Roman numeral 2) serves to differentiate this concept from Material Requirements Planning, which has the same initials. MRP II recognizes that manufacturing a product takes more than materials. It also takes labor, equipment and time. This approach uses all of them for planning purposes.

2.

Define ERP and describe its benefits.

ERP extends MRP II to integrate all parts of an enterprise, including (for example) accounting so that the financial implications of completing a product (changes in workin-process inventory and finished goods inventory, for example) can be recognized automatically.

Benefits of ERP can include shortened production cycles, more accurate demand forecasts, improved customer service, lower operating expenses, reduced IT costs, and better inventory control and use.

3.

What systems are integrated and why?

Many systems can be integrated in ERP. (“In ERP” is implied in the question, which is asked in an ERP context.) These include financial and accounting, human resources, operations (specialized to a wide range of vertical industries), supply chain and customer information. The integration is usually accomplished through a single enterprise-wide database.

4.

What were the capabilities of first-generation ERP? First-generation ERP focused on automating business processes, supporting OLTP (OnLine Transaction Processing) and other routine transactional activities. They provided reports that could be used to support planning, but did not truly incorporate planning capabilities.

5.

What are the capabilities of second-generation ERP? Second-generation ERP leverages these systems to handle transactions better (not just electronically), to provide information for improved decision making, and to transform how business is done. They add decision support and BI capabilities that use the existing enterprise-wide database. However, these capabilities may be weaker than those available through third-party packages, which do not have the ERP vendor connection to help their sales and must offer more to compete at all.

6.

Why do ERP systems fail?

The major reason is a mismatch between a company’s business processes and those supported by the ERP package. Such a mismatch can be dealt with by changing processes and/or software, but all approaches are difficult and have risks.

A list of more specific reasons for ERP failure would include: poor top management leadership, trying to automate existing redundant or non-value-added processes, unrealistic expectations, poor project management, inadequate user education and training, attempts to maintain the status quo, the aforementioned bad match between the ERP business model and actual business processes, inaccurate data, viewing the ERP implementation solely as an IT project, and technical difficulties.

7.

What are the CSF for ERP systems (i.e., how can failures be minimized)?

Through the following measures: • Strong leadership from an executive management planning committee • Viewing the implementation as an ongoing process • Staffing implementation teams with the company’s best workers, representing all functions • Involving midlevel management totally in the implementation • Using excellent project management techniques • Eliminating old systems, including all informal systems • Implementing and closely monitoring proper measurements • Establishing an aggressive but achievable implementation schedule • Applying successful change management techniques • Providing extensive education and training

Section W17.8 Review Questions 1.

Define CRM. CRM stands for Customer Relationship Management. It is “an enterprise approach to understanding and influencing customer behavior through meaningful communications in order to improve customer acquisition, customer retention, customer loyalty, and customer profitability. A CRM system provides the technology to do so.

(The “and e-CRM” at the end of the question in the January 2007 version of the chapter will be deleted as soon as possible, in all likelihood before you read this.)

2.

List the major benefits of CRM. From the first paragraph of Section 17.8: improving customer acquisition, retention, loyalty and profitability.

From the second paragraph of that section: CRM turns customer information into positive customer relationships, provides important marketing information, and empowers several categories of personnel [listed there] with better and more informative BI about their customers.

From the subsection “Benefits of CRM:” CRM improves customer loyalty/retention, using data better to manage relationships with customers. Specific benefits include:

• • • • • • •

Lower cost of recruiting customers.

No need to recruit so many customers to maintain a steady business volume.

Reduced sales costs. Existing customers are generally more responsive.

Higher customer profitability through segmentation and targeting of products and services.

Increased customer retention and loyalty. Customers stay longer, buy more, and contact you more.

Improved customer service.

Evaluation of customer profitability, which leads to identifying the most profitable classes of customers and how to create new profitable classes.

Migration from a product focus to a customer focus.

A correct answer can combine all of these and/or list other benefits found in other sources.

(The “e-CRM” for CRM at the end of the question in the January 2007 version of the chapter will be corrected as soon as possible, in all likelihood before you read this.)

3.

Relate CRM to data mining.

CRM is, by definition, about customers. The data warehouses used for data mining have a wealth of customer information. CRM can be more effective when augmented with information derived from data mining, such as being “able to predict which customers are likely to leave, which ones will probably respond to the next promotion, which ones are ripe for cross-selling, and what will happen if prices change” (p. W-151).

4.

What are CRM’s major CSF? Guidelines for CRM implementation success include doing these: • Look for ways to measure customer behavior changes after CRM systems are deployed.

• Take the long view. It may take up to a year before a firm starts accruing benefits.

• Focus on which of the problems CRM is supposed to fix.

And these don’ts: • Rely on cost savings to deliver value. As with revenue management, the focus is on revenue generation, not cost containment.

• Equate past customer satisfaction with future customer value.

5.

Why does CRM fail?

Several studies have addressed this question. Their answers, while not inconsistent with each other, are not identical either. Reasons that have been found include (with some overlap): • Failure to rethink processes • Imposing technology on salespeople • Not involving users in system selection and development • Difficult integration, high cost/low ROI, staff/customer resistance, solutions not meeting needs

• • • • 6.

List some shortcomings of CRM. • • • • • • •

7.

Not making necessary preparations Not adapting business processes as necessary Not retraining employees Not finding the right system integrators

CRM requires rethinking business processes as well as introducing new technology.

CRM may require a culture change in the organization, especially in its sales force.

Existing systems and databases may not integrate easily with a new CRM system.

CRM projects are (or at least can be) big and costly.

CRM projects often do not meet their objectives.

CRM is hard work.

CRM projects suffer from all the problems that affect other large-scale IT projects.

How can the benefits of CRM be measured?

Several measurements are possible. They fall into three categories: Cost savings: • Internal process improvements • Service center efficiencies • Marketing campaign-expense Revenue increases: • Salesperson • Increased order size • Customer profitability • Campaign response improvement Other benefits: • Lifetime customer loyalty • Protecting existing investments • Customer satisfaction • Employee turnover

Section W17.9 Review Questions 1.

Define BPM. Business Process Management (not to be confused with Business Performance Management, which shares its acronym) refers to a system which aims to streamline and automate business processes, reducing the administrative workload and creating an

environment in which processes can be leveraged for strategic value (pp. W-161–162, slightly rephrased).

2.

List the major activities of BPM. A comprehensive BPM solution should include the definition and/or modeling of business processes, the execution of those processes, and the monitoring of that execution. (The software also needs an integration layer and a user interface, but those are not “activities.”)

3.

List the benefits of BPM. Different people have identified different sets of benefits. Representative ones include: • Streamlined workflow linked to existing applications • Reduced paperwork, giving people more time for their “real jobs” • Increased business from customers who find new processes easier to deal with • Reduced process design time (50 percent reduction cited in one study), leading to faster time to market, hence more competitive products, and ultimately increased revenue • Reduced order time (80 percent cited in one study), leading to cost savings, improved customer satisfaction and again ultimately increased revenue • Efficiency gains in call centers (60 percent cited in one study) • Reduced inquiry response time (70–90 percent cited in one study) • Reduced order lead time (50 percent cited in one study) • Increased inventory turns (35 percent cited in one study) • Reduced manufacturing cycle time (40 percent cited in one study) • Improved compliance with legislation such as Sarbanes-Oxley

Section W17.10 Review Questions 1.

