Decision Support and Business Intelligence Systems 9e Turban Delen Solution Manual

Decision Support and Business Intelligence Systems 9e By Turban Delen

Email: Richard@qwconsultancy.com

Chapter 1: Decision Support Systems and Business Intelligence

Learning Objectives for Part I 1.

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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 1. Understand today’s turbulent business environment and describe how organizations survive and even excel in such an environment (solving problems and exploring opportunities) 2. Understand the need for computerized support of managerial decision making 3. Understand an early framework for managerial decision making 4. Learn the conceptual foundations of the decision support systems (DSS1) methodology 5. Describe the business intelligence (BI) methodology and concepts and relate them to DSS 6. Describe the concept of work systems and its relationship to decision support 7. List the major tools of computerized decision support 8. Understand the major issues in implementing computerized support systems

CHAPTER OVERVIEW This book deals with a collection of computer technologies that support managerial work— essentially, decision making. These technologies have had a profound impact on 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.

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corporate strategy, performance, and competitiveness. These techniques are also strongly connected to the Internet, intranets, and Web tools, as shown throughout the book. In Part I, we provide an overview of the whole book in one chapter. We cover several topics in this chapter. The first topic is managerial decision making and its computerized support; the second is frameworks for decision support. We then introduce business intelligence.

We also provide brief coverage of the tools used and their implementation, as well as a preview of the entire book.

The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate.

Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these, in the framework of the needed decisions, must be done quickly, frequently in real time, and usually requires some computerized support. This book is about using business intelligence as computerized support for managerial decision making. It concentrates both on the theoretical and conceptual foundations of decision support, as well as on the commercial tools and techniques that are available.

This introductory chapter provides more details of these topics as well as an overview of the book. This chapter has the following sections:

CHAPTER OUTLINE 1.1 OPENING VIGNETTE: NORFOLK SOUTHERN USES BUSINESS INTELLIGENCE FOR DECISION SUPPORT TO REACH SUCCESS  Questions for the Opening Vignette A. 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 DECISION MAKING PROCESS ⧫ Technology Insights 1.1: Decision Making Ability Rated First in Survey  Section 1.3 Review Questions

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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 1.1: Giant Food Stores Prices the Entire Store C. COMPUTER SUPPORT FOR UNSTRUCTURED DECISIONS D. COMPUTER SUPPORT FOR SEMISTRUCTURED PROBLEMS  Section 1.5 Review Questions 1.6

THE CONCEPT OF DECISION SUPPORT SYSTEMS (DSS) A. DSS AS AN UMBRELLA TERM ⧫ Application Case 1.2: A DDS for Managing Inventory at GlaxoSmithKline 1.

DSS As A Specific Application 2.

The Architecture of DSS 3.

Types of DSS B. EVOLUTION OF DSS INTO BUSINESS INTELLIGENCE  Section 1.6 Review Questions

1.7

A FRAMEWORK FOR BUSINESS INTELLIGENCE (BI) A. DEFINITIONS OF BI B. A BRIEF HISTORY OF BI C. THE ARCHITECTURE OF BI 1.

Data Warehousing 2.

Business Analytics ⧫ Application Case 1.3: Location, Location, Location 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 1.4: Alltel Wireless: Delivering the Right Message, to the Right Customers, at the Right Time G. THE ORIGINS AND DRIVERS OF BI H. THE DSS–BI CONNECTION 1.

Management Support Systems (MSS)

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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 1.5: 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

1.10

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 ⧫ Application Case 1.6: The Next Net

1.11

RESOURCES, LINKS, AND THE TERADATA UNIVERSITY NETWORK CONNECTION A. RESOURCES AND LINKS B. CASES C. VENDORS, PRODUCTS, AND DEMOS D. PERIODICALS E. ADDITIONAL REFERENCES F. THE TERADATA UNIVERSITY NETWORK CONNECTION G. 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: Vodafone Uses Business Intelligence to Improve Customer Growth and Retention Plans

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Questions for the Case

References TEACHING TIPS/ADDITIONAL INFORMATION

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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 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 and 9 are more on the technology side. Sections 5, 6, and 7 have elements of both in different subsections. (Sections 1 and 11 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 Norfolk Southern 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 Norfolk Southern used BI “for Decision Support to

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Reach Success.” 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!

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 1.1 Review Questions 1. How are information systems used at Norfolk Southern to support decision making?

Norfolk Southern has always used a variety of sophisticated systems to run its business. Becoming a scheduled railroad, however, required new systems that would first use statistical models to determine the best routes and connections to optimize railroad performance, and then apply the models to create the plan that would actually run the railroad operations. These new systems were called TOP, short for Thoroughbred Operating Plan; TOP was deployed in 2002.

2. What type of information is accessible through the visualization applications?

Norfolk Southern built a TOP dashboard application that pulls data from the data warehouse and then graphically depicts actual performance against the trip plan for both train performance and connection performance. The application uses visualization technology so that field managers can more easily interpret the large volumes of data (e.g., there were 160,000 weekly connections across the network).

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3. What type of information support is provided through accessNS? accessNS allows more than 14,500 users from 8,000 customer organizations to log in and access predefined and custom reports about their accounts at any time.

Users can access current data, which is updated hourly, or they can look at data from the past 3 years. accessNS provides alerting and RSS feed capabilities 4. How does Norfolk Southern use the data warehouse for HR applications?

Recently, the HR department needed to determine where to locate its field offices in order to best meet the needs of Norfolk Southern’s 30,000+ employees. By combining employee demographic data (e.g., zip codes) with geospatial data traditionally used by the Engineering Group, Human Resources was able to visually map out the employee population density, making it much easier to optimize services offices locations.

5. Can the same data warehouse be used for business intelligence and optimization applications?

Yes, Norfolk Southern was the first railroad to offer self-service business intelligence, and its innovation is setting an example that other railroads have followed.

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

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•

3.

(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 Sarbanes-Oxley Act and other reporting-related legislation is a must; social responsibility of companies is increasing.

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 • 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.)

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.

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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.

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

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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.

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.

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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-toapply 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.

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.

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.)

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

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.

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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.

4.

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.”

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

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.

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 (This section has no review questions.)

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

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ANSWERS TO QUESTIONS FOR DISCUSSION

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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.

3. 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

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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. 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.

6.

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.

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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.

8.

Distinguish BI from DSS. • • • • •

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.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

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What were the challenges for Vodafone New Zealand?

How do we increase our profit margins? How do we add revenue streams from these customers? And how do we keep them as customers?” Vodafone needed to make better decisions based on real-time knowledge of its market, customers, and competitors.

2.

How did it address these issues?

First, Vodafone formed a customer knowledge and analysis department to conduct analysis, modeling, market research and competitive intelligence.

Vodafone then implemented an enterprise data warehouse (EDW) to obtain a

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single view of all of the information in the organization. EDW permits a wellintegrated view of all of the organization’s information to allow generation of predefined or ad hoc queries and reports, online analytical processing, and predictive analysis.

3.

List the tools used by Vodafone’s applications.

In addition to the Teradata data warehouse platform, many other software tools, such as KXEN, SAS, and SPSS, were also used to build models and generate insights.

4.

What benefits are being derived from this initiative?

Perhaps the biggest benefit of the EDW is that the analysts can spend more time generating insights than managing data. The system also provides better information to decision makers to support the decision-making process.

5.

What can we learn from this case?

The goal is “to get the best possible return . . . from the process of campaigning and contacting customers.”

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Chapter 2: Decision Making, Systems, Modeling, and Support

Learning Objectives for Part II 1. Understand the conceptual foundations of decision making 2. Understand Simon’s four phases of decision making: intelligence, design, choice, and implementation 3. Understand the concept of rationality and its impact on decision making 4. Understand the foundations, definitions, and capabilities of decision support systems (DSS) and business intelligence (BI) 5. Describe DSS components and technology levels 6. Describe the various types of DSS and explain their use 7. Explain the importance of databases and database management 8. Explain the importance of models and model management In Part II, we concentrate on decision making, the decision support methodology, technology components, and development. Throughout, we highlight the major impacts of the Internet on DSS. Chapter 2 contains an overview of the conceptual foundations of decision making, the reason that all DSS are developed. Chapter 3 provides an overview of DSS: its characteristics, structure, uses, and types. Some of the major components of DSS are presented in Chapter 4.

Learning Objectives for Chapter 2 1. Understand the conceptual foundations of decision making 2. Understand Simon’s four phases of decision making: intelligence, design, choice, and implementation 3. Recognize the concepts of rationality and bounded rationality and how they relate to decision making 4. Differentiate between the concepts of making a choice and establishing a principle of choice 5. Learn how DSS support for decision making can be provided in practice 6. Understand the systems approach

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CHAPTER OVERVIEW Our major focus in this book is the support of decision making through computer-based information systems. The purpose of this chapter is to describe the conceptual foundations of decision making and how support is provided. This chapter includes the following sections: CHAPTER OUTLINE 2.1

OPENING VIGNETTE: DECISION MODELING AT HP USING SPREADSHEETS  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 ⧫ Application Case 2.1: Making Elevators go Faster!

B. PROBLEM CLASSIFICATION C. PROBLEM DECOMPOSITION D. PROBLEM OWNERSHIP  Section 2.5 Review Questions

2.6

DECISION MAKING: THE DESIGN PHASE A. SELECTION OF A PRINCIPLE OF CHOICE

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

B.

C. D. E. F. G. H. I. J. K.

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NORMATIVE MODELS ⧫ Technology Insights 2.1: The Difference Between a Criterion and a Constraint ⧫ Technology Insights 2.2: Are Decision Makers Really Rational?

SUBOPTIMIZATION DESCRIPTIVE MODELS GOOD ENOUGH OR SATISFICING DEVELOPING (GENERATING) ALTERNATIVES MEASURING OUTCOMES RISK SCENARIOS POSSIBLE SCENARIOS 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 B. SUPPORT FOR THE DESIGN PHASE ⧫ Technology Insights 2.3: 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 2.2: 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 B. CASES C. VENDORS, PRODUCTS AND DEMOS D. PERIODICALS E. THE TERADATA UNIVERSITY CONNECTION

Chapter Highlights Key Terms Questions for Discussion Exercises

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Teradata Student Network (TSN) and Other Hands-On Exercises Team Assignments and Role-Playing Internet Exercises ⧫ End of Chapter Application Case: Decisions and Risk Management(!) that Led to the Subprime Mortgage Crisis  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

       

This chapter has two major themes: (a) how decisions are made, and (b) how the decision-making 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 W2.2 on 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 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

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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.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

     

Section 2.1 Review Questions 1.

What are some of the key questions to be asked in supporting decision making through DSS? • What are the root issues underlying the decision situation? Do we understand the

problem sufficiently to support it?

• How structured is the decision? Is it unstructured, semi-structured, or structured?

• Does the decision involve judgment? To what extent?

• What data is needed to solve the problem?

• Can an existing tool be leveraged or reused?

• Is a tool needed?

• What is the implementation plan?

2.

What guidelines can be learned from this vignette about developing DSS? • Before building a model, decision makers should develop a good understanding of

the problem that needs to be addressed.

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual • Coming up with nonmodeling solutions is important because if the problem is due to

conflicting priorities, or the misalignment of incentives or unclear lines of authority or plans, then no DSS can help support the decision.

• A model many not be necessary to address the problem.

• Before developing a new tool, decision makers should explore reuse of existing

tools.

• The goal of model building is to gain better insight into the problem, not just to

generate more numbers.

3.

What lessons should be kept in mind for successful model implementation?

• Implementation plans should be developed along with the model. Successful

implementation results in solving the real problem.

• Including the end users in the development process enhances the decision makers’

analytical knowledge and capabilities. And by working together, their knowledge and skills complement each other in the final solution and the success of the implementation.

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 the following: ▪ characteristics of decision making, such as groupthink, experimentation, and information overload.

▪ decision styles of the decision makers ▪ objectives of the decision makers ▪ 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).

▪ rationality of the decision maker. A decision maker should not simply apply IT tools blindly. Rather, the decision maker gets support through a rational approach that simplifies reality and provides a relatively quick and inexpensive means of considering various alternative courses of action to arrive at the best or a good solution to the problem.

2.

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

Today’s business environment is extremely dynamic. While the decision is being made, changes may be occurring in the decision-making environment. Those changes may invalidate the assumptions upon which the decision is based.

There is time pressure from these same changes in the decision-making environment may affect decision quality by imposing time pressure on the decision maker. The fast-changing business environment often requires faster decisions, which may actually be detrimental to decision quality. The cost and expense of collecting information and analyzing a problem, with the difficulty of

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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.

• 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 in group decision making which is not a problem in individual decision making.

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.

They are quite similar activities. 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 decisionmaking 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

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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 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.

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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?

Modern information technology tools can present qualitative factors along with its analysis of quantitative factors, so decision makers can consider both together and use the qualitative information to guide them to the most useful quantitative analyses.

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.

See Figure 2.1 .

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. See Figure 2.1 .

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.

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

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. See the MMS running case in the Online File W2.1.

Section 2.5 Review Questions

1.

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 or gap 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).

2.

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.

3.

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.

4.

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.

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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.

2.

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

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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.

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 decision-making 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.

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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.

2.

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 life-cycle management provide such capability. Routine and ad-hoc reports can also help: regular reports can be designed to assist in

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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 goalseeking analyses. Different scenarios can be tested for the selected option to reinforce the final decision. A knowledge management system helps identify similar past experiences; 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.)

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15

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 semi-structured 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 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.

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

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 number of defects per output or the incidence of warranty claims).

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

Educational institution: Students’ performance on national tests, number of students getting jobs in their discipline after graduation, 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 per year, public image.

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

5.

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

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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.)

8.

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.

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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 $3,500). 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.

11.

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.

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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.)

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

 

Apply principles of the intelligence phase of Simon’s decision making model, and discuss major problems that led to the subprime mortgage crisis.

The intelligence phase includes the following activities: search and scanning of threats, weaknesses, and problems; problem identification; and problem ownership.

The stories reporting the reasons for the financial crisis paint a picture of a complete disregard for risk, irrational assumptions, lying, pure and simple greed, and follow-the-crowd mentality (groupthink). A key contributing factor was mortgagebacked securities (MBS). How did MBS bring the US Economy to its knees?

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The process of restructuring and re-packaging a pool of financial assets yielding future cash-flows is called securitisation. Such Financial instruments are called Securities, specifically Asset-Backed Securities and are sold to prospective investors who would be interested a piece of the future cash-flows. Any financial asset yielding future cash flows can be securitised by the above process.

Securitisation of assets which are essentially mortgages are called Mortgage Backed Securities (MBS). The riskiness of MBS depends on : a. Type of Mortgagor or Borrower: i. Residential MBS - on residential Property ii. Commercial MBS - on commercial property b. Type of Mortgaged Assets: i. Prime- Borrowers with good credit scores, reliable credit history, verifiable income / assets (Lower Default Risk Rating) ii. Subprime- weaker credit scores, no verification of income etc, (Higher Default Risk Rating) High-risk mortgages based on subprime rates that were used as securities. Those people responsible and profiting from these financial instruments made several wrong assumptions (or totally ignored the risks) regarding what would happen if too many people defaulted on their mortgage payments. Also, homeowners were taking on higher debt than they could afford, if they had been charged prime rates (instead of subprime rates). Subprime rates lasted only about five years, and afterwards. the prime rate would be charged. That change to prime interest rates on the mortgages resulted in a huge increase in monthly mortgage payments that too many owners could not pay. Those conditions led to a huge rate of home foreclosures when mortgage payments were not being paid. One devastating assumption was that real estate prices would continue to increase, but they fell because of the foreclosures. As such, the mortgage backed securities that were listed as assets on companies’ balance sheets became worthless causing them to fail.

In the worst case scenario, the financial guarantee provided by Fannie Mae and Freddie Mac would play a significant role in the event that an adverse economic scenario hits the mortgage market, as they would pitch in and be able to protect the interests of the investors as well as the general economic environment in the mortgage market.

2.

Given what has already taken place, reapply the intelligence phase principles to determine the current state of the problem.

Intelligence is the search for information, which should include the following searches.

The Troubled Asset Relief Program, commonly referred to as TARP, is a program of the U.S. government to buy low-value assets and equity from financial institutions to strengthen its financial sector. It is the largest component of the government's measures in 2008 to address the subprime mortgage crisis. As long as the TARP program remains in effect, the financial problems due to the subprime can be assumed to still exist.

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In addition, scanning reliable sources for information on unemployment rates and monthly changes in those rates; as well as the current rates and changes in home foreclosures, bank failures, new home builds, mortgage interest rates and terms; prime interest rates, GDP, cost of living index; stock market indices (e.g., NY Stock Exchange. NASDAQ) will provide information to determine the state of the problem and the state of the economy (recession or recovery) when students complete this case.

3.

What can you recommend about the crisis now? Apply design and choice phase principles to guide you.

Based on the continuation of the TARP program, high unemployment, and other rates indicating a recession, there has not been a full recovery from the financial crisis.

Students will make recommendations based on the rates listed in the answer to question #2 and the actual state of the economy when answering this question.

Students should not rely on intuition, but provide current statistics from reliable sources to validate their recommendations.

Types of recommendation about the crisis may be: a. implement government programs to stimulate the economy b. increase or decrease prime rates c. extend or reduce the length of unemployment payments d. offer incentives or support improve the skills of workers who have lost their jobs.

4.

What issues will you have to keep in mind as you move your proposed solutions to implementation?

Implementation depends on the solutions that students recommend. Students’ answers should involve reference to a feedback mechanism to check the effectiveness of the recommendation--and to make adjustments as necessary.

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E

Chapter 3: Decision Support Systems Concepts, Methodologies, and Technologies: An Overview

Learning Objectives for Chapter 3 1. Understand possible decision support system (DSS) configurations 2. Understand the key differences and similarities between DSS and business intelligence (BI) systems 3. Describe DSS characteristics and capabilities 4. Understand the essential definition of DSS 5. Understand DSS components and how they integrate 6. 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 7. Explain Internet impacts on DSS and vice versa 8. Explain the unique role of the user in DSS versus management information systems (MIS) 9. Describe DSS hardware and software platforms 10. Understand important DSS classifications 11. Become familiar with some DSS application areas and applications 12. Understand important, current DSS issues

CHAPTER OVERVIEW In Chapter 1, we introduced DSS and stressed its role in the solution of complex managerial problems. In Chapter 2, we presented the decision making methodology. In this chapter, we show how DSS superiority is achieved by examining its capabilities, structure, and classifications.

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CHAPTER OUTLINE 3.1

OPENING VIGNETTE: DECISION SUPPORT SYSTEM CURES FOR HEALTH CARE A. PROBLEM B. SOLUTION C. RESULTS  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: A Spreadsheet-Based DSS Enables Ammunition Requirements Planning for the Canadian Army  Section 3.3 Review Questions

3.4

DECISION SUPPORT SYSTEM CHARACTERISTICS AND CAPABILITIES  Section 3.4 Review Questions

3.5

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

Communications-driven and group DSS (GSS) 2.

Data-driven DSS 3.

Document-Driven DSS 4.

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

Model-Driven DSS 6.