Define frontline.

The frontline (also seen as two words, front line) consists of those employees who are closest to situations that arise in the course of business. The expression comes from military usage where the front lines are where the most dangerous fighting takes place, but also the best place to get a true picture of the battle. It is the approximate English equivalent of the Japanese gemba, literally “the place where truth can be found,” a common term in quality assurance work.

2.

Relate frontline systems to CRM. CRM systems are designed to support customer-related activities. Many of these activities are performed by, and many of the rest involve, salespeople. Salespeople are frontline workers. Therefore, some aspects of CRM systems are properly considered frontline systems.

3.

Describe the benefits of frontline systems.

Frontline systems help workers make good operational decisions (by contrast with most other MSS/DSS/BI systems, which support tactical and strategic decisions). This empowers employees by letting them take action on their own without management approval.

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Describe the support that corporate portals can provide to decision makers.

Corporate portals “add context to business data, and executives like having a single, personalized window into the health and well-being of their organizations. Portals leave data in their original format and provide users with a window into them” (p. W-126).

2.

Compare and contrast a value chain and a supply chain.

The supply chain describes a flow of materials and/or information.

The value chain describes a sequence of activities.

The materials flow through the activities. In that sense the two overlap. However, the value chain concept considers them from the standpoint of contribution to the purpose of the organization. The supply chain concept simply says “we need to have X at point Y at time Z.” Thus, the value chain concept deals with more fundamental business issues and may provide a better way to think about the issues, but from a practical point of view it is the supply chain that must be managed.

3.

How can cooperation between a company and its suppliers reduce inventory costs?

A company keeps inventory to provide flexibility in manufacturing and/or sales. Its suppliers keep it for the same reason. Each tries to keep the right amount for its own needs, but together, if they act separately, they have more than required. If they plan combined inventory needs jointly they can provide the necessary flexibility with less total inventory and share in the savings.

4.

Discuss the reasons for ERP’s inability to directly support decision making.

ERP generally only captures transaction data. These data must be aggregated and cleansed (much like a data warehouse would do) in order for it to be useful for decisionmaking.

5.

Discuss the major problems that could develop along the supply chain.

Difficulty in filling orders; uncertainties in the demand forecast, which may be influenced by several factors such as competition, prices, weather conditions, and technological developments; uncertainties in delivery times; quality problems with materials and parts; traffic jams interfering with shipments; poor customer service; high inventory costs; loss of revenue; extra cost of special handling and expediting shipments; and more.

Also see Review Questions 1 and 2 to Section W17-6.

6.

ERP software vendors have Web-based products. Discuss the benefits of Web-based ERP. Web-based ERP allows companies to integrate ERP functions into their company portal, to make information available to employees easily through a convenient interface.

7.

BI and ERP software as we know them today cannot support frontline decisions. Why not?

Business intelligence systems and ERP software do not implement simple decision processes or present data in a way that can be analyzed for complex situations.

Executives may get context from reports and the systems which create them, but these don’t provide frontline workers with guidance on daily problems. At the same time, traditional decision support from vendors is intended for experts–those who can access data, slice and dice it, and give it business meaning—who are unlikely to be at the front lines. Organizations need a new generation of enterprise analytic applications to implement frontline decision-making.

8.

Describe in detail how a CRM system is an EIS. Consider the definition of EIS: “an information system that serves at least two functional areas of an organization” (Review Question 1 of Section W17-2). A customer relationship management system is designed to utilize customer data at an enterprise level to make decisions on such issues as the retention of customers, attracting new customers, and segmenting customer groups to increase profitability. In order to do this it must connect multiple functional areas. It therefore meets this definition.

9.

Discuss the customer valuation in CRM. CRM is designed to help organizations meet customer needs. It is, however, impossible for any organization to meet all possible needs of all possible customers. It must decide which should be met and which should not be. Being able to do this rationally, rather than let chance take its course, is a major potential benefit of CRM systems.

One method used to value customers is known as RFM analysis: Recency, Frequency, Money. It divides customers into five groups along three dimensions: how recently they purchased, how frequently they have purchased, and how much money they have spent. The quintiles are given numbers from 1 (best) to 5 (worst). Thus, a customer with a recency score of 1 is among the 20 percent of all customers with the most recent transactions; a 5 hasn’t bought from the firm for some time. This enables companies to target strategies at each group. For example, 5-1-1 customers bought often, spent a lot of money, but haven’t been heard from in a while; it may be worth trying to find out why and/or providing incentives to return. A 1-1-5 customer buys often and has bought recently but doesn’t spend much. This one might be a candidate for upgrading to more expensive products.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

   

Why was it so important to overhaul the Levi Strauss supply-chain system?

Levi’s supply chain system wasn’t working and did not meet Wal-Mart’s standards.

Among other things, it did not meet the requirement of linking to Wal-Mart’s systems.

They needed to have a better handle on what needed to be produced and to be able to manage their own supply chain. If they could not improve their supply-chain management, they would not have been able to become a supplier to Wal-Mart. Their poor performance in things such as on-time product deliveries would have harmed their business with other customers as well.

2.

What SCM features do you think the system used?

Optimization of purchasing and manufacturing, reduction of lead times, improved demand forecasting, and creation of strategic partnership with customers (e.g., Wal-Mart).

3.

What benefits did the new system deliver?

It provided better decision-making information to managers, improved forecasting, enabled Levi’s to exchange demand information with customers, and enabled an improvement in on-time deliveries from 65 to 95 percent.

4.

How can analytics help Levi Strauss?

Business analytics is (see Chapter 6) “a broad category of applications and techniques for gathering, storing, analyzing, and providing access to data to help enterprise users make better business and strategic decisions.” Levi Strauss can use BA to track what is being manufactured and where is it being shipped, to analyze changes in demand so as to match production to what is needed, and thereby to improve their relationship with their customers.

5.

What advantages could Levi Strauss obtain by implementing an ERP system to integrate with this SCM system?

Implementing an ERP system to integrate with this SCM system would provide: • Extension of enterprise systems to include their production processes, thus automating the work flow further • Incorporating business functions that are not part of SCM (e.g., human resources, accounting) with the enterprise systems • Better cost control through real-time accounting information

CHAPTER ONLINE

18

Knowledge Acquisition, Representation, And Reasoning

Learning Objectives for Chapter 18 ⧫ Understand the nature of knowledge ⧫ Understand the knowledge-engineering process ⧫ Learn different approaches to knowledge acquisition ⧫ Explain the pros and cons of different knowledge acquisition approaches ⧫ Illustrate methods for knowledge verification and validation ⧫ Understand inference strategies in rule-based intelligent systems ⧫ Explain uncertainties and uncertainty processing in expert systems (ES)

CHAPTER OVERVIEW This chapter provides in-depth coverage of expert systems from the inside. It extends the coverage of expert systems in Chapter 12, specifically Sections 12.4–10. The content of production rules, knowledge representation, the knowledge engineer’s job, certainty factors, forward and backward chaining, and so on will be important to students who will apply expert system technology in decision support.

CHAPTER OUTLINE W18.1 OPENING VIGNETTE: DEVELOPMENT OF A REAL-TIME KNOWLEDGEBASED SYSTEM AT ELI LILLY A. PROBLEM B. SOLUTION C. DEVELOPMENT PROCESS D. RESULTS  Questions for the Opening Vignette E. WHAT WE CAN LEARN FROM THIS VIGNETTE

361

362

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

W18.2 CONCEPTS OF KNOWLEDGE ENGINEERING A. THE KNOWLEDGE-ENGINEERING PROCESS ⧫ Application Case W18.1: Development of Tire-Defect Diagnosis ES in Egypt B. COMMON KADS METHODOLOGY  Section W18.2 Review Questions W18.3 THE SCOPE AND TYPES OF KNOWLEDGE A. SOURCES OF KNOWLEDGE 1.

Knowledge Acquisition from Databases 2.

Knowledge Acquisition via the Internet B. LEVELS OF KNOWLEDGE 1.

Shallow Knowledge 2.

Deep Knowledge C. MAJOR CATEGORIES OF KNOWLEDGE 1.

Declarative Knowledge 2.

Procedural Knowledge 3.

Metaknowledge  Section W18.3 Review Questions W18.4 METHODS OF ACQUIRING KNOWLEDGE FROM EXPERTS A. ROLES OF KNOWLEDGE ENGINEERS B. KNOWLEDGE MODELING METHODS 1.

Manual Knowledge Modeling Methods 2.

Semiautomatic Knowledge Modeling Methods 3.

Automatic Knowledge Modeling Methods  Section W18.4 Review Questions W18.5 ACQUIRING KNOWLEDGE FROM MULTIPLE EXPERTS A. MULTIPLE-EXPERT SCENARIOS B. PRIMARY AND SECONDARY EXPERTS 1.

Small Groups 2.

Panels C. METHODS OF HANDLING MULTIPLE EXPERTS  Section W18.5 Review Questions W18.6 AUTOMATED KNOWLEDGE ACQUISITION FROM DATA AND DOCUMENTS A. AUTOMATED RULE INDUCTION ⧫ Application Case W18.6 Induction-Based System for Selection of Retaining Walls 1.

Advantages of Rule Induction 2.

Difficulties in Implementing Rule Induction 3.

Rule Induction Software Packages B. INTERACTIVE INDUCTION  Section W18.6 Review Questions

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

363

W18.7 KNOWLEDGE VERIFICATION AND VALIDATION  Section 18.7 Review Questions W18.8 REPRESENTATION OF KNOWLEDGE A. PRODUCTION RULES B. KNOWLEDGE AND INFERENCE RULES C. ADVANTAGES AND LIMITATIONS OF RULES ⧫ Application Case W18.7: Rule-Based Systems Tackle Employee Shrink D. SEMANTIC NETWORKS E. FRAMES 1.

Content of a Frame 2.

Hierarchy and Inheritance of Frames F. OBJECT-ORIENTED KNOWLEDGE REPRESENTATIONS G. DECISION TABLES H. DECISION TREES 1.

Predicate Calculus 2.

Other Knowledge Representation Methods I. SELECTION OF REPRESENTATION METHODS  Section W18.8 Review Questions W18.9 REASONING IN INTELLIGENT SYSTEMS A. COMMONSENSE REASONING B. REASONING IN RULE-BASED SYSTEMS 1.

Backward Chaining 2.

Forward Chaining 3.

Forward Chaining or Backward Chaining: Which to Use? C. INFERENCE TREES  Section W18.9 Review Questions W18.10 EXPLANATION AND METAKNOWLEDGE A. EXPLANATION 1.

Why Explanations 2.

How Explanations 3.

Other Explanations B. METAKNOWLEDGE  Section W18.10 Review Questions W18.11 INFERENCING WITH UNCERTAINTY A. THE IMPORTANCE OF UNCERTAINTY B. REPRESENTING UNCERTAINTY 1.

Numeric Representation of Uncertainty 2.

Graphical Representation of Uncertainty 3.

Symbolic Representation of Uncertainty C. PROBABILITIES AND RELATED APPROACHES 1.

The Probability Ratio 2.

The Bayesian Approach

364

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

D.

3.

The Dempster-Shafer Theory of Evidence THEORY OF CERTAINTY FACTORS 1.

Certainty Factors and Beliefs 2.

Combining Certainty Factors  Section W18.11 Review Questions

W18.12 EXPERT SYSTEMS DEVELOPMENT A. PHASE I: PROJECT INITIALIZATION B. PHASE II: SYSTEM ANALYSIS AND DESIGN 1.

Conceptual Design 2.

Development Strategy and Methodology 3.

Sources of Knowledge 4.

Selection of the Development Environment C. PHASE III: RAPID PROTOTYPING AND THE DEMONSTRATION PROTOTYPE D. PHASE IV: SYSTEM DEVELOPMENT E. PHASE V: IMPLEMENTATION F. PHASE VI: POSTIMPLEMENTATION 1.

System Operation 2.

System Maintenance 3.

System Expansion (Upgrading) 4.

System Evaluation  Section W18.12 Review Questions W18.13 KNOWLEDGE ACQUISITION AND THE INTERNET A. THE INTERNET AS A COMMUNICATION MEDIUM B. THE INTERNET AS AN OPEN KNOWLEDGE SOURCE  Section W18.13 Review Questions Chapter Highlights Key Terms QUESTIONS FOR DISCUSSION Exercises Teradata University and Other Hands-On Exercises Team Assignments and Role-Playing Projects Internet Exercises References

TEACHING TIPS/ADDITIONAL INFORMATION

         

As noted earlier, this chapter extends the coverage in Chapter 12, specifically Sections 12.4– 12.10. There is some overlap between the two, as this chapter was first written before that one so it goes over some of the same ground, but most students will find enough new material here to justify their time. There is also some overlap between this chapter and other parts of the book, such as in development methods, but the expert system focus in this one is enough of a reason to keep them separate.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

365

Whether or not you want to cover this chapter depends on the focus of your course. If you use it, the right place would probably be between printed Chapters 12 and 13. If you choose not to, you can recommend it as enrichment reading when your students study Chapter 12. Some concepts are easier to grasp here, especially for technically inclined students who learn better when they see how things work.

The opening vignette of this case is titled “… a real-time knowledge-based system …,” but the real-time aspect is secondary. It’s a perfectly good introduction to expert systems overall with a natural transition to the chapter body. Feel free to ignore the “real-time” part of the title if you want to.

The chapter then continues, logically enough, with what knowledge is about to an expert system and how to get it, including the job of the knowledge engineer. (Students encountered this creature briefly in Chapter 12, on pages 554 and 560.) One problem students may have here is that the definitions and discussions of knowledge here are different from, often somewhat in conflict with, those they studied in Chapter 11. They have to understand that the context here is expert systems, whereas there it was knowledge management systems. It’s not surprising that the two evolved along different paths. (This is hardly the only example of parallel developments in IT.) What would have been surprising would be if, having evolved separately as long as they did, if they had somehow ended up with consistent terminology!

When I cover this material I often assign students to find two definitions of “knowledge engineer” on the Web and then to discuss two things: whether they think they’d be good at this sort of work and whether they think they’d like it. They usually show a great deal of insight into their own strengths and weaknesses, more than one might expect. Beyond that, the assignment forces them to think seriously about what a knowledge engineer does, how, and why.