Compound DSS ⧫ Application Case 3.2: Expertise Transfer System to Train Future Army Personnel B. HOLSAPPLE AND WHINSTON’S CLASSIFICATION C. ALTER’S OUTPUT CLASSIFICATION D. OTHER DSS CATEGORIES 1.

Institutional and Ad Hoc DSS 2.

Personal, Group, and Organizational Support 3.

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

3.6

COMPONENTS OF DECISION SUPPORT SYSTEMS

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

A. B. C. D. E.

THE DATA MANAGEMENT SUBSYSTEM THE MODEL MANAGEMENT SUBSYSTEM THE USER INTERFACE SUBSYSTEM THE KNOWLEDGE-BASED MANAGEMENT SUBSYSTEM HOW THE DSS COMPONENTS INTEGRATE  Section 3.5 Review Questions

3.7

THE DATA MANAGEMENT SUBSYSTEM ⧫ Application Case 3.3: Pacific Sunwear Tracks Business Performance A. THE DATABASE B. DATA ORGANIZATION C. DATA EXTRACTION D. THE DATABASE MANAGEMENT SYSTEM ⧫ Technology Insights 3.4: The Capabilities of a Relational DBMS in a DSS E. THE QUERY FACILITY F. THE DIRECTORY G. KEY DATABASE AND DATABASE MANAGEMENT SYSTEM ISSUES 1.

Data quality 2.

Data integration ⧫ Technology Insights 3.5: The 10 Essential Ingredients of Data (Information) Quality Management 3.

Scalability 4.

Data security  Section 3.6 Review Questions

3.8

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 3.6: SNAP DSS Helps OneNet Make Telecommunications Rate Decisions E. THE MODEL BASE MANAGEMENT SYSTEM F. THE MODEL DIRECTORY G. MODEL EXECUTION, INTEGRATION AND COMMAND ⧫ Technology Insights 3.7: Major Functions of an MBMS  Section 3.7 Review Questions

3.9

THE USER INTERFACE (DIALOG) SUBSYSTEM A. MANAGEMENT OF THE USER INTERFACE SUBSYSTEM B. THE USER INTERFACE PROCESS C. DSS USER INTERFACES D. DSS DEVELOPMENTS

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⧫ 

Technology Insights 3.8: Next Generation of Input Devices Section 3.8 Review Questions

3.10

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

3.11

THE DECISION SUPPORT SYSTEM USER  Section 3.11 Review Questions

3.12

DECISION SUPPORT SYSTEM HARDWARE  Section 3.12 Review Questions

3.13

A DSS MODELING LANGUAGE: PLANNERS LAB ⧫ Application Case 3.10: Nonprofits Use Planners Lab as a Decision-Making Tool A. GENERATING ASSUMPTIONS B. PLANNERS LAB TUTORIAL EXAMPLE C. SUMMARY OF PLANNERS LAB CHARTS D. FREQUENTLY USED PLANNERS LAB KEYWORDS

3.14

RESOURCES, LINKS, AND THE TERADATA UNIVERSITY NETWORK CONNECTION A. RESOURCES AND LINKS B. CASES C. MILLER’S MIS CASES D. PERIODICALS E. 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: Spreadsheet Model-based Decision Support for Inventory Target Setting at Procter & Gamble  Questions for the Case References

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TEACHING TIPS/ADDITIONAL INFORMATION

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       

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.

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 leisurely fashion.

You may also find the Application Cases in this chapter especially useful, as they relate the 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. 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 cover them in Chapters 12 and 13.”

ANSWERS TO END OF SECTION REVIEW QUESTIONS

     

Section 3.1 Review Questions 1.

Why would this decision involve senior executives in an organization?

Decision makers in this case were the hospital system’s CFO, the chief nursing executive, unit managers, and the human resources director. These are all senior executives. The Planners Lab model provided the information the decision makers needed to make informed decisions on how to proceed.

2.

What types of decision parameters were used in making this decision?

The model is structured on a biweekly basis in order to incorporate the number of hours worked by core and contingency staff, with a lag of 12 weeks for training

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new hires. The basic model includes each hospital within the health system, a node for the entire health system, a node for human resource variables, and a node for different adjustments to explain the behavior of new hires. The variables within this model are primarily quantitative and include the following: • Total hours demanded for 2009 flu season • Total hours from 2008 flu season • Total hours from 2008 flu season after new hires • Gap between hours demanded and actual hours from 2008 • Gap between hours demanded and actual hours from 2008 after new hires • Total required hires to meet demand • Total remaining hires to meet demand after new hires 3.

What other modeling tools could be used for developing a model?

A commonly used modeling tool is Microsoft Excel.

Because DSS deal with semistructured or unstructured problems, it is often necessary to customize models, using programming tools and languages. Some examples of these are .NET Framework languages, C++, and Java. OLAP software may also be used to work with models in data analysis.

4.

Besides the parameters considered by the modelers in this case study, what other situations could a staffing projection model include?

Other situations that a staffing projection model could include are: • The expected types of skills or levels of skill needed depending on the health care emergency • The availability of overtime hours to cover increased demand • Periods of low or lower demand • Use of online recruiting methods Stduents may have various other answers.

5.

Why is this model a good example of a DSS? There is no consensus on exactly what a DSS is, and no agreement on the standard characteristics and capabilities of DSS. Students will find the answers in Figure 3.5 on p. 77. Most likely, the students will state that this model meets the criteria of a DSS because of the following: • supports a semistructured problem • support managers at all levels • interactive, easy-to-use • humans control the process • relatively easy to develop • effective and efficient • adaptable and flexible

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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.

Students may have trouble with this question because thy might not recognize the answer. Subjective data can be utilized by using “what-if” scenarios to test the impact of values.

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.

Section 3.3 Review Questions 1.

Why do people attempt to narrow the definition of DSS? Broad definitions lead to disagreements as to what actually is a DSS. Narrower definitions are clearer enabling greater focus on common characteristics. Narrow

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definitions help to identify what is and what is not a DSS. Students might recognizer that too much precision is not helpful.

2.

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

One 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 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 byproduct of their primary purpose.

Students’ comparisons will differ depending on their answers to those two questions.

3. Explain how the system described in Application Case 3.1 is a DSS. Use the definitions from questions 1.

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.

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.

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

• • • • • • • •

2.

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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

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.

3.

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 DSS classifications of the AIS SIGDSS. • • •

Communications-driven and group DSS (GSS) Data-driven DSS Document-driven DSS

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• • • 2.

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 modeldriven 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 database-oriented 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.

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

4.

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).

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Section 3.6 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 3.3 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 Table 3.2. These are: • • • •

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.

Describe the basic structure of DSS and its components.

As shown in Figure 3.6:

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• •

• • • •

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.

Section 3.7 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 Weblike 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.)

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3.

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

• •

• 4.

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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.

5.

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.

1

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.

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Section 3.8 Review Questions 1.

Models are classified as strategic, tactical, or operational. 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; e.g., 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; e.g., assessing the probable impact of alternative daily and weekly work schedules.

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.

3.

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.

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Section 3.9 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.

4.

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.

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Section 3.10 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.

Section 3.11 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.

• • •

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

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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 3.12 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 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.

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Section 3.13 Review Questions No review questions for this chapter ANSWERS TO QUESTIONS FOR DISCUSSION 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 • 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

2.

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

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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 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

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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 Tactical: - labor planning - budget preparation - promotion policy Operational: - weekly schedule - menu composition for a month Chemical Plant Strategic: - develop new process/technology - major expansion

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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.

12.

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.

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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 knowledgebased 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 3.12, Review Question 1. The two questions are essentially identical.

16.

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.

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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 decisionmaking 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 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.

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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 developed inventory decision systems in use at P&G. The modelers at P&G employed Monte Carlo simulations in spreadsheets to evaluate different inventory policies by analyzing the policy’s impact on the customer-service levels. These model simulations enable decision makers to identify the best inventory setting policies.

2.

What other inputs might be relevant in building inventory decision models?

Additionally, the company made an improvement using a safety factor that is automatically calculated to determine how many standard deviations of demand are kept as safety stock to ensure the target fill rate. Computation of the safety factor uses parameters such as a target fill rate, reaction times, lot size, forecast error, and type of probability distribution. Using these factors, the function uses a binary search to automatically calculate the safety factor. The system is utilized by hundreds of supply chain planners, incorporates well documented work processes, and integrates a formal release process.

3.

Would it be better for this DSS to be available as a Web-based DSS? No, because users are located all over the world and speak different languages and own various computer systems, and macros do not necessarily translate from one computer system to another very well.

4.

What lessons can you learn from studying this case?

This case demonstrates a decision support model that can be developed using commercially available tools. The case also touches upon the need for systems developers to be aware of the unique needs of a global, diversified company in terms of diversity of languages, systems in use, and so on.

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Chapter 4: Modeling and Analysis

Learning Objectives for Chapter 4 1.

2.

3.

4.

5.

6.

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 7. Describe how to structure a linear programming model 8. Understand how search methods are used to solve MSS models 9. Explain the differences among algorithms, blind search, and heuristics 10. Describe how to handle multiple goals 11. Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking 12. Describe the key issues of model management

CHAPTER OVERVIEW In this chapter, we describe the model base and its management, one of the major components of decision support systems (DSS). We present this material with a note of caution: Modeling can be a very difficult topic and is as much an art as a science. The purpose of this chapter is not necessarily for you to master the topics of modeling and analysis. Rather, the material is geared toward gaining familiarity with the important concepts as they relate to DSS and their use in decision making. It is important to recognize that the modeling we discuss here is only cursorily related to the concepts of data modeling. You should not confuse the two. We walk through some basic concepts and definitions of modeling before introducing the influence diagram (see Online File W4.1), which can aid a decision maker in sketching a model of a situation and even solving it. We next introduce the idea of modeling directly in spreadsheets. We then discuss the structure and application of some successful time-proven models and methodologies: optimization, decision analysis, decision trees, analytic hierarchy process, search methods, heuristic programming, and simulation.

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CHAPTER OUTLINE 4.1

OPENING VIGNETTE: MODEL-BASED AUCTIONS SERVE MORE LUNCHES IN CHILE A. PROBLEM B. SOLUTION C. RESULTS  Questions for the Opening Vignette E. WHAT WE CAN LEARN FROM THIS VIGNETTE

4.2.

MANAGEMENT SUPPORT SYSTEM MODELING A. LESSONS FROM MODELING AT PROCTER & GAMBLE B. LESSONS FROM ADDITIONAL MODELING APPLICATIONS ⧫ Application Case 4.1: Lockheed Martin Space Systems Company Optimizes Infrastructure Project-Portfolio Selection C. 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 Proves to be a Good Gamble for Harrah’s Cherokee Casino and Hotel 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. 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 4.3 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)

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 4.5.

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Section 4.4 Review Questions

MANAGEMENT SUPPORT SYSTEM MODELING WITH SPREADSHEETS ⧫ Application Case 4.3: Showcase Scheduling at Fred Astaire East Side Dance Studio  Section 4.5 Review Questions

4.6.

MATHEMATICAL PROGRAMMING OPTIMIZATION ⧫ Application Case 4.4: Spreadsheet Model Helps Assign Medical Residents A. MATHEMATICAL PROGRAMMING B. LINEAR PROGRAMMING C. THE LP PRODUCT-MIX MODEL FORMULATION D. MODELING IN LP: AN EXAMPLE ⧫ Technology Insights 4.5: Linear Programming  Section 4.6 Review Questions

4.7.

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 4.7 Review Questions

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

Treating Uncertainty 2.

Treating Risk B. DECISION TREES ⧫ Application Case 4.6: Decision Analysis Assists Doctor in Weighing Treatment Options for Cancer Suspects and Patients  Section 4.8 Review Questions 4.9. MULTICRITERIA DECISION MAKING WITH PAIRWISE COMPARISIONS A. THE ANSLYTIC HIERARCHY PROCESS ⧫ Application Case 4.7: Multicriteria Decision Support for European Radiation Emergency Support System  Section 4.9 Review Questions

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4.10. PROBLEM-SOLVING SEARCH METHODS A. ANALYTICAL TECHNIQUES B. ALGORITHMS C. BLIND SEARCHING D. HEURISTIC SEARCHING ⧫ Application Case 4.8: 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 4.9: Improving Maintenance Decision Making in the Finnish Air Force Through Simulation 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 4.10: 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. THE TERADATA UNIVERSITY NETWORK (TUN) CONNECTION

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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: HP Applies Management Science Modeling to Optimize its Supply Chain and Wins a Major Award  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

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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.

What decision problem does the Chilean agency face?

The decision problem is how to get a maximum number of low-cost high-quality meals from vendors who have an incentive to reduce costs and inefficiencies. To solve this problem, they use auctions.

The auction process begins when JUNAEB contacts and registers potential vendors. The agency then evaluates the companies from a managerial, technical, and financial point of view and eliminates those that don’t meet minimum reliability standards. The decision problem also includes qualifying vendors according to two characteristics: (1) their financial and operating capacity and (2) their technical and managerial competence.

2.

Would a decision process that does not use a model be able to accommodate the desired goals?

The answers will differ depending on student’s viewpoints. Students could argue that a model is needed to accommodate the desired goals, which are to maximize quality and quantity of meals and to minimize costs. A model represents the relationship (or represents a simplified relationship) of the variables in the decision situation. To determine the best solution or achieve the desired goals, the relationship among the variables has to be identified and understood.

Conversely, students might claim that models are not necessary, but that the decision process would take longer without a model and not reach the optimal choice (outcome).

3.

What other approaches could you use to make these decisions?

The agency could set cut-off points, for example, meal costs must be less than $2.00. Vendors that did not meet the cut-off points would be eliminated from consideration. Then the final decision could take into consideration another cutoff point. This approach could take longer and result in a sub-optimal decision.

4.

What other applications of this type of auction model can you envision?

Any B2B procurement (e.g., buying office supplies, or raw materials) could use (and many do use) this auction model.

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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

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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.

5.

What are the current trends in modeling?

Trends in modeling are financial and risk simulation, predictive analytics, and optimization.

Section 4.3 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 4.4 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.

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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)

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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.

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.

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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

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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 4.7 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.

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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 4.8 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

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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 4.9 Review Questions 1.

What is Analytic Hierarchy Process?

The analytic hierarchy process (AHP), is an excellent modeling structure for representing multicriteria (multiple goals, multiple objectives) problems—with sets of criteria and alternatives (choices)—commonly found in business environments..

2.

What steps are needed in applying AHP? The decision maker uses AHP to decompose a decision making problem into relevant criteria and alternatives. The AHP separates the analysis of the criteria from the alternatives, which helps the decision maker to focus on small, manageable portions of the problem

3.

What are the differences between AHP software and software such as ChoiceAnalyst?

ChoiceAnalyst takes a direct approach, using a gradient-free derivative and probability and statistics to generate a sampling using a confidence level and confidence interval (whose default is a model option) and a ranking calculation. A global minimum is iteratively found that best satisfies the decision maker and criteria weights along with the paired preference weight matrix. The number of iterations to reach convergence depends on the particular model involved.

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.

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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.

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• • • • • • • • • • • •

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 longrange 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.

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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 time-independent 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.

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

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 hand-drawn 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.

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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 QUESTIONS FOR DISCUSSION 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.

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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

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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 4.8, 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).

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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.)

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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.

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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.

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ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

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Describe the problem that a large company such as HP might face in offering many product lines and options.

The something-for-everyone approach drives sales, but at what cost? At what point does the price of designing, manufacturing, and introducing yet another new product, feature, or option exceed the additional revenue it is likely to generate?

Just as important, what are the costs associated with too much or too little inventory for such a product, not to mention additional supply chain complexity, and how does all of that impact customer satisfaction?

2.

Why is there a possible conflict between marketing and operations?

Marketing and sales always wanted more—more SKUs, more features, more configurations— and for good reason. Providing every possible product choice was considered an obvious way to satisfy more customers and generate more sales.

Supply chain managers, however, always wanted less. They wanted less to forecast, less inventory, and less complexity to manage. The drivers (on the supply chain side) were cost control.

3.

Summarize your understanding of the models and the algorithms.

1 produced an analytically driven process for evaluating new products for introduction, 2 created a tool for prioritizing existing products in a portfolio, and 3 developed an algorithm that solves the problem many times faster than previous technologies, thereby advancing the theory and practice of network optimization.

4.

Perform an online search to find more details of the algorithms.

Open-end answer and will be dependent upon the students online search results.

5.

Why would there be a need for such a system in an organization?

A very large organization requires tenacity and skill to bring about major changes in the processes of a company.

6.

What benefits did HP derive from implementation of the models?

The RCO software algorithm was developed as part of HP Labs’ “analytics” theme, which applies mathematics and scientific methodologies to help decision making and create better-run businesses. Analytics is one of eight major research themes of HP Labs, which last year refocused its efforts to address the most

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complex challenges facing technology customers in the next decade. The RCO algorithm is an example of an innovation that helps drive efficiency with businesses and our customers.

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Chapter 5: Data Mining for Business Intelligence

Learning Objectives for Chapter 5 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Describe data mining as an enabling technology for business intelligence Understand the objectives and benefits of business analytics and data mining Recognize the wide range of applications of data mining Learn the standardized data mining processes Understand the steps involved in data preprocessing for data mining Learn different methods and algorithms of data mining Build awareness of the existing data mining software tools Understand the pitfalls and myths of data mining

CHAPTER OVERVIEW This chapter is the first chapter describing data mining concepts in depth. Data mining is further described in Chapters 6 through 8. Students were briefly introduced to data mining in Chapter 1 (e.g., page 21). This chapter discusses data mining concepts, definitions, applications, processes, methods, and software tools. It excludes artificial neural networks (ANN), which are in Chapter 6. Examples of data mining myths and blunders are discussed in Section 5.7.

Data mining is a broad term. Data mining is a process, a technology, a solution and (to software vendors) a market. This chapter covers data mining from multiple points of view and describes common myths.

CHAPTER OUTLINE 5.1

OPENING VIGNETTE: DATA MINING GOES TO HOLLYWOOD! A. DATA B. METHODOLOGY C. RESULTS D. CONCLUSION  Questions for the Opening Vignette E. WHAT WE CAN LEARN FROM THIS VIGNETTE

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5.2

DATA MINING, CONCEPTS AND APPLICATIONS ⧫ Application Case 5.1: Business Analytics and Data Mining Help 1-800-Flowers Excel in Business A. DEFINITIONS, CHARACTERISTICS, AND BENEFITS ⧫ Technology Insights 5.1: Data in Data Mining 1.

Categorical data 2.

Nominal data 3.

Ordinal data 4.

Numeric data 5.

Interval data 6.

Ratio data ⧫ Application Case 5.2: Law Enforcement Organizations Use Data Mining to Better Fight Crime B. HOW DATA MINING WORKS 1.

Prediction 2.

Classification 3.

Clustering ⧫ Application Case 5.3: Motor Vehicle Accidents and Driver Distractions 4.

Associations 5.

Visualization and Time-Series Forecasting 6.