The three categories of knowledge—declarative, procedural, and meta—at the bottom of p. W-218 are important for understanding how expert systems work. Most students won’t have thought about these distinctions until now. The distinction between declarative and procedural knowledge is especially important, since the difference between making a decision based on how something works and making the same decision based on how an expert would approach it can be critical to planning a DSS project.

If your students have a sheet of paper that describes the typical MIS program of studies, you can use it to create an example of the difference between shallow and deep knowledge: do they take Organizational Behavior in the spring of their junior year because the schedule sheet says so, or do they take it then because it’s prerequisite to the senior-year spring-term Strategy course and there’s no room for it in the usual senior fall term? Both are declarative, not procedural; but the first is shallow, the second deep. Either could form the basis for chunk of procedural knowledge such as “If you haven’t taken OB by spring of your junior year, try to fit it in then.”

The knowledge elicitation methods in Section W18.4 begin with some your students have probably seen in their Systems Analysis course. They then move to methods that are specific to expert systems and that they almost certainly have not encountered before. If you don’t tell them what’s happening they may start this section thinking “I know this already” and not notice when they suddenly don’t.

If you have the time, role-play knowledge acquisition interviews can be both valuable and fun here. If you don’t, they can still make a good assignment for groups of two or three. If you give the groups a common task to interview about it, one which your students can be expected to know well, it can be useful (and fun here as well) to compare their results. Exercise 3

366

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

on pp. W-281–282 is a possibility, though my personal preference is to have students create production rules rather than an induction table.

Section W18.6 discusses automated knowledge acquisition. In practice, that boils down to induction. This has more than a surface similarity to case-based reasoning (CBR), which students studied in Chapter 13. The difference is more one of context than of what’s going on.

You may want to mention this.

Section W18.7 is short and easy to skip over, especially since it’s not directly about getting on with the job. Urge students to resist that temptation. Software quality control is important. Expert systems are no exception to that rule. On the job, they can ignore the subject of this section only at their peril!

Section W18.8 changes the subject from the business- and human-oriented side of expert systems to their technical internals. You may find that some students lose interest here while others wake up. If you expect that, it will be easier to deal with.

In terms of teaching this part of the chapter (from here through Section W18.11), your background and knowledge are important. If you come from a computer science background or have other experience with expert systems, you’ll probably take to it like a duck to water. If you’re more from the MIS side this may be a bit “geeky” for your taste. You might want to read up on chaining methods (Section 18.9) ahead of time, as students often have trouble with them.

Fortunately, there is no shortage of easily located Web resources for this purpose.

A few thoughts on these sections: • The term “semantic network” here is unrelated to the Semantic Web in Section 14.9.

• The material on decision trees and decision tables may seem to overlap the coverage in Section 4.6. However, the usage of the terms in this context is somewhat different. The decision tree of Figure W18.10, for example, describes a decision process. One moves through it in sequence, making one test after another until an end point is reached. (This is essentially forward chaining.) A decision tree in the context of Section 4.6 describes what is statistically likely to happen if certain actions are taken at the root, given the probable states of nature that will result. Its purpose is to help a decision maker make the best choice; it’s not as though externally verifiable facts force its reasoning process along one branch or another as they do here.

• Frames are discussed here before objects because they came first (they contributed to OO concepts) and are easier to grasp. However, today’s MIS students encounter objects in other courses, such as systems analysis and often programming, before this. You may therefore prefer to start with their familiar OO concepts and then discuss frames as an earlier embodiment of the idea.

• The fourth item in the list of human reasoning methods at the top of page W-217 is worth noting. Much of the reason for using expert systems is because each ES rule is an independent “chunk” of knowledge. The fact below that list (if it is a fact, no matter) that the price of gold will go up if an international conflict begins is true or false independently of other facts. It can be added to an existing expert system without interfering with its existing rules, or deleted from an existing expert system if it is determined not to be true without affecting the remaining ones. This concept, the separation between the reasoning in the rule base and the logic that operates on that reasoning, is a key difference between the expert

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

367

system approach and simply writing a series of “if … then … else” statements in one’s favorite programming language to accomplish the same thing—which, in principle, one could always do.

Section W18.12, on expert system development, overlaps Chapter 15 on MSS development overall and indeed with what your students have probably studied about IS development in general. As with some of the other topics in this chapter, the difference is its expert systems focus. Try to relate it to what your students know, stressing what is different about expert systems (such as deciding if the problem is a good fit to the expert system approach in the first place, specifics of selecting development tools) and what’s the same (most of this section; an expert system is, after all, still an IS).

The chapter closes with a discussion of expert systems and the Web. Many chapters of this book have sections relating their topics to the Web. Nobody studying this book needs to be told that the Web affects every aspect of technology, indeed of our lives. Expert systems are no exception.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

       

Section W18.1 Review Questions 1.

Why did Eli Lilly need to develop an intelligent system for providing advice to process operators?

Quality and consistency are critical to their manufacturing process, but are difficult to achieve. Lack of operator experience due to turnover and differences among experienced operators caused problems. This system would make consistent expertise, based on their best practices, available 24/7.

2.

Why were the coding knowledge engineers asked not to use their own knowledge framework but to record only knowledge from the experts?

Knowledge engineers are not subject matter experts! They should know enough about a problem to understand experts, but “a little knowledge is a dangerous thing.” If experts are selected properly, which as far as we can tell they were, they know more about fermentation than knowledge engineers. Inserting a knowledge engineer’s knowledge into the rules could therefore only reduce their quality.

3.

Why do we need knowledge engineers to develop ES? Can we ask experts to develop the system by themselves?

It is difficult to create a correct knowledge base. Experts who have no experience in system development often cannot express what they know in a form where it can be used by an expert system, unless they are guided by someone who understands what is needed and how to get there.

368

4.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

What do you think of Eli Lilly’s approach, which developed 10 separate knowledge bases and then put them together through knowledge fusion? What are the pros and cons of this approach?

The ten knowledge bases reflected the knowledge of ten experts. It would have been difficult and time-consuming, probably impractical, to get all this knowledge into one combined knowledge base from the start. Advantages were saving the experts’ time and avoiding confrontations when two experts used different approaches. The major disadvantage was that conflicting knowledge bases had to be resolved later, which took time and introduced opportunities for error. If one knowledge base had been developed from the beginning this problem would not have existed.

5.

For system testing and evaluation, Eli Lilly used simulated data rather than real data.

Why is this acceptable for real-time ES? Can you think of a better approach for system testing and evaluation?

The key reason is in the words “that simulated anomalies” in the case. Real data tend to reflect normal operation most of the time. The frequency with which anomalies arise is so low as to make testing those cases difficult or impractical. The only way to test the full range of system behavior is to create an artificially high frequency of anomalous situations.

Another advantage of simulated data is that cases that should yield known results can be used as a check on system behavior.

Personally, I can’t think of a better way to test this system. Perhaps one of your students can.

6.

What are the benefits of using knowledge acquisition tools?

Knowledge acquisition tools provide a consistent means of codifying knowledge rules.

Results will be in a consistent format. They will therefore be easier to maintain, modify, and (in this case) combine into a single knowledge base from the ten original individual ones.

Section W18.2 Review Questions 1.

Define knowledge engineering.

“The process of acquiring knowledge from experts and building a knowledge base” (p.

W-176).

Students may find other correct answers in other sources. Some of those may define it as the profession rather than the process, by analogy with fields such as (for example) civil engineering.

2.

List and briefly describe each of the five activities of knowledge engineering.

Knowledge acquisition: Obtaining information about how human experts solve a problem.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

369

Knowledge representation: Representing the knowledge so obtained in a knowledge base.

Knowledge validation: Confirming that this knowledge solves the problem(s) it was intended to.

Inferencing: Designing and developing software that can apply this knowledge to a specific case.

Explanation and justification: Enabling an expert system for it to tell users how a conclusion was reached or why a piece of information is needed.

Section W18.3 Review Questions 1.

List the major sources of knowledge.

The major sources are: documented knowledge and undocumented knowledge. Within these categories one can identify many sub-categories. For example, documented knowledge can exist in books, films, databases, etc.

2.

Differentiate between documented and undocumented knowledge.

Documented knowledge exists in tangible form. Undocumented knowledge is in people’s minds.

3.

How is knowledge collected?

The way knowledge is collected depends on its source. Knowledge in databases can be collected through automated means such as data mining. Knowledge documented in other forms can be collected via human senses. Knowledge on the Internet can be collected automatically if it was suitably coded originally. Undocumented knowledge is usually collected by knowledge engineers working with human experts.

4.

Define shallow knowledge and deep knowledge.

Shallow knowledge represents surface-level information that reflects the input/output relationships of a system. Deep knowledge reflects the internal structure of a system. It causes the input/output relationships to be what they are.

5.

Define procedural knowledge and declarative knowledge.

Procedural knowledge describes an expert’s approach to that system, problem or situation.

Declarative knowledge describes a system, problem or situation.

6.

Define metaknowledge.

“Knowledge about knowledge,” knowledge about how knowledge-based systems work.

370

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Section W18.4 Review Questions 1.

List the difficulties in acquiring knowledge from experts.

Some difficulties, from the Technology Insights W18.2 box on p. W-183, are: • Experts may not know how to articulate their knowledge or may be unable to do so.

• Experts may lack time or may be unwilling to cooperate.

• Testing and refining knowledge is complicated.

• Methods for knowledge elicitation may be poorly defined.

• System builders tend to collect knowledge from one source, but the relevant knowledge may be scattered across several sources.

• Builders may attempt to collect documented knowledge rather than use experts.

The knowledge collected may be incomplete.

• It is difficult to recognize specific knowledge when it is mixed up with irrelevant data.

• Experts may change their behavior when they are observed or interviewed.

• Problematic interpersonal communication factors may affect knowledge engineer and expert.

(Students may find other difficulties in other sources.)

2.

List the required skills of knowledge engineers.

Some required skills, from the Technology Insights W18.3 box on p. W-184, are: • Computer skills (e.g., hardware, programming, software) • Tolerance and ambivalence • Effective communication abilities (e.g., sensitivity, tact, diplomacy) • Broad educational background • Advanced, socially-sophisticated verbal skills • Fast-learning capabilities (of different domains) • Understanding of organizations and individuals • Wide experience in knowledge engineering • Empathy and patience • Persistence • Logical thinking • Versatility and inventiveness • Self-confidence.

(Students may find other required skills in other sources.)

3.

What are the major roles of knowledge engineers?

Some roles, from the list near the bottom of p. W-184, are: 1.

Advise the expert on the process of interactive knowledge elicitation.

2.

Set up and appropriately manage the interactive knowledge acquisition tools.

3.

Edit the unencoded and coded knowledge base in collaboration with the expert.

4.

Set up and appropriately manage the knowledge-encoding tools.

5.

Validate application of the knowledge base in collaboration with the expert.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

371

6.

Train clients in effective use of the knowledge base in collaboration with the expert by developing operational and training procedures.

(Students may find other roles in other sources.)

4.

Define knowledge modeling.

Knowledge modeling is the process of developing a structure within which the knowledge of an expert, or an expert system, can be represented.

(This term is not explicitly defined in the text. Students may write their own correct definitions or find them in other sources.)

5.

Describe structured and unstructured interviews.

A structured interview follows a predetermined process. An unstructured interview does not; it is basically a discussion focused on how the expert solves a particular type of problem.

6.

Define process tracking and analysis.

Process tracking is a psychological approach to following an expert’s reasoning process.

Protocol analysis is a method of process tracking in which an expert attempts to verbalize his or her thought process as he or she solves a problem. (Analysis in this question, refers to protocol analysis.)

7.

Describe observation as a method of knowledge acquisition.

Observation refers to watching an expert solve a problem. It can track either physical (motor) movements or eye movements.

8.

List some other manual methods of knowledge acquisition.

Some methods, from the shaded box on pp. W-190–191, are: • Case analysis • Critical incident analysis • Discussions with users • Commentaries • Conceptual graphs and models • Brainstorming • Prototyping • Multidimensional scaling • Johnson’s hierarchical clustering • Performance review (of a knowledge-based system, not of personnel!)

(Students may find other methods in other sources.)

9.

Describe RGA. RGA (Repertory grid analysis) is a (often computer-supported) knowledge acquisition method based on personal construct psychology. In it, a domain expert identifies

372

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

important objects within a domain, their attributes, and a scale along which those attributes can be rated.

10.

List and describe other automated methods of knowledge acquisition.

Several other automated methods of knowledge acquisition are discussed on pp. W-191– 192 of the text. Students may find still more methods in other sources.

Section W18.5 Review Questions 1.

List the benefits of using multiple experts.

Some benefits, from Table W18.3 on p. W-197, are: • On average, fewer mistakes than with a single expert • Elimination of the need for using a world-class expert (who is difficult to get and expensive) • Wider domain than a single expert’s • Synthesis of expertise • Enhanced quality due to synergy among experts (Students may find other benefits in other sources.)

2.

List the shortcomings of using multiple experts.

Some shortcomings, also from Table W18.3 on p. W-197, are: • Groupthink phenomenon • Fear on the part of some experts of senior experts or a supervisor (i.e., lack of confidentiality) • Compromising solutions generated by a group with conflicting opinions • Wasted time in group meetings • Difficulties in scheduling the experts • Dominating experts (i.e., controlling, not letting others speak) (Students may find other shortcomings in other sources.)

3.

List some configurations of multiple experts.

• • • •

4.

Individual experts, whose expertise is combined later (as in the opening vignette) A primary expert whose knowledge is augmented by that of secondary experts Groups of experts whose joint solution process is used in the system Panels of experts used to review system progress, content and plans

List methods for using multiple experts.

Some methods, condensed from the bullet list on p. W-198, are: • Blend several lines of reasoning through consensus methods • Use an analytic approach, such as group probability or an analytic hierarchy process • Keep the lines of reasoning distinct and select a specific one to match the situation.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

373

• Automate the process, using software or a blackboard approach • Decompose the knowledge acquired into specialized knowledge sources (Students may find other methods in other sources.)

Section W18.6 Review Questions 1.

Define automated acquisition of knowledge and describe its objectives.