Hypothesis- or Discovery-Driven Data Mining  Section 5.2 Review Questions

5.3

DATA MINING APPLICATIONS ⧫ Application Case 5.4: A Mine on Terrorist Funding  Section 5.3 Review Questions

5.4

DATA MINING PROCESS A, Step 1: Business Understanding B. Step 2: Data Understanding C. Step 3: Data Preparation D. Step 4: Modeling Building E. Step 5: Testing and Evaluation F. Step 6: Deployment ⧫ Application Case 5.5: Data Mining in Cancer Research G. OTHER DATA MINING STANDARDIZED PROCESSES AND METHODOLOGIES  Section 5.4 Review Questions

5.5

DATA MINING METHODS A. CLASSIFICATION B. ESTIMATING THE TRUE ACCURACY OF CLASSIFICATION MODELS 1.

SIMPLE SPLIT 2.

K-FOLD CROSS-VALIDATION

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C.

D.

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3.

ADDITIONAL CLASSIFICATION ASSESSMENT METHODOLOGIES 4.

CLASSIFICATION TECHNIQUES 5.

DECISION TREES ⧫ Application Case 5.6: Highmark, Inc., Employs Data Mining to Manage Insurance Costs CLUSTER ANALYSIS FOR DATA MINING 1.

DETERMINING THE OPTIMAL NUMBER OF CLUSTERS 2.

ANALYSIS METHODS 3.

K-MEANS CLUSTERING ALGORITHM ASSOCIATION RULE MINING 1.

APRIORI ALGORITHM  Section 5.5 Review Questions

5.6

DATA MINING SOFTWARE TOOLS ⧫ Application Case 5.7: Predicting Customer Churn—A Competition of Different Tools  Section 5.6 Review Questions

5.7

DATA MINING MYTHS AND BLUNDERS  Section 5.7 Review Questions

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: Data Mining Helps Develop Custom-Tailored Product Portfolios for Telecommunication Companies  Questions for the Case References

TEACHING TIPS/ADDITIONAL INFORMATION

       

Data mining was mentioned briefly in Chapters 1, 2 and 3. Students have encountered the term several times, but will only have a general idea of what it means. By the time they finish this chapter, students will have a comprehensive understanding of key data mining topics and applications.

The order of sections in this chapter follows a logical sequence to make teaching this topic easy. After the opening vignette, Section 5.2 discusses the general concepts.

Section 5.3 covers a wide range of applications. Section 5.4 focuses on the management

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issues. Sections 5.5 describes the most popular data mining methods and explains their representative techniques. Section 5.6 covers software vendors, commercial tools, and open source tools for modeling and data visualization.

Compared to most other chapters in this book, this one contains a large number of application cases. 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.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 5.1 Questions for the Opening Vignette 1.

Why should Hollywood decision makers use data mining?

Data mining supports and improves predictability when enough is known about the situation to identify the predictors (independent variables) and to build a model. Data mining can improve the accuracy of predicting box-office receipts, which are critical to their financial success. With data mining, decisions are based on data-driven forecasting models and a classification model rather than on hunches and wild guesses. Importantly, predictive models are effective in early stages of movie production before huge investments have been made. Of course, minimizing investments in flops improves profitability.

2.

What are the top challenges for Hollywood managers? Can you think of other industry segments that face similar problems?

Hollywood managers have to allocate their scarce resources (budget, actors, facilities, directors, etc.) to get the highest returns on their investments. Movies are capital investments for Hollywood. They invest in movies for the same reason that other types of companies (manufacturers, retailers, service sector, entertainment, financial) make investments--to maximize return on investment (ROI). The top challenges facing all of these industry sectors are to identify which investments and which combination of investments will maximize ROI at a particular time; and which variables (predictors) to consider in evaluating alternatives.

3.

Do you think the researchers used all of the relevant data to build prediction models?

Students’ opinions will differ depending on their view of what is meant by “relevant.” Most students should recognize that all or almost all relevant data could be collected and analyzed given the capabilities of data mining tools, but that such a data collection effort could be too time-consuming and expensive given that all relevant data is not necessary to develop a reliable model. For students who are very literal, they might legitimately claim that it is not possible to know whether all relevant data had been used. It is important that students support their opinion by explaining the basis for it.

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Why do you think the researchers chose to convert a regression problem into a classification problem?

Students may have trouble with this question until after they have read the chapter. However, they read in the case that rather than forecasting the point estimate of box-office receipts, the researchers classified a movie based on its box-office receipts in one of nine categories. Those categories ranged from “flop” to “blockbuster,” because greater precision might not add improve the outcome or might not be possible. The use of categories not only simplified the decision situation, but also might be as precise as feasible. Regression provides a pointestimate which would have less reliability than broader classification categories.

Regression also requires much more data to achieve reliability than a classification problem.

Blockbuster movies (and flops) have unique factors that may not be captured in a regression model, but their common characteristics may be sufficient to predict their general degree of success.

5.

How do you think these prediction models can be used? Can you think of a good production system for such models?

The prediction models can be used to select the combination of independent variables, e.g., MPAA rating, competition, actors (star value), genre, special effects, sequel, number of screens, and current tastes.

Decision makers can use such models to evaluate tradeoffs and to determine how much to invest in the production. Since each variable impacts the cost of movie production (movies have budgets), these prediction models can be used to determine which tradeoffs to make to maximize success.

6.

Do you think the decision makers would easily adapt to such an information system?

Again, students’ answers will differ since both answers--yes or no--are feasible.

Some students may think the decision makes would not easily adapt because movie production is more of an art than a science. Other students may think that given the increased competition in the entertainment industry, decision makers need to adapt to a more scientific method.

7.

What can be done to further improve the prediction models explained in this case?

Prediction models can be improved by updating the models as new information (movies) becomes available. Narrower models can be built depending on the type of movie. For example, each type of movie (drama, sci-fi, animation, etc.) may have its own prediction model. As researchers develop a better understanding of what leads to “movie success,” more specialized and sophisticated models can be built.

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Section 5.2 Review Questions 1.

Define data mining. Why are there many different names and definitions for data mining?

Data mining is the process through which previously unknown patterns in data were discovered. Another 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. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models.

Data mining has many definitions because it’s been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales using the popularity of data mining.

2.

What recent factors have increased the popularity of data mining?

Following are some of most pronounced reasons: • More intense competition at the global scale driven by customers’ ever-changing needs and wants in an increasingly saturated marketplace.

• General recognition of the untapped value hidden in large data sources.

• Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc.

• Consolidation of databases and other data repositories into a single location in the form of a data warehouse.

• The exponential increase in data processing and storage technologies.

• Significant reduction in the cost of hardware and software for data storage and processing.

• Movement toward the de-massification (conversion of information resources into nonphysical form) of business practices.

3.

Is data mining a new discipline? Explain.

Although the term data mining is relatively new, the ideas behind it are not.

Many of the techniques used in data mining have their roots in traditional statistical analysis and artificial intelligence work done since the early part of 1980s. New or increased use of data mining applications makes it seem like data mining is a new discipline.

In general, data mining seeks to identify four major types of patterns: Associations, Predictions, Clusters and Sequential relationships. These types of patterns have been manually extracted from data by humans for centuries, but the increasing volume of data in modern times has created a need for more automatic approaches. As datasets have grown in size and complexity, direct manual data analysis has increasingly been augmented with indirect, automatic data processing tools that use sophisticated methodologies, methods, and algorithms. The manifestation of such evolution of automated and semiautomated means of processing large datasets is now commonly referred to as data mining.

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What are some major data mining methods and algorithms?

Generally speaking, data mining tasks can be classified into three main categories: prediction, association, and clustering. Based on the way in which the patterns are extracted from the historical data, the learning algorithms of data mining methods can be classified as either supervised or unsupervised. With supervised learning algorithms, the training data includes both the descriptive attributes (i.e., independent variables or decision variables) as well as the class attribute (i.e., output variable or result variable). In contrast, with unsupervised learning the training data includes only the descriptive attributes. Figure 5.4 (p.

201) shows a simple taxonomy for data mining tasks, along with the learning methods, and popular algorithms for each of the data mining tasks.

5.

What are the key differences between the major data mining methods?

Prediction is the act of telling about the future. It differs from simple guessing by taking into account the experiences, opinions, and other relevant information in conducting the task of foretelling. A term that is commonly associated with prediction is forecasting. Even though many believe that these two terms are

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synonymous, there is a subtle but critical difference between the two. Whereas prediction is largely experience and opinion based, forecasting is data and model based. That is, in order of increasing reliability, one might list the relevant terms as guessing, predicting, and forecasting, respectively. In data mining terminology, prediction and forecasting are used synonymously, and the term prediction is used as the common representation of the act.

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.

Section 5.3 Review Questions 1.

What are the major application areas for data mining?

Applications are listed near the beginning of this section (pp. 204-206): CRM, banking, retailing and logistics, manufacturing and production, brokerage, insurance, computer hardware and software, government, travel, healthcare, medicine, entertainment, homeland security, and sports.

2.

Identify at least five specific applications of data mining and list five common characteristics of these applications.

This question expands on the prior question by asking for common characteristics.

Several such applications and their characteristics are listed on (pp. 204-206): CRM, banking, retailing and logistics, manufacturing and production, brokerage, insurance, computer hardware and software, government, travel, healthcare, medicine, entertainment, homeland security, and sports.

3.

What do you think is the most prominent application area for data mining? Why? Students’ answers will differ depending on which of the applications (most likely banking, retailing and logistics, manufacturing and production, government, healthcare, medicine, or homeland security) they think is most in need of greater certainty. Their reasons for selection should relate to the application area’s need for better certainty and the ability to pay for the investments in data mining.

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Can you think of other application areas for data mining not discussed in this section? Explain.

Students should be able to identify an area that can benefit from greater prediction or certainty. Answers will vary depending on their creativity.

Section 5.4 Review Questions 1.

What are the major data mining processes?

Similar to other information systems initiatives, a data mining project must follow a systematic project management process to be successful. Several data mining processes have been proposed: CRISP-DM, SEMMA, and KDD.

2.

Why do you think the early phases (understanding of the business and understanding of the data) take the longest in data mining projects?

Students should explain that the early steps are the most unstructured phases because they involve learning. Those phases (learning/understanding) cannot be automated. Extra time and effort are needed upfront because any mistake in understanding the business or data will most likely result in a failed BI project.

3.

List and briefly define the phases in the CRISP-DM process.

CRISP-DM provides a systematic and orderly way to conduct data mining projects. 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.

4.

What are the main data preprocessing steps? Briefly describe each step and provide relevant examples.

Data preprocessing is essential to any successful data mining study. Good data leads to good information; good information leads to good decisions. Data preprocessing includes four main steps (listed in Table 5.4 on page 211): data consolidation: access, collect, select and filter data data cleaning: handle missing data, reduce noise, fix errors data transformation: normalize the data, aggregate data, construct new attributes data reduction: reduce number of attributes and records; balance skewed data

5.

How does CRISP-DM differ from SEMMA? The main difference between CRISP-DM and SEMMA is that CRISP-DM takes a more comprehensive approach—including understanding of the business and the relevant data—to data mining projects, whereas SEMMA implicitly assumes that the data mining project’s goals and objectives along with the appropriate data sources have been identified and understood.

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Section 5.5 Review Questions 6.

Identify at least three of the main data mining methods.

Classification learns patterns from past data (a set of information—traits, variables, features—on characteristics of the previously labeled items, objects, or events) in order to place new instances (with unknown labels) into their respective groups or classes. The objective of classification is to analyze the historical data stored in a database and automatically generate a model that can predict future behavior.

Cluster analysis is an exploratory data analysis tool for solving classification problems. The objective is to sort cases (e.g., people, things, events) into groups, or clusters, so that the degree of association is strong among members of the same cluster and weak among members of different clusters.

Association rule mining is a popular data mining method that is commonly used as an example to explain what data mining is and what it can do to a technologically less savvy audience. Association rule mining aims to find interesting relationships (affinities) between variables (items) in large databases.

7.

Give examples of situations in which classification would be an appropriate data mining technique. Give examples of situations in which regression would be an appropriate data mining technique.

Students’ answers will differ, but should be based on the following issues.

Classification is for prediction that can be based on historical data and relationships, such as predicting the weather, product demand, or a student’s success in a university. If what is being predicted is a class label (e.g., “sunny,” “rainy,” or “cloudy”) the prediction problem is called a classification, whereas if it is a numeric value (e.g., temperature such as 68°F), the prediction problem is called a regression.

8.

List and briefly define at least two classification techniques.

• Decision tree analysis. Decision tree analysis (a machine-learning technique) is arguably the most popular classification technique in the data mining arena.

• Statistical analysis. Statistical classification techniques include logistic regression and discriminant analysis, both of which make the assumptions that the relationships between the input and output variables are linear in nature, the data is normally distributed, and the variables are not correlated and are independent of each other.

• Case-based reasoning. This approach uses historical cases to recognize commonalities in order to assign a new case into the most probable category.

• Bayesian classifiers. This approach uses probability theory to build classification models based on the past occurrences that are capable of placing a new instance into a most probable class (or category).

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• Genetic algorithms. The use of the analogy of natural evolution to build directed search-based mechanisms to classify data samples.

• Rough sets. This method takes into account the partial membership of class labels to predefined categories in building models (collection of rules) for classification problems.

9.

What are some of the criteria for comparing and selecting the best classification technique?

• The amount and availability of historical data.

• The types of data, categorical, interval, ration, etc.

• What is being predicted -- class or numeric value • The purpose or objective

10.

Briefly describe the general algorithm used in decision trees.

A general algorithm for building a decision tree is as follows: 1. Create a root node and assign all of the training data to it.

2. Select the best splitting attribute.

3. Add a branch to the root node for each value of the split. Split the data into mutually exclusive (nonoverlapping) subsets along the lines of the specific split and mode to the branches.

4. Repeat the steps 2 and 3 for each and every leaf node until the stopping criteria is reached (e.g., the node is dominated by a single class label).

11.

Define Gini index. What does it measure?

The Gini index and information gain (entropy) are two popular ways to determine branching choices in a decision tree.

The Gini index measures the purity of a sample. If everything in a sample belongs to one class, the Gini index value is zero.

12.

Give examples of situations in which cluster analysis would be an appropriate data mining technique.

Cluster algorithms are used when the data records do not have predefined class identifiers (i.e., it is not known to what class a particular record belongs).

13.

What is the major difference between cluster analysis and classification?

Classification methods learn from previous examples containing inputs and the resulting class labels, and once properly trained they are able to classify future cases. Clustering partitions pattern records into natural segments or clusters.

14.

What are some of the methods for cluster analysis?

The most commonly used clustering algorithms are k-means and selforganizing maps.

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Give examples of situations in which association would be an appropriate data mining technique.

Association rule mining is appropriate to use when the objective is to discover two or more items (or events or concepts) that go together. Students’ answers will differ.

Section 5.6 Review Questions 1. What are the most popular commercial data mining tools?

Examples of these vendors include SPSS (PASW Modeler), SAS (Enterprise Miner), StatSoft (Statistica Data Miner), Salford (CART, MARS, TreeNet, RandomForest), Angoss (KnowledgeSTUDIO, KnowledgeSeeker), and Megaputer (PolyAnalyst). Most of the more popular tools are developed by the largest statistical software companies (SPSS, SAS, and StatSoft).

2. Why do you think the most popular tools are developed by statistics companies?

Data mining techniques involved the use of statistical analysis and modeling. So it’s a natural extension of their business offerings.

3. What are the most popular free data mining tools?

Probably the most popular free and open source data mining tool is Weka. Others include RapidMiner, and Microsoft’s SQL Server.

4. What are the main differences between commercial and free data mining software tools?

The main difference between commercial tools, such as Enterprise Miner, PASW, and Statistica, and free tools, such as Weka and RapidMiner, is computational efficiency. The same data mining task involving a rather large dataset may take a whole lot longer to complete with the free software, and in some cases it may not even be feasible (i.e., crashing due to the inefficient use of computer memory).

5. What would be your top five selection criteria for a data mining tool? Explain.

Students’ answers will differ. Criteria they are likely to mention include cost, user-interface, ease-of-use, computational efficiency, hardware compatibility, type of business problem, vendor support, and vendor reputation.

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Section 5.7 Review Questions 1. What are some of the most common myths about data mining?

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.

2.

What do you think are the reasons for these myths about data mining?

Students’ answers will differ. Some answers might relate to fear of analytics, fear of the unknown, or fear of looking dumb.

3.

What are the most common data mining mistakes? How can they be minimized and/or eliminated?

1.

2.

Selecting the wrong problem for data mining.

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.

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.

8.

Believing everything you are told about data.

9.

Believing everything you are told about your own data mining analysis.

10.

Measuring your results differently from the way your sponsor measures them.

Ways to minimize these risks are basically the reverse of these items.

ANSWERS TO QUESTIONS FOR DISCUSSION

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1. Define data mining. Why are there many names and definitions for data mining?

Data mining is the process through which previously unknown patterns in data were discovered. Another 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. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models.

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Data mining has many definitions because it’s been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales using the popularity of data mining.

2. What are the main reasons for the recent popularity of data mining?

Following are some of most pronounced reasons: • More intense competition at the global scale driven by customers’ ever-changing needs and wants in an increasingly saturated marketplace.

• General recognition of the untapped value hidden in large data sources.

• Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc.

• Consolidation of databases and other data repositories into a single location in the form of a data warehouse.

• The exponential increase in data processing and storage technologies.

• Significant reduction in the cost of hardware and software for data storage and processing.

• Movement toward the de-massification (conversion of information resources into nonphysical form) of business practices.

3. Discuss what an organization should consider before making a decision to purchase data mining software.

Technically speaking, data mining is a process that uses statistical, mathematical, and artificial intelligence techniques to extract and identify useful information and subsequent knowledge (or patterns) from large sets of data. Before making a decision to purchase data mining software organizations should consider the standard criteria to use when investing in any major software: cost/benefit analysis, people with the expertise to use the software and perform the analyses, availability of historical data, a business need for the data mining software.

4. Distinguish data mining from other analytical tools and techniques.

Students can view the answer in Figure 5.2 (p. 197) which shows that data mining is a composite or blend of multiple disciplines or analytical tools and techniques.

5. Discuss the main data mining methods. What are the fundamental differences among them?

Prediction is the act of telling about the future. It differs from simple guessing by taking into account the experiences, opinions, and other relevant information in conducting the task of foretelling. A term that is commonly associated with prediction is forecasting. Even though many believe that these two terms are synonymous, there is a subtle but critical difference between the two. Whereas prediction is largely experience and opinion based, forecasting is data and

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model based. That is, in order of increasing reliability, one might list the relevant terms as guessing, predicting, and forecasting, respectively. In data mining terminology, prediction and forecasting are used synonymously, and the term prediction is used as the common representation of the act.

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.

6. What are the main data mining application areas? Discuss the commonalities of these areas that make them a prospect for data mining studies.

Applications are listed near the beginning of this section (pp. 204-206): CRM, banking, retailing and logistics, manufacturing and production, brokerage, insurance, computer hardware and software, government, travel, healthcare, medicine, entertainment, homeland security, and sports.

The commonalities are the need for predictions and forecasting for planning purposes and to support decision making.

7. Why do we need a standardized data mining process? What are the most commonly used data mining processes?

In order to systematically carry out data mining projects, a general process is usually followed. Similar to other information systems initiatives, a data mining project must follow a systematic project management process to be successful.

Several data mining processes have been proposed: CRISP-DM, SEMMA, and KDD. 8. Discuss the differences between the two most commonly used data mining process.