Automated acquisition of knowledge refers collectively to methods of acquiring knowledge in which the role of people is absent or minimized. Its objectives are (p. W198, bottom): • To increase the productivity of knowledge engineering (reduce the cost) • To reduce the skill level required from the knowledge engineer • To eliminate (or drastically reduce) the need for an expert • To eliminate (or drastically reduce) the need for a knowledge engineer • To increase the quality of the acquired knowledge

2.

Describe the acquisition of documented knowledge.

Knowledge can often be acquired from other sources in addition to, or instead of, human experts. This eliminates the need to use an expert. It is useful in knowledge-based systems in which handling information, rather than world-class expertise, is the main concern. Searching policy manuals or catalogues are examples.

At present, few methodologies deal with knowledge acquisition from documented sources. It may be difficult to find the necessary knowledge in a database. Two ways to improve searches are to use domain knowledge to guide the search and to use intelligent agents. Another proposed approach uses induction of classification rules based on set theory.

Documented knowledge of almost any type can be transferred easily to a database. It can then be analyzed manually or by artificial intelligence technologies.

Thus, ES can be used to build other ES. The ability to construct ES to scan databases and text is increasing. Data in computers can be retrieved electronically to create or update knowledge bases without human intervention. The field is developing new methods to interpret meanings in order to find rules and other forms of knowledge.

(Condensed from Technology Insights W18.5 at the top of p. W-199).

3.

Describe rule induction.

Induction in general is the logical process of obtaining a general law from specific examples.

Rule induction in expert systems is the process of deriving general rules from examples. If an induction system were shown ten car loan applications, in which all loans on three-year-old or newer cars were approved and all those on cars four or more years old were turned down, it might infer that loans should be approved only if a car is less than four years old.

374

4.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Describe the advantages of rule induction.

• •

5.

In large or complex domains, experts may be unable to explain how they reached their answers but may still be able to provide specific examples.

The approach reduces or eliminates the need for a knowledge engineer, allowing a systems analyst or the expert himself/herself to derive rules.

• List difficulties in rule induction.

Some difficulties, condensed from the bullet list on p. W-201, are: • Some induction programs may generate rules that are not easy for a human to understand.

• An expert may still be needed to specify which problem attributes are significant.

• Induction algorithms, which attempt to generate efficient decision trees, vary in capability.

• This approach is only good for rule-based classification problems. (However, many problems can be rephrased or split to fall into this category.)

• The number of attributes must be fairly small.

• The number of examples necessary can be very large.

• The set of examples must be “sanitized;” exception cases must be removed.

• This approach is limited to situations under certainty.

• One cannot know in advance if there are enough examples or if an algorithm is good enough.

(Students may find other difficulties in other sources.)

6.

Describe interactive induction.

Interactive induction is a process in which an expert interacts with a computer program and the program derives, from the interaction, models of domain knowledge and rules for applying it.

Section W18.7 Review Questions 1.

Define evaluation, validation, and verification of knowledge.

• Evaluation is an effort to assess an ES’s overall value by assessing acceptable performance levels as well as whether the system would be usable, efficient, and cost-effective.

• Validation is the part of evaluation that deals with the functional performance of the system as it compares to the expert’s. It confirms that a system performs with acceptable accuracy.

• Verification substantiates that a system is correctly implemented to its specifications.

2.

Describe validation methods.

They test three things: whether the system’s decisions agree with the expert’s, whether the system uses the same inputs and processes as the expert (using different inputs can be a clue to an internal problem that might not affect output from the text cases) and whether

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

375

expert and novice decisions are significantly different (which helps decide if the test cases are enough to do the job).

3.

Describe automated verification of knowledge.

One approach is as follows: When an ES is presented with a new case to classify, it assigns a confidence factor to each selection. By comparing these confidence factors with those provided by an expert, it is possible to measure the accuracy of the ES for each case. By performing comparisons on many cases, it is possible to derive an overall measure of ES performance.

Section W18.8 Review Questions 1.

Define production rules and knowledge/inference rules.

Production rules are a form of knowledge representation which represent it by condition/action pairs: IF some condition, THEN some conclusion. (Some rules add ELSE some other conclusion.)

Knowledge rules are production rules that relate to the problem being solved.

Inference rules are production rules that describe the inference process itself. They are independent of any specific problem domain.

List the major advantages and limitations of rules.

Some advantages, from the shaded area at the top of p. W-206, are: • Rules are easy to understand because they are a natural form of knowledge to many people.

• Inferences and explanations are easily derived.

• Modifications and maintenance are relatively easy.

• Uncertainty is easily combined with rules.

• Each rule is often independent of all others.

Some limitations, from the bullet list near the bottom of p. W-206, are: • Complex knowledge requires thousands of rules. This may create difficulties in using and maintaining the system.

• Builders like rules, so they try to force all knowledge into rules rather than look for more appropriate representations.

• Systems with many rules may encounter a limitation of the control (inference) program.

• Some programs have difficulty evaluating rule-based systems and making inferences.

(Students may find other advantages and limitations in other sources.)

2.

Define semantic network.

A semantic network (not to be confused with the semantic Web!) is a graphical depiction of knowledge, using nodes and links to show hierarchical relationships among objects.

376

3.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Define frame and describe the advantages of frames.

A frame is a data structure that includes all the knowledge about a particular object.

Advantages, or capabilities, of frames include (from bullet list at bottom of p. W-208, slightly condensed): • Ability to clearly document information about a domain model • Related ability to constrain the allowable values that an attribute can take on • Modularity of information, permitting ease of system expansion and maintenance • Readability and consistency • Syntax for referencing domain objects in the rules • Platform for building a graphic interface with object graphics • Allow the scope of facts considered during forward or backward chaining to be restricted • Access to a mechanism that supports the inheritance of information down a class hierarchy (Students may find other advantages in other sources.)

4.

Describe object-oriented knowledge representations.

Object-oriented knowledge representations resemble frames. A frame’s slots correspond to an object class; its facets correspond to an instance. Object methods correspond approximately to frame procedural attachments. The concept of inheritance carries over directly.

5.

Define decision tree and decision table.

A decision tree is a graphical representation of the rules in an expert system’s knowledge base.

A decision table is a representation of a decision rule, where each possible set of conditions on one side of the table is matched with a conclusion on the other side of the table.

(The answers to Review Questions 1 and 2 of Section 4.6 define the same terms in a different context where they have different meanings.)

6.

Explain predicate calculus.

Predicate calculus is a formal reasoning system that extends propositional logic by the ability to represent relationships among items and derive new relationships from existing ones. It forms the theoretical foundation for rule-based expert systems.

Section W18.9 Review Questions 1.

Define inferencing and inference engine.

Inferencing is the process in which an expert system applies rules from its knowledge base to facts about a specific situation in order to derive conclusions.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

377

An inference engine is a program that performs inferencing.

2.

Define forward chaining and backward chaining.

Forward chaining is an inferencing process that applies known facts to rules to derive new facts that can then be applied to other rules. The process continues until a conclusion is reached.

Backward chaining is an inferencing process that attempts to evaluate rules that lead to conclusions. To do so it needs facts, which may be conclusions of other rules. The process continues until all required facts are available as input.

3.

Describe inference trees.