The main difference between CRISP-DM and SEMMA is that CRISP-DM takes a more comprehensive approach—including understanding of the business and the relevant data—to data mining projects, whereas SEMMA implicitly assumes that the data mining project’s goals and objectives along with the appropriate data sources have been identified and understood.

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9. Are data mining processes a mere sequential set of activities?

Even though these steps are sequential in nature, there is usually a great deal of backtracking. Because the data mining is driven by experience and experimentation, depending on the problem situation and the knowledge/experience of the analyst, the whole process can be very iterative (i.e., one should expect to go back and forth through the steps quite a few times) and time consuming. Because latter steps are built on the outcome of the former ones, one should pay extra attention to the earlier steps in order not to put the whole study on an incorrect path from the onset.

10. Why do we need data preprocessing? What are the main tasks and relevant techniques used in data preprocessing?

Data preprocessing is essential to any successful data mining study. Good data leads to good information; good information leads to good decisions. Data preprocessing includes four main steps (listed in Table 5.4 on page 211): data consolidation: access, collect, select and filter data data cleaning: handle missing data, reduce noise, fix errors data transformation: normalize the data, aggregate data, construct new attributes data reduction: reduce number of attributes and records; balance skewed data 11. Discuss the reasoning behind the assessment of classification models.

The model-building step also encompasses the assessment and comparative analysis of the various models built. Because there is not a universally known best method or algorithm for a data mining task, one should use a variety of viable model types along with a well-defined experimentation and assessment strategy to identify the “best” method for a given purpose.

12. What is the main difference between classification and clustering? Explain using concrete examples.

Classification learns patterns from past data (a set of information—traits, variables, features—on characteristics of the previously labeled items, objects, or events) in order to place new instances (with unknown labels) into their respective groups or classes. The objective of classification is to analyze the historical data stored in a database and automatically generate a model that can predict future behavior. Classifying customer-types as likely to buy or not buy is an example.

Cluster analysis is an exploratory data analysis tool for solving classification problems. The objective is to sort cases (e.g., people, things, events) into groups, or clusters, so that the degree of association is strong among members of the same cluster and weak among members of different clusters. Customers can be grouped according to demographics.

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13. . Moving beyond the chapter discussion, where else can association be used?

Students’ answers will vary.

14. What are the most common myths and mistakes about data mining?

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.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS 1.

 

Why do you think that consulting companies are more likely to use data mining tools and techniques? What specific value proposition do they offer?

Consulting companies use data mining tools and techniques because the results are valuable to their clients. Consulting companies can develop data mining expertise and invest in the hardware and software and then earn a return on those investments by selling those services. Data mining can lead to insights that provide a competitive advantage to their clients.

2.

Why was it important for argonauten360° to employ a comprehensive tool that has all modeling capabilities?

In order to offer a comprehensive set of intelligence services, the company needed a comprehensive tool -- or else their analysts needed to learn many different tools.

After 12 months of evaluating a wide range of data mining tools, the company chose Statistica Data Miner because it provided the ideal combination of features to satisfy most every analyst’s needs and requirements with user-friendly interfaces.

3.

What was the problem that argonauten360° helped solve for a call-by-call provider?

It is a very competitive business, and the success of the call-by-call telecommunications provider depends greatly on attractive per-minute calling rates.

Rankings of those rates are widely published, and the key is to be ranked somewhere in the top-five lowest-cost providers while maintaining the best possible margins.

4.

Can you think of other problems for telecommunication companies that are likely to be solved with data mining?

Predicting customer churn (lost customers) Predicting demand for capacity Predicting the volume of calls for customer service based on time of day.

Predicting demographic shifts

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Chapter 6: Artificial Neural Networks for Data Mining Learning Objectives for Chapter 6 1. Understand the concept and different types of artificial neural networks (ANN) 2. Know the similarities and differences between biological and artificial neutral networks 3. Learn the different types of neutral network architectures 4. Learn the advantages and limitations of ANN 5. Understand how backpropagation neural networks learn 6. Understand the complete process of using neural networks 7. Appreciate the wide variety of applications of neural networks

CHAPTER OVERVIEW Neural networks have emerged as advanced data mining tools in cases where other techniques may not produce satisfactory predictive models. As the term implies, neural networks have a biologically inspired modeling capability but are essentially statistical modeling tools. In this chapter, we study the basics of neural networks, different types of neural network architectures, some specific applications, and the process of implementing a neural network project.

CHAPTER OUTLINE 6.1

OPENING VIGNETTE: PREDICTING GAMBLING REFERENDA WITH NEURAL NETWORKS A. DATA B. SOLUTION C. RESULTS  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

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6.2

BASIC CONCEPTS OF NEURAL NETWORKS A. BIOLOGICAL AND ARTIFICIAL NEURAL NETWORKS ⧫ Technology Insights 6.1: The Relationship Between Biological and Artificial Neural Networks ⧫ Application Case 6.2: 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 6.2 Review Questions

6.3

LEARNING IN ANN ⧫ Application Case 6.3: 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 6.3 Review Questions

6.4

DEVELOPING NEURAL NETWORK-BASED SYSTEMS A. DATA COLLECTION AND PREPARATION B. SELECTION OF NETWORK STRUCTURE C. LEARNING ALGORITHM SELECTION D. NETWORK TRAINING ⧫ Technology Insights 6.4: ANN Software E. TESTING F. IMPLEMENTATION OF AN ANN  Section 6.4 Review Questions

6.5

ILLUMINATING THE BLACK BOX OF ANN WITH SENSITIVITY ANALYSIS ⧫ Application Case 6.5: Sensitivity Analysis Reveals Injury Severity factors in Traffic Accidents  Section 6.5 Review Questions

6.6

A SAMPLE NEURAL NETWORK PROJECT  Section 6.6 Review Questions

6.7

OTHER POPULAR NEURAL NETWORK PARADIGMS A. B.

KOHONEN’S SELF-ORGANIZING FEATURE MAPS SOM NETWORK ARCHITECTURE

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C. D.

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THE SOM LEARNING ALGORITHM 1. Applications of SOM HOPFIELD NETWORKS  Section 6.7 Review Questions

APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS ⧫ Application Case 6.6: Neural Networks for Breast Cancer Diagnosis  Section 6.8 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: Coors Improves Beer Flavors with 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 6.7 without impacting what students get from the rest of the chapter.)

Regarding that vignette: Students may note that the chapter title is “Artificial 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 6.2 (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

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may seem obvious to an instructor, but is often not equally obvious to students seeing the concept for the first time.

In Section 6.3, it is important for students to understand the simple learning example on pp. 256-257. The material in the book is not enough for them to learn on their own.

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.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 6.1 Review Questions 1. Why is it important to study public opinion toward legalized gambling?

Although many consider legalized gambling to be vital to the regeneration and revitalization of inner cities and economically depressed areas, attempts to legalize gambling have generally been met with caution or resistance. The lack of support for legalization of gambling originates from both perceived and actual ethical concerns.

2. What factors might be used to predict public opinion toward gaming/gambling activities?

• Ballot Type I • Percent population voted • Medium family income • Percent population church members • Percent population male • Poverty level • Unemployment rate • Percent population minority—non-White percentage • Percent population older than 45 • Metropolitan statistical area Can you think of factors that are not mentioned in this case study?

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Marital status proposed location of gaming facility, access to public transportation, and other tourist attractions.

3. What are the potential benefits and shortcomings of gaming/gambling for a county?

Proponents of legalized gambling argue that the expansion of gambling-tourism can create significant positive long-term sociocultural (e.g., better living conditions, more leisure opportunities, stronger cultural identity, etc.) and economic benefits (improved job opportunities, more disposable income, increased tax revenue, and more) for many communities.

Opponents of gambling argue that such activities defy religious beliefs and work ethics; invite political corruption, swindling, money laundering and organized crime; erode traditional family and societal values and responsibilities; and instigate irresponsible behavior. In addition, gambling may produce negative fiscal externalities, such as increased state expenditures on public welfare.

4. Why do you think ANN excels in analyzing this type of social choice problem?

The vignette shows that if ample effort is put forth to identify the potential factors there is very little (if anything) that cannot be predicted and analyzed with data mining techniques in general and neural networks in particular.

5. What were the outcomes of the study? Who can use these results? How can the results be used?

Policy makers and the gaming industry could use the findings of this study (and/or similar studies) to predict which communities will pass a gambling initiative and which will strongly oppose it. The factors identified can be used as predictors for targeting those communities with high acceptance probabilities so as to effectively utilize resources to promote gambling and to avoid potential conflicts that may arise between the gaming industry and communities.

6. Search the Internet to locate two additional cases that use ANN to predict public opinion.

Open-ended question, answer will be determined by the search results each student selects.

Section 6.2 Review Questions 1.

What is an ANN? An ANN (artificial neural network) is a computer program that models a biological neural network to learn to recognize patterns, and then recognize them in new data.

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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.

3.

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.

4.

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.

5.

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 6.3 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 6.2 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

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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 and momentum?

The purpose of the learning rate is to control the rate at which output errors can change the weights in a 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.

Most implementations of the learning process also include a counterbalancing parameter called momentum to balance the learning rate. Essentially, whereas the purpose of the learning rate is to correct for the error, momentum is aimed at slowing the learning process. Many of the software programs available for neural networks can automatically select these parameters for the user or let the user experiment with many different combinations of such parameters.

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.

5.

Search the Internet to identify other learning algorithms for feed forward neural networks.

Open-ended answer, depending on what a student finds.

Section 6.4 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

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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.

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Section 6.5 Review Questions 1. What is the so-called “black-box” syndrome?

ANN are typically thought of as black boxes, capable of solving complex problems but lacking the explanation of its capabilities. This phenomenon is commonly referred to as the “black-box” syndrome.

2. Why is it important to be able to explain an ANN’s model structure?

It is important to be able to explain a model’s “inner being”; such an explanation offers assurance that the network has been properly trained and will behave as desired once deployed in a business intelligence environment. Such a need to “look under the hood” might be attributable to a relatively small training set (as a result of the high cost of data acquisition) or a very high liability in case of a system error.

3. How does sensitivity analysis work?

Sensitivity analysis is a method for extracting the cause-and-effect relationships among the inputs and the outputs of a trained neural network model. In the process of performing sensitivity analysis, the trained neural network’s learning capability is disabled so that the network weights are not affected.

4. Search the Internet to find other ANN explanation methods.

Open-ended answer, depending on what a student finds.

Section 6.6 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

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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 7.7 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.

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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 6.8 Review Questions 1.

List some applications of neural networks in accounting and 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 sports applications of neural networks?

In a recent study, Loeffelholz et al. (2009) examined the use of neural networks as a tool for predicting the success of basketball teams in the National Basketball Association (NBA).

Iyer and Sharda (2009) explored the use of neural networks to rate and select the best combination cricket players for a competition.

3.

How have neural networks been used in the health care field?

Tumor classification is cited in the text (in Application Case 6.6). 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.

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

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.)

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.

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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 “selffulfilling 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 noncreditworthiness 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.

1

It is possible that someone has figured them out but is, for understandable reasons, keeping quiet.

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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.

 

Why is beer flavor important to the Coors profitability?

Beer flavor is a key factor in customer preferences and buying behavior.

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 measured 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.

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,

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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.

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Chapter 7: Text and Web Mining

Learning Objectives for Chapter 7 ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫ ⧫

Describe text mining and understand the need for text mining Differentiate between text mining and data mining Understand the different application areas for text mining Know the process of carrying out a text mining project Understand the different methods to introduce structure to text-based data Describe Web mining, its objectives, and its benefits Understand the three different branches of Web mining Understand Web content mining, Web structure mining, and Web log mining

CHAPTER OVERVIEW This chapter provides a rather comprehensive overview of text mining and Web mining as they relate to business intelligence and decision support systems. Both Web mining and text mining are essentially the derivatives of data mining. Because text data and Web traffic data are increasing in volume an order of magnitude more than the data in structured databases, it is important to know some of the techniques used to process large amounts of unstructured data.

CHAPTER OUTLINE 7.1

OPENING VIGNETTE: Mining Text for Security and Counterterrorism  Questions for the Opening Vignette A. WHAT WE CAN LEARN FROM THIS VIGNETTE

7.2

TEXT MINING CONCEPTS AND DEFINITIONS ⧫ Technology Insights 7.1: Text Mining Lingo ⧫ Application Case 7.1: Text Mining for Patent Analysis  Section 7.2 Review Questions

7.3

NATURAL LANGUAGE PROCESSING ⧫ Application Case 7.2: Text Mining Helps Merck to Better Understand and Serve Its Customers  Section 7.3 Review Questions

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7.4

TEXT MINING APPLICATIONS A. MARKETING APPLICATIONS B. SECURITY APPLICATIONS ⧫ Application Case 7.3: Mining for Lies C. BIOMEDICAL APPLICATIONS D. ACADEMIC APPLICATIONS ⧫ Application Case 7.4: Flying Through Text  Section 7.4 Review Questions

7.5

TEXT MINING PROCESS A. TASK 1: ESTABLISH THE CORPUS B. TASK 2: CREATE THE TERM–DOCUMENT MATRIX 1.

2.

C.

REPRESENTING THE INDICES REDUCING THE DIMENSIONALITY OF THE MATRIX

TASK 3: EXTRACT THE KNOWLEDGE 1.

2.

3.

4.

CLASSIFICATION CLUSTERING ASSOCIATION TREND ANALYSIS

⧫

Application Case 7.5: Research Literature Survey with Text



Section 7.5 Review Questions

Mining

7.6

TEXT MINING TOOLS A. TASK 1: COMMERCIAL SOFTWARE TOOLS B. TASK 1: FREE SOFTWARE TOOLS  Section 7.6 Review Questions

7.7

WEB MINING OVERVIEW  Section 7.7 Review Questions

7.8

WEB CONTENT MINING AND WEB STRUCTURE MINING ⧫ Application Case 7.6: Caught in a Web  Section 7.8 Review Questions

7.9

WEB USAGE MINING  Section 7.9 Review Questions

7.10

WEB MINING SUCCESS STORIES ⧫ Application Case 7.7: Web Site Optimization Ecosystem  Section 7.10 Review Questions Chapter Highlights Key Terms Questions for Discussion Exercises Teradata Student Network (TSN) and Other Hands-On Exercises Team Assignments and Role-Playing

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Internet Exercises ⧫  References

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End of Chapter Application Case: HP and Text Mining Questions for the Case

TEACHING TIPS/ADDITIONAL INFORMATION

       

Text and Web mining are topics that students find especially interesting and (relatively) easier than data mining. They are able to relate to text mining applications. For example, text mining tools have been part of national intelligence initiatives such as the information awareness program for decades.

The opening vignette discusses DARPA and MITRE, which had teamed up to develop capabilities to automatically filter text-based information sources to generate actionable information in a timely manner. Students can come up with examples of value-added text mining applications. This will be a good chapter to engage students in class discussions.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

     

Section 7.1 Review Questions 1. How can text mining be used in a crisis situation?

In a crisis situation, a timely and correct response based on all available information is critical. Text mining reduces response time and increases the probability of taking the best action given the circumstances.

Text mining discovery tools can be used to mine relevant information sources quickly to detect patterns in the form of actionable information. Tools can make it easy for the end user to take the knowledge discovered by the analytics tools and embed it in a concise and useful form in an intelligence product.

2. What is Genoa project? What is the motivation behind projects like Genoa?

The Genoa1 project is part of DARPA’s (Defense Advanced Research Projects Agency) total information awareness program. It provides advanced tools and techniques to rapidly analyze information related to a current situation to support better decision making. Specifically, Genoa provides knowledge discovery tools to better “mine” relevant information sources for discovery of patterns. The motivation for such projects is to capture and filter information quickly and easily.

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3. What is TopCat? What does TopCat do?

TopCat is a system developed by MITRE that identifies different topics in a collection of documents and displays the key “players” for each topic. TopCat uses association rule mining technology to identify relationships among people, organizations, locations, and events. Grouping these relationships creates topic clusters (such as the three shown in Figure 7.1), which are built from 6 months of global news from several print, radio, and video sources—over 60,000 news stories in all.

This tool enables an analyst to discover, say, an association between people involved in a bombing incident, such as “McVeigh and Nichols belong to a common organization!” which gives a starting point for further analysis.

This, in turn, can lead to new knowledge that can be leveraged in the analytical model to help predict whether a particular terrorist organization is likely to strike elsewhere in the next few days.

4. What is a summarization filter?

The summarization filter, developed with DARPA funding, identifies and aggregates descriptions of people from a collection of documents by means of an efficient syntactic analysis, the use of a thesaurus, and some simple natural language processing techniques. It also extracts from these documents salient sentences related to these people by weighting sentences based on the presence of the names of people as well as the location and proximity of terms in a document, their frequency, and their correlations with other terms in the document collection.

5. Comment on the future of text mining tools for counterterrorism.

Terrorists plan their activities via the Internet, which is referred to as chatter.

Chatter is continuously monitored and extracted for analysis. Text mining plays a crucial role in counterterrorism because of the need to analyze and filter huge volumes of data and identify plans before they are carried out.

Section 7.2 Review Questions 1. What is text mining? How does it differ from data mining?

Text mining is the application of data mining to unstructured, or less structured, text files. As the names indicate, text mining analyzes words; and data mining analyzes numeric data.

2. Why is the popularity of text mining as a BI tool increasing?

The information age is characterized by rapid growth in the amount of data and information collected, stored, and made available in electronic media. Text

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mining as a BI is increasing because of the rapid growth in text data and availability of sophisticated BI tools.

The benefits of text mining are obvious in the areas where very large amounts of textual data are being generated, such as law (court orders), academic research (research articles), finance (quarterly reports), medicine (discharge summaries), biology (molecular interactions), technology (patent files), and marketing (customer comments).

3. What are some popular application areas of text mining?

• Information extraction. Identification of key phrases and relationships within

text by looking for predefined sequences in text via pattern matching.

• Topic tracking. Based on a user profile and documents that a user views, text mining can predict other documents of interest to the user.

• Summarization. Summarizing a document to save time on the part of the reader.

• Categorization. Identifying the main themes of a document and then placing the document into a predefined set of categories based on those themes.

• Clustering. Grouping similar documents without having a predefined set of categories.

• Concept linking. Connects related documents by identifying their shared concepts and, by doing so, helps users find information that they perhaps would not have found using traditional search methods.

• Question answering. Finding the best answer to a given question through knowledge-driven pattern matching.

Section 7.3 Review Questions 1. What is natural language processing?

Natural language processing (NLP) is an important component of text mining and is a subfield of artificial intelligence and computational linguistics. It studies the problem of “understanding” the natural human language, with the view of converting depictions of human language (such as textual documents) into more formal representations (in the form of numeric and symbolic data) that are easier for computer programs to manipulate.

2. How does NLP relate to text mining?

Text mining uses natural language processing to induce structure into the text collection and then uses data mining algorithms such as classification, clustering, association, and sequence discovery to extract knowledge from it.

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3. What are some of the benefits and challenges of NLP? NLP moves beyond syntax-driven text manipulation (which is often called “word counting”) to a true understanding and processing of natural language that considers grammatical and semantic constraints as well as the context.

•

• • •

• •

Part-of-speech tagging. It is difficult to mark up terms in a text as corresponding to a particular part of speech because the part of speech depends not only on the definition of the term but also on the context within which it is used.

Text segmentation. Some written languages, such as Chinese, Japanese, and Thai, do not have single-word boundaries.

Word sense disambiguation. Many words have more than one meaning.

Selecting the meaning that makes the most sense can only be accomplished by taking into account the context within which the word is used.

Syntactic ambiguity. The grammar for natural languages is ambiguous; that is, multiple possible sentence structures often need to be considered. Choosing the most appropriate structure usually requires a fusion of semantic and contextual information.

Imperfect or irregular input. Foreign or regional accents and vocal impediments in speech and typographical or grammatical errors in texts make the processing of the language an even more difficult task.

Speech acts. A sentence can often be considered an action by the speaker. The sentence structure alone may not contain enough information to define this action.

4. What are the most common tasks addressed by NLP? Following are among the most popular tasks: • Information retrieval.

• Information extraction.

• Named-entity recognition.

• Question answering.

• Automatic summarization.

• Natural language generation.

• Natural language understanding.

• Machine translation.

• Foreign language writing.

• Speech recognition.

• Text-to-speech.

• Text proofing.

• Optical character recognition.

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Section 7.4 Review Questions 1. List and briefly discuss some of the text mining applications in marketing.

Text mining can be used to increase cross-selling and up-selling by analyzing the unstructured data generated by call centers.

Text mining has become invaluable for customer relationship management.

Companies can use text mining to analyze rich sets of unstructured text data, combined with the relevant structured data extracted from organizational databases, to predict customer perceptions and subsequent purchasing behavior.

2. How can text mining be used in security and counterterrorism?

Students may use the introductory case in this answer.

In 2007, EUROPOL developed an integrated system capable of accessing, storing, and analyzing vast amounts of structured and unstructured data sources in order to track transnational organized crime.

Another security-related application of text mining is in the area of deception detection.

3. What are some promising text mining applications in biomedicine?

As in any other experimental approach, it is necessary to analyze this vast amount of data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research.

Section 7.5 Review Questions 1. What are the main steps in the text mining process?

• • •

See Figure 7.5 (p. 304). Text mining entails three tasks: Establish the Corpus: Collect and organize the domain-specific unstructured data Create the Term–Document Matrix: Introduce structure to the corpus Extract Knowledge: Discover novel patterns from the T-D matrix

2. What is the reason for normalizing word frequencies? What are the common methods for normalizing word frequencies?

In order to have a more consistent TDM for further analysis, these raw indices need to be normalized. Common methods are Log frequencies, Binary frequencies, and Inverse document frequencies.

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3. What is singular value decomposition? How is it used in text mining?

Singular value decomposition (SVD), which is closely related to principal components analysis, reduces the overall dimensionality of the input matrix (number of input documents by number of extracted terms) to a lower dimensional space, where each consecutive dimension represents the largest degree of variability (between words and documents) possible 4. What are the main knowledge extraction methods from corpus?

The main categories of knowledge extraction methods are classification, clustering, association, and trend analysis.

Section 7.6 Review Questions 1. What are some of the most popular text mining software tools?

1. ClearForest offers text analysis and visualization tools.

2. IBM Intelligent Miner Data Mining Suite, now fully integrated into IBM’s InfoSphere Warehouse software, includes data and text mining tools.

3. Megaputer Text Analyst offers semantic analysis of free-form text, summarization, clustering, navigation, and natural language retrieval with search dynamic refocusing.

4. SAS Text Miner provides a rich suite of text processing and analysis tools.

5. SPSS Text Mining for Clementine extracts key concepts, sentiments, and relationships from call-center notes, blogs, e-mails, and other unstructured data and converts it to a structured format for predictive modeling.

6. The Statistica Text Mining engine provides easy-to-use text mining functionally with exceptional visualization capabilities.

7. VantagePoint provides a variety of interactive graphical views and analysis tools with powerful capabilities to discover knowledge from text databases.

8. The WordStat analysis module from Provalis Research analyzes textual information such as responses to open-ended questions, interviews, etc.

2. Why do you think most of the text mining tools are offered by statistics companies?

Students’ should mention that many of the capabilities of data mining apply to text mining. Since statistics companies offer data mining tools, offering text mining is a natural business extension.

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3. What do you think are the pros and cons of choosing a free text mining tool over a commercial tool?

Free tools have fewer features, more difficult user-interfaces, lack support, and have slower or reduced processing capabilities. The advantage of free tools is obviously the cost.

Section 7.7 Review Questions 1. What are some of the main challenges the Web poses for knowledge discovery?

• The Web is too big for effective data mining.

• The Web is too complex.

• The Web is too dynamic.

• The Web is not specific to a domain.

• The Web has everything.

2. What is Web mining? How does it differ from regular data 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. Text mining is less structured because it’s based on words instead of numeric data.

3. What are 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.

Section 7.8 Review Questions 1. What is Web content mining? How does it differ from text mining?

Web content mining refers to the extraction of useful information from Web pages. The documents may be extracted in some machine-readable format so that automated techniques can generate some information about the Web pages.

2. Define Web structure mining, and differentiate it from Web content mining.

Web structure mining is the process of extracting useful information from the links embedded in Web documents.

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3. What are the main goals of Web structure mining?

It is used to identify authoritative pages and hubs, which are the cornerstones of the contemporary page-rank algorithms that are central to popular search engines such as Google and Yahoo!

4. What are hubs and authorities? What is the HITS algorithm?

A search on the Web to obtain information on a specific topic usually returns a few relevant, high-quality Web pages and a larger number of unusable Web pages. Use of an index based on authoritative pages (or some measure of it) will improve the search results and ranking of relevant pages.

The structure of Web hyperlinks has led to another important category of Web pages called a hub. A hub is one or more Web pages that provide a collection of links to authoritative pages HITS is a link analysis algorithm that rates Web pages using the hyperlink information contained within them. In the context of Web search, the HITS algorithm collects a base document set for a specific query.

Section 7.9 Review Questions 1. Define Web usage mining.

Web usage mining is the extraction of useful information from data generated through Web page visits and transactions.

2. In an e-commerce environment, what are the potential applications of Web usage mining?

Analysis of the information collected by Web servers can help us better understand user behavior.

3. What is a clickstream? Why is it important in Web usage mining?

Analysis of Web usage data is often called clickstream analysis. By using the data and text mining techniques, a company might be able to discern interesting patterns from the clickstreams. Such information could be useful in determining where to place online advertisements. Clickstream analysis might also be useful for knowing when visitors access a site.

4. What types of information do Web servers collect when users visit a Web page and engage in an interactive activity?

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Log data in server access logs, referrer logs, agent logs and client-side cookies

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User profiles Metadata such as page attributes, content attributes and usage data

Collected information includes the IP address of the visitor’s computer, the site from which the visitor originated, how long the visitor remained on the page, which pages were visited, information stored in cookies.

5. Identify value-added features developed by major e-commerce sites that may be based on Web usage 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 user access patterns.

Predict user behavior based on previously learned rules and users’ profiles.

Present dynamic information to users based on their interests and profiles.

Section 7.10 Review Questions 1. Why do we need Web mining?

Forward-thinking companies like Ask.com, Scholastic, and St. John Health System are actively using Web mining systems to answer critically important questions of “Who?” “Why?” and “How?” As documented, the benefit of integrating these systems effectively and efficiently can be significant, both in terms of incremental financial growth and increasing customer loyalty and satisfaction.

2. In your own words, what are the pros and cons of Web mining?

Students’ answers will vary.

3. What are the common characteristics of Web mining success stories?

Given the continual shift of advertising dollars, resources, and, most important, customers into the online channel, the belief is that executives who aggressively pursue a more holistic view of their customers using Web mining techniques will have a substantial advantage over those who continue to base their analyses on intuitions, gut feelings, and wild guesses.

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ANSWERS TO QUESTIONS FOR DISCUSSION

       

1. Explain the relationship among data mining, text mining, and Web mining.

Technically speaking, data mining is a process that uses statistical, mathematical, and artificial intelligence techniques to extract and identify useful information and subsequent knowledge (or patterns) from large sets of data. Data mining is the general concept. 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).

2. What should an organization consider before making a decision to purchase text mining and/or Web mining software?

Before making a decision to purchase any mining software organizations should consider the standard criteria to use when investing in any major software: cost/benefit analysis, people with the expertise to use the software and perform the analyses, availability of data/information, and a business need for the software and capabilities.

3. Discuss the differences and commonalities between text mining and Web mining.

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).

4. In your own words, define text mining and discuss its most popular applications.

Students’ answers will vary.

5. Discuss the similarities and differences between the data mining process (e.g., CRISP-DM) and the three step, high-level text mining process explained in this chapter.

CRISP-DM provides a systematic and orderly way to conduct data mining projects. 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; the model results are evaluated; and the models can be employed for regular use.

• • •

Text mining entails three tasks: See Figure 7.5 (p. 304).

Establish the Corpus: Collect and organize the domain-specific unstructured data Create the Term–Document Matrix: Introduce structure to the corpus Extract Knowledge: Discover novel patterns from the T-D matrix

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6. What does it mean to induce structure into the text-based data? Discuss the alternative ways of inducing structure into text-based data.

Text-based data is inherently unstructured and must be converted (induced) to a structured format for predictive modeling or other type of analysis. The second step of the 3-step text mining process introduces structure to the corpus so that knowledge can be extracted.

Text mining uses natural language processing to induce structure into the text collection and then uses data mining algorithms such as classification, clustering, association, and sequence discovery to extract knowledge from it.

Text mining uses natural language processing to induce structure into the text collection and then uses data mining algorithms such as classification, clustering, association, and sequence discovery to extract knowledge from it.

SPSS Text Mining for Clementine extracts key concepts, sentiments, and relationships from call-center notes, blogs, e-mails, and other unstructured data and converts it to a structured format for predictive modeling.

7. What is the role of natural language processing in text mining? Discuss the capabilities and limitations of NLP in the context of text mining.

NLP is an important component of text mining. It studies the problem of “understanding” the natural human language, with the view of converting depictions of human language (such as textual documents) into more formal representations (in the form of numeric and symbolic data) that are easier for computer programs to manipulate. The goal of NLP is to move beyond syntaxdriven text manipulation (which is often called “word counting”) to a true understanding and processing of natural language that considers grammatical and semantic constraints as well as the context.

8. List and discuss three prominent application areas for text mining. What is the common theme among the three application areas you chose?

National security and counterterrorism: The FBI’s system is expected to create a gigantic data warehouse along with a variety of data and text mining modules to meet the knowledge discovery needs of federal, state, and local law enforcement agencies.

Biomedical: A system extracts disease–gene relationships from literature accessed via MEDLINE. Marketing: Text mining can be used to increase cross-selling and up-selling by analyzing the unstructured data generated by call centers.

The value of all of these applications is significantly increased by extracting knowledge from huge volumes of text-data and documents through the use of text mining tools.

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9. Discuss the relationship between Web mining and Web analytics.

Web mining can be defined as the discovery and analysis of interesting and useful information from the Web about the Web, and usually using Web-based tools. Web analytics is the analysis that can be done to text-data captured and processed during Web mining. Web analytics applications focus on “where and when” questions by aggregating, mining, and visualizing large volumes of data, by reporting on online marketing and visitor acquisition efforts, by summarizing page-level visitor interaction data, and by summarizing visitor flow through defined multistep processes.

Web analytics is underlying technology for Web site optimization ecosystem, which is defined by the ability to log, parse, and report on the clickstream behavior of site visitors.

10. What are the three main areas of Web mining? Discuss the differences and commonalities among these three areas.

Web mining consists of three areas: Web content mining, Web structure mining, and Web usage mining.

Web content mining refers to the automatic extraction of useful information from Web pages. It may be used to enhance search results produced by search engines.

Web structure mining refers to generating interesting information from the links included in Web pages. Web structure mining can also be used to identify the members of a specific community and perhaps even the roles of the members in the community.

Web usage mining refers to developing useful information through analysis of Web server logs, user profiles, and transaction information.

11. What is Web content mining? How does it differ from text mining? Discuss and justify your answers with concrete examples.

Same answer as above (#10). Students’ examples will differ.

12. What is Web structure mining? What are authoritative pages and hubs? How do they relate to Web structure mining?

Web structure mining refers to generating interesting information from the links included in Web pages. Web structure mining can also be used to identify the members of a specific community and perhaps even the roles of the members in the community.

A search on the Web to obtain information on a specific topic usually returns a few relevant, high-quality Web pages and a larger number of unusable Web

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pages. Use of an index based on authoritative pages (or some measure of it) will improve the search results and ranking of relevant pages.

The structure of Web hyperlinks has led to another important category of Web pages called a hub. A hub is one or more Web pages that provide a collection of links to authoritative pages HITS is a link analysis algorithm that rates Web pages using the hyperlink information contained within them. In the context of Web search, the HITS algorithm collects a base document set for a specific query.

13. Discuss the expected benefits of Web structure mining. Provide examples from realworld applications that you are familiar with.

Web structure mining is expected to create value from the immense volume of data collected by Web servers. Students’ examples will vary.

14. What is Web usage mining? Draw a picture of the Web usage mining process and explain/discuss the major steps in the process.

Web usage mining refers to developing useful information through analysis of Web server logs, user profiles, and transaction information.

See Figure 7.9 on p. 317.

15. Provide two exemplary business applications of Web usage mining; discuss their usage and business value.

Students’ answers will vary. Examples are: 1. Determine the lifetime value of clients.

2. Design cross-marketing strategies across products.

3. Evaluate promotional campaigns.

4. Target electronic ads and coupons at user groups based on user access patterns.

5. Predict user behavior based on previously learned rules and users’ profiles.

6. Present dynamic information to users based on their interests and profiles.

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.

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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.)

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Chapter 8: Data Warehousing Learning Objectives for Chapter 8 1.

2.

3.

4.

5.

6.

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 7. Describe real-time (active) data warehousing 8. Understand data warehouse administration and security issues

CHAPTER OVERVIEW The concept of data warehousing has been around since the late 1980s. This chapter provides the foundation for an important type of database, called a data warehouse, which is primarily used for decision support and provides improved analytical capabilities. We discuss data warehousing in the following sections: CHAPTER OUTLINE 8.1

OPENING VIGNETTE: DIRECTV THRIVES WITH ACTIVE DATA WHAREHOUSING A. PROBLEM B. SOLUTION C. RESULTS  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

8.2

DATA WAREHOUSING DEFINITIONS AND CONCEPTS A. WHAT IS A DATA WAREHOUSE? B. CHARACTERISTICS OF DATA WAREHOUSING C. DATA MARTS

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D. E.

F.

8.3

OPERATIONAL DATA STORES ENTERPRISE DATA WAREHOUSES (EDW) ⧫ Application Case 8.1: Enterprise Data Warehouse Delivers Cost Savings and Process Efficiencies METADATA  Section 8.2 Review Questions

DATA WAREHOUSING PROCESS OVERVIEW ⧫ Application Case 8.2: Data Warehousing Supports First American Corporation’s Corporate Strategy  Section 8.3 Review Questions

8.4 DATA WAREHOUSING ARCHITECTURES A. ALTERNATIVE ARCHITECTURES B. WHICH ARCHITECTURE IS THE BEST  Section 8.4 Review Questions 8.5

DATA INTEGRATION AND THE EXTRACTION, TRANSFORMATION, AND LOAD (ETL) PROCESS A. DATA INTEGRATION ⧫ Application Case 8.3: BP Lubricants Achieves BIGS Success B. EXTRACTION, TRANSFORMATION, AND LOAD  Section 8.5 Review Questions

8.6

DATA WAREHOUSE DEVELOPMENT ⧫ Application Case 8.4: 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 8.5: 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 8.6: Hosted Data Warehouses E. DATA WAREHOUSING IMPLEMENTATION ISSUES ⧫ Application Case 8.7: A Large Insurance Company Integrates Its Enterprise Data with AXIS F. MASSIVE DATA WAREHOUSES AND SCALABILITY  Section 8.6 Review Questions

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8.7

REAL-TIME DATA WAREHOUSING ⧫ Application Case 8.8: Egg plc Fries the Competition in Near-Real-Time ⧫ Technology Insights 8.9: The Real-Time Realities of Active Data Warehousing.  Section 8.7 Review Questions

8.8

DATA WAREHOUSE ADMINISTRATION AND SECURITY ISSUES ⧫ Technology Insights 8.10: Ambeo Delivers Proven Data Access Auditing Solution  Section 8.8 Review Questions

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8.9

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: Continental Airlines Flies High with Its Real-Time Data Warehouse  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 8.2 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

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turned out to be important in practice. So, it’s important to really know what each of them is about. (The concept of an 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 8.3 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 8.4, 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 8.5 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 8.6, 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 8.4 but are not mentioned here. While these aren’t promoted for data warehouses quite as much as they were a few years ago, 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 8.7 should go quickly. The business case for real-time DWs is well presented, and the technology introduces no new conceptual difficulties.

Section 8.8 returns to issues that students will be familiar with from a general database management context. The need for “establishing effective corporate security

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policies and procedures,” for instance, is not unique to data warehouses. Therefore, you may, again, want to 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 8.1 Review Questions 1. Why is it important for DirecTV to have an active data warehouse?

With real-time data, DirecTV can view the current state of its business and quickly identify problems, which is the first and foremost step toward solving them analytically.

2. What were the challenges DirecTV faced on its way to having an integrated active data warehouse?

DirecTV faced the challenge of dealing with high transactional data volumes created by an escalating number of daily customer calls. Accommodating such a large data volume, along with rapidly changing market conditions, was one of DirecTV’s key challenges.

3. Identify the major differences between a traditional data warehouse and an active data warehouse, such as the one implemented at DirecTV. Before the active data warehouse solution, the data was pulled from the server every night in batch mode, a process that was taking too long and straining the system. A daily batch-data upload to the data warehouse had long been (and for many companies, still is) the standard procedure. With an active data warehouse, fresh data is immediately available.

4. What strategic advantage can DirecTV derive from the real-time system as opposed to a traditional information system?

With fresh data at their fingertips, call center sales personnel were able to contact a customer who had just asked to be disconnected and make a new sales offer to retain the customer just hours later the same day.

5. Why do you think large organizations like DirecTV cannot afford not to have a capable data warehouse?

Decision makers require concise, dependable information about current operations, trends, and changes. Data are often fragmented in distinct operational systems, so managers often make decisions with partial information, at best. Data warehousing

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cuts through this obstacle by accessing, integrating, and organizing key operational data in a form that is consistent, reliable, timely and readily available, wherever and whenever needed.

In addition, customers can obtain real-time information on their subscriptions, TV services, and other account information, so the system also provides a significant competitive advantage over competitors.

Section 8.2 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.

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What is an ODS? Operational Data Store is the database from which a business operates on an ongoing 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 8.3 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.

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• • •

3.

Comprehensive database. This is the EDW that supports decision analysis by providing relevant summarized and detailed information.

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 8.4 Review Questions 1.

What are the key similarities and differences between a two-tiered and a threetiered 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.

Information interdependence between organizational units Upper management’s information needs Urgency of need for a data warehouse Nature of end-user tasks

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5.

6.

7.

8.

9.

10.

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Constraints on resources 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.)

5.