An inference tree is a schematic representation of the rules in a knowledge base. Rules are linked when the conclusion of one rule is used in the condition of another.

Section W18.10 Review Questions 1.

Describe explanation facility.

The explanation facility of an expert system is the part of the system that explains either why a particular piece of information is needed (typically from the user; databases don’t care why another program asks them for something) or how a particular conclusion was reached (the facts that led to it and the rules those facts were applied to).

2.

Why do we need explanations?

Some reasons, condensed from the bullet list near the top of p. W-224, are: • Make the system more intelligible to the user.

• Uncover shortcomings of the rules and knowledge base.

• Explain situations that were unanticipated by the user.

• Satisfy psychological needs by helping users feel more assured about the actions of the ES. • Clarify assumptions underlying the system’s operations to both the user and the builder.

• Conduct sensitivity analyses.

(Students may find other reasons in other sources. One item not mentioned here is to train users in problem solving, by explaining how the experts whose knowledge is embodied in their ES would have dealt with a given situation.)

3.

What type of explanations exist?

The basic types are “Why” explanations, which explain why a particular piece of input information is needed, and “How” explanations, which explain how a conclusion was reached.

378

4.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Describe metaknowledge.

Metaknowledge is “knowledge about knowledge,” or a system’s knowledge about how it reasons. Inference rules are a form of metaknowledge. So is an explanation facility.

Section W18.11 Review Questions 1.

Relate uncertainty to knowledge.

Knowledge is often uncertain. Knowledge may reflect an uncertain prediction of future events, an expert’s best guess at what will happen if a certain action is taken, an attempt to describe human behavior (which varies, as we all know), or a description of the usual situation (which may have exceptions). A full description of knowledge must therefore include a description of its uncertainty.

2.

How is uncertainty represented?

Externally, it is typically represented in one of three ways: as a number (e.g., from 1 to 100), graphically (via a “slider”) or via a term chosen from an ordered set (e.g., from “very unlikely” to “nearly certain”). Within an expert system, all these are likely to end up as numerical values.

3.

Define probability ratio.

The probability ratio of an event is the ratio of the number of times an event is expected to occur to the total number of situations in which it could occur. For example, the probability ratio of a student receiving an A is the number of As he or she receives divided by his or her number of grades.

4.

What is the Bayesian approach?

The Bayesian approach is a statistical inference method in which historical observations and/or known facts are used to compute the probability that a hypothesis may be true. For example, suppose we have two bowls with 25 marbles in each. One contains mostly blue marbles (with a few red), the other mostly red (with a few blue). A person takes a marble from one of them. We do not know from which, but if we know the marble’s color, we can infer the probability that it came from one bowl or the other. Here, a marble is more likely to have come from the bowl that contains mostly its color.

5.

Describe theory of evidence and belief functions.

The theory of evidence (or Dempster-Shafer theory) is a mathematical approach to combine separate pieces of information (evidence) to yield upper and lower bounds on the probability of an event.

A belief function is the degree to which we can establish a lower bound on the probability of an event. For example, we may not know if an omniscient deity exists, but we are willing to say that it is at least 10 percent probable that one does. It may be more probable than that, some put the figure at or near 100 percent, but even that 10 percent figure can be a useful guide to some of our actions.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

379

(The upper bound on the probability of an event is referred to in this context as its plausibility.)

6.

Describe certainty factors.

A certainty factor is a numerical expression of the degree to which we believe that a proposition is, or will be, true. These are not probabilities, since the truth or falsity of the proposition can be established by information which we do not have at present.

Section W18.12 Review Questions 1.

List the six phases of prototyping ES and describe each briefly.

The six phases of developing an ES via the prototyping method are: Phase I: Project initialization: All steps that lead up to management approval to proceed with the project. Corresponds to the initialization and feasibility study for any IS. Phase II: System analysis and design: Figuring out the development strategy, choosing knowledge sources, selecting a development environment.

Phase III: Rapid prototyping, with the demonstration prototype: Putting together a smallscale version of the system to confirm the above choices and obtain user feedback.

Phase IV: System development: Develop the knowledge base, create the user interface, write the explanations, evaluate the systems as it is being developed.

Phase V: Implementation: User testing/acceptance, integration with other software (if necessary) and into business processes, documentation (hopefully not an afterthought!), security (ditto).

Phase VI: Post-implementation: Operation, maintenance, expansion, ongoing evaluation and feedback.

2.

How do you select a development environment? What are the alternatives?

The fundamental choice is between programming languages and ES shells, with a hybrid environment that combines elements of both also possible. (The other conceptual alternative, buying a package, has presumably been eliminated before this question is asked, though in a practical situation that option may be evaluated along with development packages.)

If a shell provides the necessary facilities and features, using one is simpler than developing an ES from scratch in a programming language. The choice of a specific shell will depend on features, ability to interface with other aspects of the corporate IS if necessary, and other usual factors such as vendor stability and price.

Developing an expert system from scratch is seldom necessary and not to be undertaken lightly. Even so-called “AI languages” such as LISP and Prolog are designed more for computer scientists than for IS developers.

A toolkit (sometimes written as two words, tool kit) can be a viable compromise when most of an ES can be developed with standard tools but a few pieces require custom programming.

380

3.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

List the major topics of implementation.

See answer to Question 1 above, Phase V.

4.

List the activities of postimplementation.

See answer to Question 1 above, Phase VI.

Section W18.13 Review Questions 1.

Describe the Internet as a communication media for facilitating knowledge engineering.

The Internet can facilitate knowledge engineering work, and particularly knowledge acquisition, in several ways. Four of them are: • Interviews with domain experts can be conducted remotely.

• Knowledge bases can be worked with, expanded, maintained, etc., from a distance.

• Documented knowledge can be found on the Web. • Intelligent agents can facilitate identifying relevant knowledge.

(The misprint “media” in the online text in early January 2007 will be corrected to “medium,” hopefully by the time you read this.)

2.

How can knowledge sources available on the Internet facilitate knowledge engineering?

Intelligent agents, some in popular search engines and some custom, can identify relevant knowledge to be incorporated into a knowledge base. This can be useful, for example, in creating a foundation of publicly available knowledge for a problem, or published “best practices” to deal with a situation, prior to eliciting proprietary (perhaps better) knowledge from a company’s own experts.

ANSWERS TO QUESTIONS FOR DISCUSSION 1.

         

Assume that you are to collect knowledge for one of the following systems: a.

An advisory system on equal-opportunity hiring situations in your organization b.

An advisory system on investment in residential real estate c.

An advisory system on how to prepare your federal tax return (form 1040) What sources of knowledge would you consider for these systems? (Refer to Figure W18.2.) a.

Federal and local regulations; historical data about your company; comparative data with other companies in the region and in the industry; expert advice on minorities, their availability, skills, and expectations; demographic information about media through which to publicize vacancies, company history with hiring and/or promoting minority employees, general and industry-specific best practices. Most of these are available on the Web. The rest are probably available through your company’s HR and/or EEO office.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

381

b.

Real estate listings (available on the Web; some sources are free), desirable and undesirable locations (from census information), investment objectives (from users), current mortgage rates under different conditions (free on the Web). c.