Which data warehousing architecture is the best? Why? See Table 8.1 Average Assessment Scores for the Success of the Architectures. What is interesting is the similarity of the averages for the bus, hub-and-spoke, and centralized architectures. The differences are sufficiently small that no claims can be made for a particular architecture’s superiority over the others, at least based on a simple comparison of these success measures.

Section 8.5 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.

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Section 8.6 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.

2.

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.

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-the-ground-up data modeling project.

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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 8.4, Review Question 3.

Section 8.7 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).

2.

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.

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.

1

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.

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3.

4.

5.

6.

7.

4.

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.

List some of the drivers for RDW. • • •

•

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.

Section 8.8 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

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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 8.9 Review Questions Section 8.9 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 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.

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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 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

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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 lowquality 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.)

9.

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

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.

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(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 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.

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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 realtime data warehouse, as was implemented at Continental.

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 long-term decisions aren’t affected much by the last hour’s, day’s or even week’s data, lower-level 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 near-real-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.)

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Chapter 9: Business Performance Management

Learning Objectives for Chapter 9 1. Understand the all-encompassing nature of business performance management (BPM) 2. Understand the closed-loop processes linking strategy to execution 3. Describe some of the best practices in planning and management reporting 4. Describe the difference between performance management and measurement 5. Understand the role of methodologies in BPM 6. Describe the basic elements of the balanced scorecard and Six Sigma methodologies 7. Describe the differences between scorecards and dashboards 8. Understand some of the basics of dashboard design CHAPTER OVERVIEW

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Business performance management (BPM) is an outgrowth of decision support systems (DSS), enterprise information systems (EIS), and business intelligence (BI). From a market standpoint, it was over 25 years in the making. As with decision support, BPM is more than just a technology. It is an integrated set of processes, methodologies, metrics, and applications designed to drive the overall financial and operational performance of an enterprise. It helps enterprises translate their strategies and objectives into plans, monitor performance against those plans, analyze variations between actual results and planned results, and adjust their objectives and actions in response to this analysis.

This chapter examines the processes, methodologies, metrics, and systems underlying BPM. Because BPM is distinguished from DSS and BI by its focus on strategy and objectives, the chapter begins with an exploration of the notions of enterprise strategy and execution and the gap that often exists between them. The specific sections are:

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CHAPTER OUTLINE 9.1

OPENING VIGNETTE: DOUBLE DOWN AT HARRAH’S A. PROBLEM B. SOLUTION C. RESULTS AND A NEW PROBLEM  Questions for the Opening Vignette D. WHAT WE CAN LEARN FROM THIS VIGNETTE

9.2

BUSINESS PERFORMANCE MANAGEMENT (BPM) OVERVIEW A. BPM DEFINED B. BPM AND BI COMPARED  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 Coffee Wars  Section 9.5 Review Questions

9.6

ACT AND ADJUST: WHAT DO WE NEED TO DO DIFFERENTLY?  Section 9.6 Review Questions

9.7

PERFORMANCE MEASUREMENT A. KPIS AND OPERATIONAL METRICS B. PROBLEMS WITH EXISTING PERFORMANCE MEASUREMENT SYSTEMS C. EFFECTIVE PERFORMANCE MEASUREMENT ⧫ Application Case 9.2: Expedia.com’s Customer Satisfaction Scorecard  Section 9.7 Review Questions

9.8

BPM METHODOLOGIES A. BALANCED SCORECARD (BSC) 1.

The Meaning of Balance

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B.

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2.

Aligning Strategies and Actions SIX SIGMA 1.

Six Sigma Defined 2.

The DMAIC Performance Model 3.

Lean Six Sigma 3.

The Payoff from Six Sigma ⧫ Technology Insights 9.3: BSC Meets Six Sigma  Section 9.8 Review Questions

9.9

BPM ARCHITECTURE AND APPLICATIONS A. BPM ARCHITECTURE 1.

BPM Applications B. COMMERCIAL BPM SITES  Section 9.9 Review Questions

9.10

PERFORMANCE DASHBOARDS AND SCORECARDS A. DASHBOARDS VERSUS SCORECARDS B. DASHBOARD DESIGN C. WHAT TO LOOK FOR IN A DASHBOARD  Section 9.10 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: Tracking Citywide Performance  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 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

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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.

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ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 9.1 Review Questions 1.

Describe Harrah’s marketing strategy. How does Harrah’s strategy differ from its competitors?

Harrah’s has generally avoided investing vast sums of money in lavish hotels, shopping malls, and attractions, unlike its competitors. Instead, it has operated on the basis of a business strategy that focuses on “knowing their customers well, giving them great service, and rewarding their loyalty so that they seek out a Harrah’s casino whenever and wherever they play” (Watson and Volonino, 2001).

The execution of this strategy has involved creative marketing, innovative uses of information technology, and operational excellence.

2.

What is Harrah’s Total Rewards program?

Harrah’s answer was Total Gold, a patented customer loyalty program that is now known as the Total Rewards program. Not only did the program serve to reward customers with cash and comps for their gaming and other activities at any of Harrah’s properties, but, more important, it provided Harrah’s with a vast collection of high volume, real-time transaction data regarding its customers and their behaviors.

3.

What are the basic elements of Harrah’s closed-loop marketing system?

The data warehouse forms the base of a “closed-loop” marketing system that enables Harrah’s to clearly define the objectives of its marketing campaigns, to execute and monitor those campaigns, and to learn what types of campaigns provide the highest return for particular types of customers.

4.

What were the results of Harrah’s marketing strategy?

Harrah’s Total Rewards loyalty card program and closed-loop marketing system has produced substantial returns over the past decade, including (Watson and Volonino, 2001): • A brand identity for Harrah’s casinos • An increase in customer retention worth several million dollars • An increase in the number of customers who play at more than one Harrah’s property, increasing profitability by millions of dollars • A high internal rate of return on its information technology investments

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

What economic issues does Harrah’s face today? Could the Total Rewards system be modified in any way to handle these issues?

Yet, like its competitors, Harrah’s still faces substantial economic problems. In the first 3 months of 2009, it posted operating losses of $127 million, although this was an improvement over 2008. In the first 3 months of 2008, it had operating losses of $270 million. In 2008, Harrah’s also doubled its debt load (to the tune of $24 billion) when it was taken private in January 2008 by the equity firms Apollo Management and TPG Capital. Today, its debt load has left it facing potential bankruptcy.

So, even though Harrah’s has had an award-winning performance management system in place for years and is a recognized leader in the use of data and predictive analytics, it still confronts the same strategic problems and economic issues that its “lesser equipped” competitors face.

For a number of years, Harrah’s closed-loop marketing system enabled it to execute a strategy that clearly differentiated it from its competitors. The system also provided the means to monitor key indicators of operational and tactical importance. One of the problems with the system is that it is predicated on the assumption of growing, or at least stable, demand. What it could not do, at least in the short run, was predict drastically reduced or nonexistent demand or fundamental changes in the economy.

Section 9.2 Review Questions 3.

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.

How Does BPM differ from BI? How are they the same?

BPM is an outgrowth of BI and extends it. BI needs to be extended to support BPM properly.

• BPM is promoted and sold by the same companies that market and sell the BI tools and suites.

• BI has evolved so that many of the original differences between the two no longer exist (e.g., BI used to be focused on departmental rather than enterprisewide projects).

• BI is a crucial element of BPM.

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Briefly describe TSA’s PIMS system

The Transportation Security Administration (TSA) uses a BI system called the Performance Information System (PIMS) to track passenger volumes, screener performance (attribution, absenteeism, overtime, and injuries), dangerous items, and total passenger throughput.

4. 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.

2.

3.

4.

5.

6.

7.

8.

3.

Conduct a current situation analysis Determine the planning horizon Conduct an environmental scan Identify critical success factors Complete a gap analysis Create a strategic vision Develop a business strategy Identify strategic objectives and goals

What are some of the sources of the gap between formulating a strategy and actually executing the strategy?

Communications, Alignment of rewards and Incentives, Focus, and Resources

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Section 9.4 Review Questions 1.

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 critical questions that a monitoring framework answers?

What to monitor, and How to monitor

2.

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.

3.

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.

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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.

Section 9.6 Review Questions 1.

Why do 60 to 80 percent of all new projects or ventures fail?

A project can fail in a number of different ways, ranging from considering too few options or scenarios, failing to anticipate a competitor’s moves, ignoring changes in the economic or social environment, inaccurately forecasting demand, or underestimating the investment required to succeed, just to name a few of the possibilities. This

2.

Describe the basic steps in Harrah’s closed-loop model.

1. The loop begins by defining quantifiable objectives of a marketing campaign or test procedure in the form of expected values or outcomes for customers who are in the experimental test group versus those in the control groups. The campaign is designed to provide the right offer and message at the right time.

The selection of customers and their treatments are based on their prior experiences with Harrah’s.

2. Next, the campaign or test is executed. The campaign is designed to provide the right offer and message at the right time. The selection of particular customers and the treatments they receive are based on their prior experiences with Harrah’s.

3. Each customer’s response to the campaign is tracked. Not only are response rates measured, but other metrics are as well, such as revenues generated by the incentive and whether the incentive induced a positive change in behavior (e.g., increased frequency of visit, profitability of the visit, or cross-play among the various casinos).

4. The effectiveness of a campaign is evaluated by determining the net value of the campaign and its profitability relative to other campaigns.

5. Harrah’s learns which incentives have the most effective influence on customer behavior or provide the best profitability improvement. This knowledge is used to continuously refine its marketing approaches.

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3.

According to the Saxon Group’s research results, what are some of the performance management practices of the average company?

The overall impact of the planning and reporting practices of the average company was that management had little time to review results from a strategic perspective, decide what should be done differently, and act on the revised plans.

The fact is that there was little tie between a company’s strategy, tactics, and expected outcomes (Axson, 2007).

4.

Why do few companies have time to analyze their strategic and tactical results and take corrective action based on this analysis?

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.”

2.

What is a KPI, and what are its distinguishing characteristics?

KPIs are multidimensional. Loosely translated, this means that KPIs have a variety of distinguishing features, including: • Strategy. KPIs embody a strategic objective.

• Targets. KPIs measure performance against specific targets. Targets are defined in strategy, planning, or budget sessions and can take different forms (e.g., achievement targets, reduction targets, absolute targets, etc.).

• Ranges. Targets have performance ranges (e.g., above, on, or below target).

• Encodings. Ranges are encoded in software, enabling the visual display of performance (e.g., green, yellow, red). Encodings can be based on percentages or more complex rules.

• Time frames. Targets are assigned time frames by which they must be accomplished. A time frame is often divided into smaller intervals to provide performance mileposts.

• Benchmarks. Targets are measured against a baseline or benchmark. The previous year’s results often serve as a benchmark, but arbitrary numbers or external benchmarks may also be used.

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How does a KPI differ from an operational metric?

Most organizations collect a wide range of operational metrics. As the name implies, these metrics deal with the operational activities and performance of a company. The following list of examples illustrates the variety of operational areas covered by these metrics: • Customer performance. Metrics for customer satisfaction, speed and accuracy of issue resolution, and customer retention.

• Service performance. Metrics for service-call resolution rates, service renewal rates, SLA compliance, delivery performance, and return rates.

• Sales operations. New pipeline accounts, sales meetings secured, conversion of inquiries to leads, and average call closure time.

• Sales plan/forecast. Metrics for price-to-purchase accuracy, purchase order to fulfillment ratio, quantity earned, forecast-to-plan ratio, and total closed contracts.

2.

What are some of the drawbacks of relying solely on financial metrics for measuring performance?

The text cites these four: • 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.

4.

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.

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• •

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.

Section 9.8 Review Questions 4.

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.

5.

What are the four perspectives in BSC? The four perspectives in BSC are financial, customer, internal business processes, and learning/growth.

6.

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 non-financial indicators, between leading and lagging indicators, between internal and external indicators, between quantitative and qualitative indicators, and between short-term 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.)

7.

How does a BSC align strategies and actions?

As a strategic management methodology, BSC enables an organization to align its actions with its overall strategies. BSC accomplishes this task through a series of interrelated steps. The specific steps that are involved vary from one book to the next. In their latest rendition, Kaplan and Norton (2008) lay out a six stage process: 1. Developing and formulating a strategy. Develop and clarify the organization’s mission, values, and vision; identify through strategic analysis the internal and external forces impacting the strategy; and define the organization’s strategic direction, specifying where and how the organization will compete.

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2. Planning the strategy. Convert statements of strategic direction into specific objectives, measures, targets, initiatives, and budgets that guide actions and align the organization for effective strategy execution.

3. Aligning the organization. Ensure that business unit and support unit strategies are in line with the corporate strategy and that employees are motivated to execute the strategy.

4. Planning the operations. Ensure that the changes required by strategy are translated into changes in operational processes and that resource capacity, operational plans, and budgets reflect the directions and needs of the strategy.

5. Monitoring and learning. Determine through formal operational review meetings whether short-term financial and operational performance are in line with specified targets and through strategy review meetings whether the overall strategy is being executed successfully.

6. Testing and adapting the strategy. Determine through strategy testing and adapting meetings whether the strategy is working, whether fundamental assumptions are still valid, and whether the strategy needs to be modified or adapted over time.

8.

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 is a strategic theme?

Strategic themes split a strategy into several distinct value-creating processes.

Each strategic theme represents a collection of related strategic objectives.

7.

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.

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

What are the basic processes in the DMAIC model?

The basic processes in the DMAIC model is define, measure, analyze, improve, and control.

9.

Compare Lean Production with Six Sigma See Table 9.2, p.400.

10.

What are some of the ways that the success of Six Sigma implementations is improved?

Six Sigma is integrated with business strategy. Six Sigma techniques are powerful in reducing process variation. Today, an increasing number of companies are implementing a Six Sigma approach to business excellence as part of the business strategy.

• Six Sigma supports business objectives. Successful deployments are based on some major business challenge or risk that the company can overcome only through Six Sigma. Identifying the challenge means all the company’s business leaders are clear about why the company is adopting strategies based on Six Sigma principles.

• Key executives are engaged in the process. A company must involve all key business leaders in helping to design its Six Sigma deployment. Managers will never fully support Six Sigma if they view it as taking away from their resources rather than adding capability and helping them become more successful in achieving their goals; nor will they actively support it if they think it is eating up vital budgetary allotments rather than setting the stage for significant financial payback.

• Project selection process is based on value potential. The most effective Six Sigma companies have a rigorous project selection process driven by an evaluation of how much shareholder value a project can generate. It can be characterized as a trade-off decision comparing value delivered to effort expended.

• There is a critical mass of projects and resources. Some companies start their deployments by training a handful of people and launching a few “demonstration” projects. Others ramp-up for immediate corporate-wide deployment, training hundreds of Black Belts and launching dozens of projects within the first 6 months. In this context a Black Belt refers to an employee who is trained or certified in Six Sigma and who devotes 100% of his or her time to the execution of a Six Sigma project. Either approach is workable, but for every company there is a critical level of Six Sigma effort.

• Projects-in-process are actively managed. Given that most companies want to generate measurable, significant results within 6 months or a year, the tendency is to push as many projects into the Lean Six Sigma deployment as possible. It is better to focus on getting a few high-potential projects done right than to just flood the workplace with dozens of less-important projects. With the right

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resources working on the right projects, learning and results are maximized by short cycle times.

• Team leadership skills are emphasized. Use of Six Sigma does involve some technical skills—the ability to process and analyze data, for example. But good leadership skills are even more important. This emphasis on leadership also relates to how a company chooses people to fill Black Belt roles. Placing the most promising people in the Black Belt role is painful at first, yet it yields fast results and a rapid transformation of the organization.

• Results are rigorously tracked. Six Sigma results should “pay as you go” and be confirmed 11.

Compare BSC with Six Sigma.

See Technology Insights 9.3, Table 9.3, p 402-403.

12.

How can BSC and Six Sigma be integrated?

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.

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. .

Section 9.9 Review Questions 1.

What is logical 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.”

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.

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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.

3. Describe the major categories of BPM applications?

Budgeting, planning and forecasting Profitability modeling and optimization Scorecard applications Financial consolidation Statutory and financial reporting 4. What major change has occurred in the BPM market in the last 3 to 4 years?

Over the past 3 to 4 years, the biggest change in the BPM market has been the consolidation of the BPM vendors.

5. 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 • Challengers: SAP (the only vendor listed here; it is not possible to list “some vendors” in this category) • Visionaries: Oracle CPM, Business Objects, others • Niche: Oracle/PeopleSoft EPM, Lawson, others

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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.

Section 9.10 Review Questions 1.

What are the major differences between a scorecard and a dashboard?

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 distinguishes an operational dashboard from a tactical dashboard?

They can be distinguished as follows: 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.

3. What layers of information are provided by a dashboard?

The most distinctive feature of a dashboard is its three layers of information: 1. Monitoring. Graphical, abstracted data to monitor key performance metrics.

2. Analysis. Summarized dimensional data to analyze the root cause of problems.

3. Management. Detailed operational data that identifies what actions to take to resolve a problem.

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.

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.

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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.

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.

ANSWERS TO QUESTIONS FOR DISCUSSION

       

1. 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 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 five 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.

The section titled “BPM Applications” 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

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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. Netflix’s strategy of moving to online video downloads has been widely discussed in a number of articles that can be found online. What are the basic objectives of Netflix’s strategy? What are some of the major assumptions underlying the strategy?

Given what you know about discovery-driven planning, do these assumptions seem reasonable?

Students’ answers will differ. The basic strategy was to deliver movies to audiences using streaming technology to eliminate the need for expensive physical storefronts. Netflix was going to introduce a Netflix Player to downloading and playing movies, but that strategy was canceled for streaming. A good reference for the major objectives of Netflix’s strategy are discussed in Wired at http://www.wired.com/techbiz/it/magazine/17-10/ff_netflix?currentPage=all The CEO and founder of Netflix in 1997 had predicted that his company would deliver video over the Net rather than via mail.

Some basic assumptions were that users would prefer not to travel, be willing to pay for the convenience of streaming video, have devices and bandwidth needed for quality movie watching via streaming; and that some other technology would not make streaming obsolete.

Discovery-driven planning provides a way to systematically uncover problematic assumptions that otherwise remain unnoticed or unchallenged in the planning and monitoring processes. Students’ answers may differ, but the assumptions seem reasonable based on discovery-driven planning.

5. 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):

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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.

6. Describe how a BSC fits the description of a diagnostic control system.

A diagnostic control system is defined 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.

7. 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.

8. 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.)

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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 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.

9. Create a measure for some strategic objective of interest (you can use one of the objectives formulated in Discussion Question 3).

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

Property What is it called, and is it understandable?

Why is it being used, and what actions is it encouraging?

Relates to

What strategies does it support, and what other measures is it linked to?

Metric/formula

What is the actual measurement?

Target level(s) Frequency

What is the desirable level of performance?

How often is the measurement made and reported?

Explanation Market share increase; yes Used to measure improvement in our competitive position in our industry; encourages sales Supports strategy of expanding market penetration to obtain new customers while market is still in early growth stages; linked to shipments Ratio of sales revenue to total market size using public industry data 30 percent Monthly

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall

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Measurement Source of data

Property Where do the data come from?

Who measures

What are the name and function of the person responsible for collecting, collating, and analyzing the data?

Who—name and function—is responsible for ensuring the performance levels?

How will the owner use the data, and what actions can he or she take to improve performance?

Who acts on the data (owner) What do they do

Efficiency

Effectiveness

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?

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?

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Explanation Our data: accounting; industry data: IDC John Doe, Business Analyst

Jane Roe, VP of Sales 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

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10. 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.

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

11. 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

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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.”

12. 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.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS

1.



What are the major components of the CPR dashboard?

The project had three components: • Performance management application. A backend computer system providing a single point of access for agencies to input data.

• Analytics tool/dashboard. The front-end system to provide standardized reporting format with drill-down capabilities, performance summaries, and trend graphics.

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• Data definition. Review and identification of the topics measures and critical indicators to be included in the CPR system for 44 mayorial agencies.

2.

How many agencies were involved in the definition and implementation of the CPR dashboard?

Development of these components continued through July 2007. At this time, the system was opened to the 44 agencies and the mayor’s office for review. At the end of the summer, Mayor Bloomberg directed that the system be made available to the public through the city’s NYC.gov Web site as soon as possible. Subsequent work on the system focused on making the dashboard as easy and flexible as possible for public use.

3.

What were the major steps used in defining and implementing the CPR? The CPR required the agencies to take a fresh look at the services they were delivering and how best to measure the outcomes of those services. Initially, this resulted in thousands of measures, many of which did not exist at the time.

Eventually, the list of initial measures was reduced to 525 critical measures. The majority of these measures represent final outcomes of services that directly impact the city’s residents. This is why the decision was made to open up the system to the public.

4.

What role did “themes” play in the CPR dashboard?

The developers decided to categorize the measures into eight citywide “themes” that captured the way the city government serves the people who work and live in New York City. The themes included: citywide administration, community services, economic development and business affairs, infrastructure, education, legal affairs, public safety, and social services.

5.

What are the major capabilities of the CPR dashboard?

The CPR dashboard does this by providing the following capabilities (NYCStat, 2009): • Tracks performance for the most important “outcome” measures that directly reflect how the lives of the public are affected by city government.

• Measures performance by comparing current data to prior data for the year before, thereby holding agencies accountable for year-over-year improvements.

• Highlights agency performance through graphs and color coding, making positive or negative performance trends immediately obvious.

• Drill-down capability, allowing users to review comparative trends over a 5-year period.

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• Aggregates important measures into citywide themes, which cut across agency silos and disciplines to reveal the overall picture about city government performance.

• Updates each measure monthly, quarterly, or annually so that the most recent data is always available.

• Offers the ability to download data in a variety of formats for more detailed review and analysis.

• Provides detailed information about each measure, including an explanation of what the measure means, its reporting frequency, and other useful details.

6.

What can businesses learn from a government initiative like the CPR? According to Sarlin, “It is interesting to see how government agencies at all levels are implementing best practices to be more run like a business.” In the case of New York City’s Mayoral Operations Office, you might expect this, because Mayor Bloomberg comes from the world of business, more specifically, the world of real-time financial information (see bloomberg.com). However, other government agencies have announced online dashboards even though they have virtually no connection with business. For instance, the new U.S. Chief Information Officer Vivek Kundra recently announced the U.S. Federal IT Dashboard, which provides public access to data reported to the Office of Management and Budget (OMB), including general information on over 7,000 federal IT investments and detailed data for nearly 800 of these investments classified as “major.” Mr. Kundra has spent his entire career in government positions. These efforts suggest that business may be able to learn quite a bit from these initiatives.

These government dashboard initiatives demonstrate the following (Buytendijk, 2008): • Transparency makes a difference. These initiatives provide widespread, public access to large collections of performance data. Many businesses could learn from the depth and breadth of the information these agencies are sharing.

• Importance of collaboration. Many BI projects suffer from a silo approach, with different departments doing their own dashboards or scorecards. Each department may realize a short-term ROI. However, the combination will be suboptimal and is not likely to lead to an overall ROI. Both the CPR dashboard and the federal IT dashboard show that it is possible to create an organizationwide initiative spanning multiple domains.

• Continuous Improvement. CPR is based on trends, not targets. This means that the performance indicators are aimed at continuous improvement instead of static goals. It sets a good example for how an operational ortactical dashboard should look.

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Chapter 10: Collaborative Computer-Supported Technologies and Group Support Systems

Learning Objectives for Part IV 1. Understand the concepts and process of groupwork 2. Describe how IT-based collaboration and communication support groupwork in general and decision making in particular 3. Understand the fundamental principles and capabilities of groupware and group support systems (GSS) 4. Describe the fundamental principles and capabilities of knowledge management (KM) 5. Describe tools of KM and how they relate to decision support 6. Relate KM, collaboration, and communication to each other Millions of people and thousands of organizations worldwide use the decision support and business intelligence (BI) concepts and tools described in Chapters 1 through 9 to successfully support their decision making. However, individual decision makers do not work in a vacuum. Typically, groups (or teams) of people work and make decisions together. Very effective computerized methods have evolved to support the complex situations and different settings of work groups. Part IV describes collaborative computing in several situations and settings, supporting people in one room and supporting people in different locations (see Chapter 10). The principles of KM are the subject of Chapter 11. Group decision support systems (GDSS) were the first specialized form of collaborative decision making supported by computers. Today, many group support system (GSS) technologies can facilitate electronic meetings, including virtual meetings. Many organizations now routinely use GSS, even in asynchronous modes (i.e., different times and different places), usually over the Web, for either direct or indirect support of decision making. Similarly, KM can be considered a form of enterprise-wide collaborative computing paradigm that identifies and makes available the needed

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knowledge of an organization in a meaningful form to anyone, anyplace, and anytime.

The Web is the platform that enables most collaborative support systems, including sharing of data, information, and knowledge and making decisions.

Learning Objectives for Chapter 10 1. Understand the basic concepts and processes of groupwork, communication, and collaboration 2. Describe how computer systems facilitate communication and collaboration in an enterprise 3. Explain the concepts and importance of the time/place framework 4. Explain the underlying principles and capabilities of groupware, such as group support systems (GSS) 5. Understand the concepts of process gain, process loss, task gain, and task loss and explain how GSS introduces, increases, or decreases each of them 6. Describe indirect support for decision making, especially in synchronous environments 7. Become familiar with the GSS products of the major vendors, including Lotus, Microsoft, WebEx, and Groove 8. Understand the concept of GDSS and describe how to structure an electronic meeting in a decision room 9. Describe the three settings of GDSS 10. Describe specifically how a GDSS uses parallelism and anonymity and how they lead to process/task gains and losses 11. Understand how the Web enables collaborative computing and group support of virtual meetings 12. Describe the role of emerging technologies in supporting collaboration 13. Define creativity and how it can be facilitated by computers

CHAPTER OVERVIEW People work together, and groups make most of the complex decisions in organizations.

The increase in organizational decision-making complexity increases the need for meetings and groupwork. Supporting groupwork, where team members may be in different locations and working at different times, emphasizes the important aspects of communications, computer-mediated collaboration, and work methodologies. Group support is a critical aspect of decision support systems (DSS). Effective computersupported group support systems have evolved to increase gains and decrease losses in task performance and underlying processes. In addition, creativity is an important element of decision making that collaborative computing can nurture and enhance. The sections of this chapter are as follows:

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CHAPTER OUTLINE 10.1

OPENING VIGNETTE: PROCTER & GAMBLE DRIVES IDEATION WITH GROUP SUPPORT SYSTEMS A. PROBLEM B. SOLUTION 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) ⧫ Application Case 10.2: GSS Boosts Innovation in Crime Prevention B. GROUPWARE C. TIME/PLACE FRAMEWORK D. 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 ⧫ Application Case 10.4: Catalyst Maintains an Edge with WebEx 1.

Electronic Teleconferencing 3.

Video Teleconferencing 4.

Web Conferencing 5.

Interactive Whiteboards a.

Screen Sharing b.

Instant Video C. SUPPORT OF ASYNCHRONOUS COMMUNICATION

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1.

Online Workspaces  Section 10.4 Review Questions

10.5

INTEGRATED GROUPWARE SUITES A. Lotus Notes (IBM Collaboration Software) B. Microsoft Windows Meeting Space and Live Meeting C. GROOVE NETWORKS D. WEBEX MEETING CENTER AND PLACEWARE CONFERENCE CENTER E. GROUPSYSTEMS F. OTHER VENDORS ⧫ Application Case 10.5: Wimba Extends Classrooms at CSU Chico 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 C. HOW GDSS (OR GSS) IMPOVES GROUPWORK ⧫ Technology Insights 10.6: Modeling in Group Decision Making: EC11 for Groups ⧫ Technology Insights 10.7: Collaborative Problem Solving at KUKA D. FACILITIES FOR GDSS 1.

Decision Rooms ⧫ Application Case 10.8: 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

10.7

PRODUCTS AND TOOLS FOR GDSS/GSS AND SUCCESSFUL IMPLEMENTATION A. ORGANIZING A GSS SESSION B. THINKTANK BY GROUPSYSTEMS ⧫ Technology Insights 10.9: The Standard GHSS Process 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

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1.

B. C. D.

E. F. G.

10.9

The Benefits of VoIP ⧫ Technology Insights 10.10: VoIP Systems Help Increase Productivity and Enhance Learning Experiences at the State University of New York (SUNY) COLLABORATIVE WORKFLOW WEB 2.0 WIKIS 1.

Wikilog 2.

Commercial Aspects of Wikis and Their Derivatives COLLABORATION HUBS COLLABORATIVE NETWORKS CORPORATE (ENTERPRISE) PORTALS  Section 10.8 Review Questions

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. COLLECTIVE INTELLIGENCE C. PROJECT MANAGEMENT ⧫ Application Case 10.11: 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 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

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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 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, 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.

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.)

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ANSWERS TO END OF SECTION REVIEW QUESTIONS

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     

Section 10.1 Review Questions 1. What led P&G to implement group support systems?

P&G needed more efficient ways to design and produce innovative and profitable products to stay competitive. They also wanted to meet and exceed customers’ expectations by creating new and improved products, which has been the case for many consumer goods companies in the past.

2. What are the pros and cons of using computer-mediated collaboration systems?

A computer-mediated group support system not only makes the innovation process faster, but much, much better. A con is that people may not want to contribute their best ideas if they think that won’t get credit for their ideas.

3. What is innovation Gym? What is it used for, and who uses it?

P&G called its new innovation infrastructure the “innovation Gym.” The company’s innovation Gym, located near its Cincinnati headquarters, serves as an incubator for a wide range of innovations.

4. What are the benefits of the innovation Gym? Can you think of other benefits that are not mentioned in the vignette?

Established by chairman and CEO, the facility and the infrastructure bring together employees and consumers for true collaboration and innovation.

Students’ answers to the second question will differ.

5. Who are the “internal clients” that the vignette refers to? How can they benefit from the Gym? Internal “clients” that come to P&G’s facilitation team for assistance in planning and executing meetings are P&G’s own product managers and other employees.

They can benefit by knowledge sharing of best and worst practices, innovation, and sharing their expertise. They also benefit from the power of anonymity, fast ideation, and reducing post-meeting busywork.

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6. Discuss the difficulties associated with sharing and collaboration in two processes, one with computer-mediations technology and other without.

Challenges with computer-mediations technology: group members may be located in different time zones and not know each other well enough to share or trust each other; there are technology glitches that could interrupt the process.

Challenges without computer-mediations technology: People may be afraid to speak up; meetings and idea sharing take longer; fewer options make be evaluated or not evaluated in sufficient depth.

Section 10.2 Review Questions 1.

Define groupwork.

Groupwork is “work done by two or more people together.”

2. List five characteristics of groupwork.

The text 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.

• 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.

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3. Describe the process of a group meeting for decision making.

The text 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 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).

• 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.

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• •

A group may have better and more precise communication working together.

Risk propensity is balanced. Groups moderate high-risk takers and encourage conservatives.

5. Describe five potential losses of group meetings.

The text 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.

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Define and provide simple examples to three coordination levels in groupwork.

See Table 10.1, p 424

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 limitations do computers have 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 work-around such as “smilies” (more formally emoticons), increased use of video, and new technologies. They also do not do well at communicating language subtleties and cross-cultural 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.

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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.”

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.

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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.”

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.

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

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”.

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. Students may be attracted to this list to answer this question. This would be incorrect, since activities are not benefits.

3.

Define EMS. 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).

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.

(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.)

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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. 452: • 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.

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Computer software can provide call management features that surpass those of most telephone carriers. The text lists these benefits, originally from a Siemens Communications ad.

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 communications-enable different business processes • Enables optimized conferencing tools to replace business travel For users: • 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 2.

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,

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and so on. Creating a 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 supplychain 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.

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Section 10.10 Review Questions 1.

Define creativity.

“The human trait that leads to the production of acts, items, and instances of novelty.”

“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.

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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.

ANSWERS TO QUESTIONS FOR DISCUSSION

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1. 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

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• Shared whiteboard, smart whiteboard • Instant video • Brainstorming • 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

5. 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.

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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.

10. 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:

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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.

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

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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.)

18. 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.

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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 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.)

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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.

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.

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Chapter 11: Knowledge Management

Learning Objectives for Chapter 11 1.

2.

3.

4.

5.

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) 6. Describe different approaches to knowledge management 7. Describe the activities of the chief knowledge officer and others involved in knowledge management 8. Describe the role of knowledge management in organizational activities 9. Describe ways of evaluating intellectual capital in an organization 10. Describe how KMS are implemented 11. Describe the roles of technology, people, and management in knowledge management 12. Describe the benefits and drawbacks of knowledge management initiatives 13. Describe how knowledge management can revolutionize the way an organization functions

CHAPTER OVERVIEW In this chapter, we describe the characteristics and concepts of knowledge management.

In addition, we explain how firms use information technology (IT) to implement KM systems and how these systems are transforming modern organizations. Knowledge management, although conceptually ancient, is a relatively new business philosophy. The goal of knowledge management is to identify, capture, store, maintain, and deliver useful knowledge in a meaningful form to anyone who needs it, anyplace and anytime, within an organization. Knowledge management is about sharing and collaborating at the organization level. Knowledge management has the potential to revolutionize the way we share expertise, make decisions, and conduct business, as discussed in the following sections:

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CHAPTER OUTLINE 11.1

OPENING VIGNETTE: MITRE 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 ⧫ Application Case 11.1: KM at Consultancy Firms C. EXPLICIT AND TACIT KNOWLEDGE ⧫ Application Case 11.2: 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 ⧫ Application Case 11.3: NASA Blends KM with Risk Management  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 11.4 Texaco Drills for Knowledge C. HYBRID APPROACHES TO KNOWLEDGE MANAGEMENT ⧫ Technology Insights 11.5: KM: A Demand-led Business Activity D. BEST PRACTICES E. KNOWLEDGE REPOSITORIES 1.

Developing a Knowledge Repository

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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 2.

Intelligent Agents 3.

Knowledge Discovery in Databases 4.

Extensible Markup Language (XML) 5.

Web 2.0  Section 11.6 Review Questions

11.7

KNOWLEDGE MANAGEMENT SYSTEMS IMPLEMENTATION A. KNOWLEDGE MANAGEMENT PRODUCTS AND VENDORS 1.

Software Development Companies and EIS Vendors 2.

Knowledge Servers ⧫ Application Case 11.6: 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 11.7: Online Knowledge Sharing at Xerox D. KMS DEVELOPERS ⧫ Technology Insights 11.8: Seven Principles for Designing Successful COP E. KMS STAFF  Section 11.8 Review Questions

11.9

ENSURING THE SUCCESS OF KNOWLEDGE MANAGEMENT EFFORTS A. KNOWLEDGE MANAGEMENT SUCCESS STORIES

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Technology Insights 11.9: MAKE: Most Admired Knowledge Enterprises ⧫ Application Case 11.10: The British Broadcasting Corporation Knowledge Management Success 1.

Expert Location Systems ⧫ Application Case 11.11: How the U.S. Department of Commerce Uses an Expert Location System ⧫ Technology Insights 11.12: Six Keys to KM Success for Customer Service C. KNOWLEDGE MANAGEMENT VALUATION 1.

Financial Metrics for Knowledge Management Valuation 2.

Nonfinancial Metrics for Knowledge Management Valuation D. KNOWLEDGE MANAGEMENT FAILURE ⧫ Technology Insights 11.13: KM Myths ⧫ Application Case 11.14: When KMS Fail, They Can Fail in a Big Way A. FACTORS THAT LEAD TO KNOWLEDGE MANAGEMENT SUCCESS ⧫ Technology Insights 11.15: Knowledge Management Traps B. POTENTIAL DRAWBACKS OF KMS C. 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 self-contained.) 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

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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, 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 11.8 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).

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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

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Section 11.1 Review Questions 1. What is MITRE, and why is KM critical to its success?

The MITRE Corporation was founded in 1958 to address the government’s need to create the Semi-Automated Ground Environment (SAGE), an integrated system to defend the United States against the threat of Soviet air attacks. Since then, MITRE has been serving as an objective, nonprofit corporation whose mission is to serve U.S. interests by creating solutions to pervasive, cross-organizational problems facing the federal government in civil aviation, tax administration, and national security. Its mission statement reads: “As a public interest company, in partnership with the government, MITRE addresses issues of critical national importance, combining systems engineering and information technology to develop innovative, viable solutions that make a difference.” Frequently, this means enabling innovation, integration, and collaboration within and across public sector agencies, requiring efficient and effective knowledge management.

As demand for its services increased, MITRE felt the need to better utilize its intellectual assets and started to develop KM systems. Over time, MITRE’s organizational culture changed from the “culture of silos” to the “culture of sharing” as a number of successful KM initiatives and systems were developed.

2. What problems led to MITRE’s explorations of KM solutions?

It is common for large, knowledge-intensive corporations to develop a culture of silos, each with its own pocket of knowledge. Over time, these silos start to function like rivals, compromising an organization’s abilities. This is what MITRE was experiencing that led to the MII initiative in 1996.

3. Describe MITRE’s holistic approach to KM. Discuss the value of individual layers and concepts. Is there anything in this approach that you do not agree with?

Figure 11.1 Page 473, illustrates a comprehensive approach to knowledge management (similar to the one adopted at MITRE), which can be viewed as integrating strategies, processes, and technologies that enable the enterprise to

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acquire, create, share, and make actionable knowledge needed to achieve its mission.

4. Describe the benefits of MITRE’s KM systems. Can you think of other benefits (tangible or intangible) that are not mentioned in the case?

Since 1995, MITRE has invested millions of dollars in a variety of KM systems; in return, it has reaped an order-of-magnitude ROI in reduced operations cost and improved productivity. According to corporate executives, before the KM initiatives MITRE was successful at what it was doing, but now, with KM, they are very successful, because they can leverage the collective knowledge of the company for every project.

5. Explain how new Internet technologies (such as Web 2.0) can further enable the KM system at MITRE. Web 2.0 technologies can enable any time, any place knowledge sharing and contributions by providing a familiar and easy-to-use platform. Students will have their own opinions based on the Web 2.0 technologies they use.

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.)

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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 “knowwhat” 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. 480) 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.

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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, 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.”(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.

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.

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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. .

2.

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.

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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.

4.

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.