Tax guides (IRS publications in the United States; available online free), best practices in tax return preparation (from accounting consulting firms; may require payment), identification of problem areas related to the regulations (intelligent searching of tax-related Internet discussion groups), statistical data on chances to be audited by category (reported annually in the news), scale of punishments for various types of dishonesty (legal codes).

(Note that the question asks for sources of knowledge, not just types of knowledge.)

2.

Discuss the major advantages of rule induction. Give an example that illustrates the method and describe a situation in which you think it would be most appropriate.

Advantages: See answer to Review Question 4 of Section W18.6.

Example: See answer to Review Question 3 of Section W18.6. This is also a situation in which it would be appropriate, at least to generate a first prototype that would then be refined.

3.

Transfer of knowledge from a human to a machine is said to be more difficult than transfer of knowledge from a human to a human. Why? Machines require that knowledge be inputted and organized in a much more detailed level than humans require. Human knowledge must be prepared for the machine (e.g., by adding steps, details, etc.) whereas a person could usually simply tell another person about it. Also, humans can pick up nonverbal cues and nuances to determine the meaning of the knowledge, while the same information must be made explicit to a computer. That is why humans have been able to teach other humans for millennia, but still have difficulty teaching computers to do anything.

4.

What are the major advantages and disadvantages of working with a prototype system for knowledge acquisition?

One advantage is that the decision maker(s) and user(s) learn more about the problem they are trying to solve while performing the prototyping process, as occurred in the opening vignette and in the application cases in this chapter.

Others are that the expert can react to the results of the prototype and deliver feedback quickly. Users can comment easily as the prototype provides them with something to critique. Users can also see the direction that the project and the knowledge development is taking every step of the way, helping them guide it in the direction they need to solve their business problems.

Disadvantages are those associated with prototyping any system: it can take more time to iterate a system than to develop it to a fixed specification, developers get frustrated as users “can’t make up their minds,” users may not understand how much behind-the-scenes work remains to be done to get from a visually appealing prototype to a useful production system, and—if a working prototype is to be used as a surrogate specification, rather than evolved directly into the finished system—it may be difficult to match a user interface developed with a production tool to what users saw on a prototype.

382

5.

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

Discuss some of the problems of knowledge acquisition through the use of expert reports.

• • • • • •

6.

The expert is probably not trained in knowledge engineering and almost surely did not write the report with this use in mind. Reports may therefore not have what is needed for expert system development.

Reports may describe how a task is done, not how it should be done.

Conversely, reports may reflect wishful thinking or what a writer thinks management wants to hear, rather than reality.

Steps may be omitted because they are obvious to the expert and, in the expert’s view, to the report’s intended readers as well.

Experts may not know how to organize their thoughts well.

Experts may not like to write and may write poorly.

Review the functions of ACQUIRE from aiinc.ca. What are the major benefits of ACQUIRE (or similar products) compared to a conventional rule induction package?

It is an interactive process. Thus, it can be used by an expert to deal with cases where automatic induction is not possible. It provides for structured discovery of knowledge, i.e., it helps the expert think through the process. It also helps the expert identify important decision criteria.

7.

Why is it important to have knowledge analyzed, coded, and documented in a systematic way? How is this related to the knowledge management systems and e-learning systems that many organizations are adopting?

If knowledge is not organized systematically, its quality may be poor (one may forget certain aspects) and a system may produce inconsistent results. Knowledge management systems require that knowledge be codified in a systemic way for access across an organization.

8.

With machine learning methods, a computer develops knowledge models automatically.

Explain why (or why not) machine learning techniques such as rule induction and neural computing can enhance knowledge acquisition.

Subject matter experts can be unwilling or unable to provide the knowledge an expert system needs. Machine learning can bypass human experts and thus avoid these problems.

(See Chapter 8 for a discussion of machine learning in neural networks.)

9.

Why is frame representation considered more complex than production rule representation? What are the advantages of the former over the latter?

Although the representation of information in frames may be simple, using that knowledge for inferencing may be very complex. Production rules, with their IF-THENELSE form, are an easier way for most people to describe procedural knowledge.

An advantage of frames is that they represent information about a situation independently of the rules that operate on it. They are a natural way to reflect the human tendency to categorize objects with increasing specificity: living

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

383

creature>animal>mammal>cow>Elsie. Rules that apply to mammals are known to apply to cows, and thence to Elsie.

10.

Provide an example that shows how a semantic network can depict inheritance.

The “is-a” relationship in a semantic network indicates that a member of one class belongs to a higher class. For example, a semantic network could have an “is-a” relationship between cow and mammal, indicating that a cow is a mammal. This tells us that a “cow” object (or frame) inherits the properties of a “mammal” object (or frame).

11.

Class scheduling is a domain that needs certain expertise. The scheduler needs to consider the professor’s preferences, student course conflicts, equipment available in different classrooms, equipment requirements of different courses, and so on. Identify a knowledge rule with a procedural rule that is useful for class scheduling.

Recall (p. 182) that knowledge rules relate to the problem being solved, while procedural rules relate to how that problem is to be solved.

A knowledge rule might be: IF instructor is Dr. Mallach, THEN room-has-projector is mandatory A procedural rule might be: If knowledge rule contains “mandatory” THEN set priority to “high” The way such rules are phrased varies from one development environment to another. Most shells do not allow procedural rules to be changed, but have other ways to achieve objectives such as what is intended in the above example.

12.

Certainty factors are popular in rule-based systems. Why? What unique features does the theory of uncertainty provide?

Certainty factors are popular because they are intuitively accessible to human experts, who think in similar terms. Their theory provides the ability to say both how much we believe something to be true and how much we believe its opposite to be true, independently of each other, which matches the way people often think about future events (yet often without realizing that they are doing so). Certainty factors also provide expert systems with flexibility in how they are manipulated, since they are not tied to the fixed formalisms of probability or statistics.

13.

If you had a dialog with a human expert, what questions besides why and how questions would you be likely to ask? Give examples.

I might ask the other four “journalistic” questions: who, where, when and what. Answers would provide added insight into the problem. Example: “When should this measurement be taken?”

I might also ask “why not?” questions, so the expert could explain why a particular choice was not made. Example: “Why did you not recommend that I take Business Law?”

384

Decision Support and Business Intelligence Systems (8th Edition) Instructor’s Manual

14.

Describe the relationship between metaknowledge and explanations.

Metaknowledge is knowledge about knowledge. Explaining how a system reached a conclusion is talking about its knowledge. Simple explanations can be coded into expert system rules, but these do not go beyond a single rule and thus do not show relationships among rules or provide insight into a system’s overall problem-solving strategy.

Metaknowledge allows a system to understand the overall rationale behind a conclusion by looking at the broader picture, enabling users to make informed decisions about following the system’s advice. (However, most expert systems do not use metaknowledge effectively for this purpose.)

15.

What can happen if the wrong knowledge representation of a problem domain or an inappropriate ES shell or tool is selected?

Exactly what can happen to any IS if an inappropriate data structure or development tool is used: • It may take longer, and cost more, than necessary to develop the system.

• If the tool or data representation is truly bad, it may be impossible (in practice) to develop it without going back and revisiting that choice.

• If the system is eventually developed, it may not work well, may be inefficient in its use of computing resources, and may be hard to maintain.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS Online Chapter 18 has no end-of-chapter application case.

   