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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. 493 and shown graphically on p. 494: 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.

3.

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

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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 • 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

3.

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

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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 management-related 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

2.

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

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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.

3.

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.

These include (but are not necessarily limited to) the following: • Customer goodwill • External relationship capital • Structural capital • Human capital • Social capital • Environmental capital

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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: • 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.

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

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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.

4.

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.

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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.

12.

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.

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• • • •

13.

19

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.

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 open-ended.

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.

(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

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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. 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 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. Additional indications 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-thanideal 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

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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.

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.

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Chapter 12: Artificial Intelligence and Expert Systems

Learning Objectives for Part V 1. Understand the foundations, definitions, and capabilities of artificial intelligence and knowledge-based systems 2. Learn what expert systems are and understand how expert systems (ES) are used to develop intelligent decision support systems (DSS).

3. Understand how case-based reasoning (CBR) is used to develop intelligent systems.

4. Understand how genetic algorithms are used to develop intelligent systems 5. Understand how fuzzy logic (FL) can be used to develop intelligent systems 6. Understand how support vector machines (SVM) are used to develop intelligent systems.

7. Learn what intelligent software agents (ISA) are and understand how they can be used to develop intelligent systems.

In this part, we focus on using artificial intelligence techniques to develop intelligent systems for decision support. These systems include rule-based expert systems (ES) and a new generation of advanced intelligent systems that use artificial neural networks, support vector machines, genetic algorithms, case-based reasoning, and intelligent software agent techniques. In this part, we first present an overview of artificial intelligence followed by a detailed explanation of rule-based expert systems (in Chapter 12). Chapter 13 describes advanced intelligent systems that are commonly used in the commercial world, including support vector machines, case-based reasoning, genetic algorithms, fuzzy logic, and intelligent software agents.

Learning Objectives for Chapter 12 1.

2.

3.

4.

5.

6.

7.

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 Learn the knowledge engineering process used to build ES Explain the benefits and limitations of rule-based systems for decision support Identify proper applications of ES

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8. Learn about tools and technologies for developing rule-based DSS

CHAPTER OVERVIEW In addition to the use of data and mathematical models, some managerial decisions require qualitative information and the judgmental knowledge that resides in the minds of human experts. Therefore, it is necessary to find effective ways to incorporate such information and knowledge into decision support systems (DSS). A system that integrates knowledge from experts is commonly called a knowledge-based decision support system (KBDSS) or an intelligent decision support system (IDSS). A KBDSS can enhance the capabilities of decision support not only by supplying a tool that directly supports a decision maker, but also by enhancing various computerized DSS environments. The foundation for building such systems is the techniques and tools that have been developed in the area of artificial intelligence—rule-based expert systems being the primary one. This chapter introduces the essentials of artificial intelligence and provides a detailed description of expert systems.

CHAPTER OUTLINE 12.1

OPENING VIGNETTE: A WEB-BASED EXPERT SYSTEM FOR WINE SELECTION 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 Grand 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 ⧫ Technology Insights 12:2: Artificial Intelligence Versus Natural Intelligence 1.

Expert Systems 2.

Natural Language Processing

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3. ⧫ 4.

5.

6.

7.

8.

9.

10.

11.

12.

13. ⧫

Speech (Voice) Understanding Application Case 12.3: Automatic Speech Recognition in Call Centers Robotics and Sensory Systems Computer Vision and Scene Recognition Intelligent Computer-Aided Instruction Automatic Programming Neural Computing Game Playing Language Translation Fuzzy Logic Genetic Algorithms Intelligent Agents Application Case 12.4: 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 ⧫ Application Case 12.5: Sample Sessions of a Rule-Based ES ⧫ Application Case 12.6: Expert System Help in Identifying Sport Talent  Section 12.4 Review Questions

12.5

APPLICATIONS OF EXPERT SYSTEMS ⧫ Application Case 12.7: Sample Applications of ES A. CLASSICAL APPLICATIONS OF ES 1.

DENDRAL 2.

MYCIN 3.

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

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E. F. G. ⧫

BLACKBOARD (WORKPLACE) EXPLANATION SYSTEM (JUSTIFIER) KNOWLEDGE-REFINING SYSTEM Application Case 12.8: A Fashion Mix-and-Match Expert System  Section 12.6 Review Questions

12.7

KNOWLEDGE ENGINEERING A. KNOWLEDGE ACQUISITION ⧫ Technology Insights 12.9: Difficulties in Knowledge Acquisition B. KNOWLEDGE VERIFICATION AND VALIDATION C. KNOWLEDGE REPRESENTATION 1.

Production Rules 2.

Knowledge and Inference Rules D. INFERENCING 1.

Forward and Backward Chaining Example 2.

Inferencing with Uncertainty 3.

Combining two or more Rules E. EXPLANATION AND JUSTIFICATION 1.

Why Explanations 2 How Explanations  Section 12.7 Review Questions

12.8

PROBLEM AREAS SUITABLE FOR EXPERT SYSTEMS ⧫ Application Case 12.10: Monitoring Water Quality with Sensordriven Expert Systems  Section 12.8 Review Questions

12.9

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 CRITICAL 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

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Section 12.10 Review Questions

12.11 EXPERT SYSTEMS ON THE WEB ⧫ Application Case 12.11: Banner with Brains: Web-Based ES for Restaurant Selection ⧫ Application Case 12.12: Rule-Based System for Online Student Consultation  Section 12.11 Review Questions ⧫ Technology Insights 12.12: Automated and Real-Time Decision Systems

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

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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. 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. 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 way1, 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!

1

Unless the rules were written to depend on the order in which a shell processes rules. This is usually a poor idea.

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ANSWERS TO END OF SECTION REVIEW QUESTIONS

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     

Section 12.1 Review Questions 1. Describe MenuVino’s motivation for developing the Web-based expert system.

MenuVino, Inc., a Web-based wine retailer, has developed several online knowledge automation expert systems to provide expert advice on wine selection.

The systems analyze a Web site visitor’s individual flavor preferences to develop a personal taste profile in order to recommend wines that the customer is more likely to enjoy. The systems also match wines to particular meals as well as to food-related details such as ingredients, preparation methods, sauces, and so on.

The advising expert systems are part of a commercial site aimed at matching users with their ideal wines.

2. Do you think that expert systems are a good fit for this application domain? Explain.

What other application domains may have similar needs?

With the help of artificial intelligence (especially with expert systems), many different types of specialized knowledge and experience can be extracted and represented in a computer. When the experts and expertise are hard to find, such an automated system can be very useful.

3. What major difficulties might emerge in the process of developing such an expert system? How can they be overcome?

Selecting the right wine for a given situation requires significant amount of expertise. People often buy a wine without knowing its taste, whether it suits their taste preferences, or if it is in harmony with the intended meal or occasion. Most non-experts pick a wine based on price (higher is better!) and high-level classification (red for meat and white for fish, etc.). Beyond this simple classification schema, most people are clueless what it is that they are buying.

Everyone agrees that it is preferable to savor the wines we genuinely like and appreciate. However, it is difficult to form an opinion unless you have tasted them all, which is an impossible proposition if you are buying them over the Web. Tastes are very personal to individuals, and to discover yours may be difficult even if you have the opportunity to try them all. Even then, they tend to change based on other factors such as the occasion, meal, mood, and so on.

4. How is the system described in this case different from traditional DSS tools and techniques described in previous chapters?

Unlike traditional DSS tools, in addition to the use of data and mathematical models, this system decisions require qualitative information and the judgmental knowledge that resides in the minds of human experts.

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5. Can you think of alternative tools, technologies, or solutions to the problem described in the case? How do they compare to the presented expert system solution?

Making such specialized knowledge accessible to many users in an automated and interactive environment has great potential to boost the utility and profitability of many other specialized business applications.

Section 12.2 Review Questions 1.

What is artificial intelligence?

The book offers two definitions, 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. 537: (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

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most people). Answers may also be on the list of AI application areas beginning on p. 538: (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. 535) 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 artificial intelligence application.

Review Technology Insights 12.2 Page 537. Also see the Opening Vignette as an example.

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 12.3, p. 539.)

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6.

Define Natural Language Processing and describe an NLP application.

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. 542).

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.

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What is expertise? Provide an example of expertise.

Expertise is “the extensive, task-specific knowledge that experts possess” (p.

543).

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 is MYCIN problem domain?

MYCIN was designed to diagnosis of bacterial blood infections.

2.

Name two applications of ES in finance and describe their benefits.

Open-ended answer, which depends on the applications each student or group finds.

3.

Name two applications of ES in marketing and describe their benefits.

Open-ended answer, which depends on the applications each student or group finds.

4.

Name 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.

The development environment is used by (or in conjunction with) the expert to introduce knowledge into an expert system’s knowledge base.

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

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-then-else) 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. 491, 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.

State 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.

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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 goal-directed 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.

4. List three most popular methods to deal with uncertainty in ES. • •

•

Evaluation is a broad concept. Its objective is to assess an ES’s overall value. In addition to assessing acceptable performance levels, it analyzes whether the system would be usable, efficient, and cost-effective.

Validation is the part of evaluation that deals with the performance of the system (e.g., as it compares to the expert’s). Simply stated, validation is building the right system (i.e., substantiating that a system performs with an acceptable level of accuracy).

Verification is building the system right or substantiating that the system is correctly implemented to its specifications.

5. Why do we need to incorporate uncertainty in ES solutions?

In the realm of ES, these activities are dynamic because they must be repeated each time the prototype is changed. In terms of the knowledge base, it is necessary to ensure that the right knowledge base (i.e., that the knowledge is valid) is used. It is also essential to ensure that the knowledge base has been constructed properly (i.e., verification).

6. What are the ways by which ES justify its knowledge This feature offers a means of evaluating the integrity of the system when it is to be used by the experts themselves. Two basic types of explanations are the why and the how. Metaknowledge is knowledge about knowledge. It is a structure within the system using the domain knowledge to accomplish the system’s problem solving strategy. Explanation in rule-based ES is usually associated with a way of tracing the rules that are fired during the course of a problem-solving session. It uses “Why” and “How” explanations (p. 563).

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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. 566): 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.

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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. 569–570 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.

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• • • • • • • •

•

3.

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.)

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.)

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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.

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. .

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

It is said that powerful computers, inference capabilities, and problem-solving heuristics are necessary but not sufficient for solving real problems. Explain.

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.

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.

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(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)

8.

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.

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.)

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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 at 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

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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.

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Chapter 13 Advanced Intelligent Systems

Learning Objectives for Chapter 13 1.

2.

3.

4.

5.

Understand machine-learning concepts Learn the concepts and applications of case-based systems Understand the concepts and applications of genetic algorithms Understand fuzzy logic and its applications in designing intelligent systems Understand the concepts behind support vector machines and their applications in developing advanced intelligent systems 6. Understand the concepts behind intelligent software agents and their use, capabilities, and limitations in developing advanced intelligent systems.

7. Explore integrated intelligent support systems

CHAPTER OVERVIEW In addition to rule-based expert systems, several other advanced techniques are available for designing intelligent information systems. These include case-based reasoning systems, genetic algorithms, fuzzy logic and fuzzy inference systems, support vector machines, and intelligent agents. A case-based reasoning system contains a large repository of historical cases that represents unique past experiences. Genetic algorithms mimic the natural process of evolution to help find solutions to complex problems.

Fuzzy logic (and fuzzy inference systems) creates a bridge between symbolic reasoning and mathematical calculation to improve decision performance in uncertain problem situations. Support vector machines are becoming increasingly more popular prediction systems for complex real-world problems. Intelligent software agents are the key enabling technology for the next generation of intelligent information systems distributed over the Internet. The concepts and motivating applications of these advanced techniques are described in this chapter, which is organized into the following sections:

CHAPTER OUTLINE 13.1 OPENING VIGNETTE: MACHINE LEARNING HELPS DEVELOP AN AUTOMATED READING TUTORING TOOL A. PROBLEM B. SOLUTION

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C. D.

RESULTS  Questions for the Opening Vignette 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 A. THE BASIC IDEA OF CBR B. THE CONCEPT OF A CASE IN CBR C. THE PROCESS OF CBR D. BENEFITS AND USABILITY OF CBR E. ISSUES AND APPLICATIONS OF CBR ⧫ Application Case 13.1 A CBR System for Optimal Selection and Sequencing of Songs F. SUCCESS FACTORS FOR CBR SYSTEMS G. TOOLS FOR BUILDING CBR  Section 13.3 Review Questions

13.4

GENETIC ALGORITHM AND DEVELOPING GA APPLICATIONS A. EXAMPLE: THE VECTOR GAME: 1.

Description of the Vector Game 2.

Default Strategy: Random Trial and Error 3.

Improved Strategy: Use of Genetic Algorithms A. TERMINOLOGY OF GENETIC ALGORITHMS B. HOW DO GENETIC ALGORITHM WORK? 1.

Limitations of Genetic Algorithms 2.

Genetic Algorithm Applications ⧫ Application Case 13.2 Genetic Algorithms Schedule Assembly Lines at Volvo Trucks North America ⧫ Technology Insights 13.3 Genetic Algorithm Software  Section 13.4 Review Questions

13.5

FUZZY LOGIC FUNDAMENTALS A. FUZZY INFERENCE SYSTEM B.

13.6

FUZZY LOGIC APPLICATIONS 1.

Fuzzy Logic Applications for Manufacturing and Management 2.

Fuzzy Logic Applications in Business: Examples  Section 13.5 Review Questions

SUPPORT VECTOR MACHINES) A. HOW DOES SVM WORK?

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B. C. D.

E.

F. G. H.

13.7

3

IMPLEMENTATION KERNEL TRICK THE PROCESS OF BUILDING SVM 1.

Numerisizing the Data 2.

Normalizing the Data 3.

Select the Kernel Model APPLICATIONS OF SVM 1.

Diagnosing Nerve Disease 2.

Indentifying the Winner of Competitive Events 3.

Analyzing Metabolite Concentrations SUPPORT VECTORMACHINES VERSUS ARTIFICAL NEURAL NETWORKS BENEFITS OF SVM DISADVANTAGES OF SVM  Section 13.6 Review Questions

INTELLIGENT AGENTS A. INTELLIGENT AGENTS: A BRIEF HISTORY AND DEFINITIONS B. COMPONENTS OF AN AGENT C. CHARACTERISTICS OF INTELLIGENT AGENTS 1.

Autonomy (Empowerment) 2.

Communication and Collaboration (Interactivity) 3.

Automating Repetitive Tasks (Consistency) 4.

Reacting to Situations (Reactivity) 5.

Being Proactive 6.

Having Temporal Continuity 7.

Developing Personality 8.

Operating in the Background (Mobility) 9.

Learning from Experience (Intelligence) ⧫ Technology Insights 13.4 Intelligent Agents, Objects, and ES D. WHY USE INTELLIGENT AGENTS? E. CLASSIFICATION OF INTELLIGENT AGENTS 1.

Classification by Application Type 2.

Classification by Characteristics F. LEADING INTELLIGENT AGENT RESEARCH PROGRAMS  Section 13.7 Review Questions

13.8

DEVELOPING INTEGRATED ADVANCED SYSTEMS A. FUZZY NEURAL NETWORKS ⧫ Application Case 13.5 International Stock Selection B. GENETIC ALGORITHMS AND NEURAL NETWORKS ⧫ Application Case 13.6 Hybrid ES and Fuzzy Logic System Dispatches Trains  Section 13.8 Review Questions Chapter Highlights

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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, (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 6), one (statistical regression) is usually a topic for other courses, and some of the others are not discussed at all.

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.

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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.

ANSWERS TO END OF SECTION REVIEW QUESTIONS

     

Section 13.1 Review Questions 1. What is literacy and why does it have a global importance?

Literacy is the ability to identify, understand, interpret, create, and communicate printed materials associated with varying contexts. The United Nations recognizes literacy as a human right, noting that basic education, of which literacy is the key learning tool, was recognized as a human right over 50 years ago in the Universal Declaration of Human Rights.

2. How can information systems help in overcoming illiteracy?

Researchers from Carnegie Mellon University are developing automated reading tutors using a combination of machine-learning techniques.

3. What types of machine-learning technologies can be used in improving reading skills?

Speech-recognition-based, computer-guided oral reading technologies/ 4. What do you think are the characteristics of an expert reading tutor that should be closely represented in an automated computer tool like the reading tutor?

Speech recognition, natural language processing, expert knowledge representation, and an inference engine can create an automated tutor, which should be modeled after the ideal learning process guided by the expert reading tutors. The expert reading tutor should respond like a human tutor and motivate by giving praise and encouraging the reader.

Section 13.2 Review Questions 1.

Define machine learning.

“Is a family of artificial intelligence technologies that is primarily concerned with the design and development of algorithms that allow computers to learn based on historical data” (p. 583). Students may find other correct definitions in other sources.

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Decision Support and Business Intelligence Systems (9th Edition) Instructor’s Manual

What is learning?

“A process of self-improvement and thus an important feature of intelligent behaviors” (p. 583). 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 Figure 13.1 on p. 584 lists eight areas.. 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.

What is CBR? What problems is CBR most suitable for?

A machine learning method that “adapts solutions used to solve old problems for use in solving new problems” (p. 585). CBR is based on the premise that new problems are often similar to previously encountered problems, and, therefore, past successful solutions may be of use in solving the current situation.

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.

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List five benefits of CBR. The bullet list on p. 588-589 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 current and future 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 bullets on pp. 591–592 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).

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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.

593) There are also definitions in sources such as http://en.wikipedia.org/wiki/Genetic_algorithm

2.

Describe the evolution process in genetic algorithms. How is it similar to the biological 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.

4. List major areas of genetic algorithm application.

Application areas for genetic algorithms noted in the text (p. 599) 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,

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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.

5. Describe in detail three genetic algorithm applications.

Open-ended answer. One application is described in sufficient detail to answer this question in Application Case 13.2 (p. 600). Others can be found in the references preceding that case or by a Web or library search.

6. 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 13.5 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.

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2. What are the basic premises behind the fuzzy logic approach?

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. 604 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, anti-lock 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 13.6 Review Questions 1.

Define support vector machines and describe its characteristics.

Support vector machines (SVM) are one of the most popular machine-learning techniques. They belong to the family of generalized linear models, which

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achieve a classification or regression decision based on the value of the linear combination of input features. Because of their architectural similarities, SVM are also closely associated with feed forward-type artificial neural networks.

2.

How does SVM work? What types of problems can SVM be applied to?

Following a machine-learning process, SVM learns from the historic cases (represented as data points) to build a mathematical model that can optimally assign the data instances into respective classes.

3.

What are the steps in building SVM solutions?

What follows is a simple recipe (a step-by-step process) to develop SVM-based prediction models that are more likely to produce better results: 1. Preprocess the data. a. Scrub the data.

• Deal with the missing values.

• Deal with the presumably erroneous values.

• Deal with the noisy data. b. Transform the data.

• Numerisize the data • Normalize the data 2. Develop the model. a. Select the kernel type (RBF is often a natural choice). b. Determine the kernel parameters based on the selected kernel type (e.g., C and γ for RBF). This is a difficult decision, because there is no universally optimal choice here. One should consider using cross-validation and experimentation to determine the most appropriate values for these parameters. c. If the results are satisfactory, finalize the model, otherwise change the kernel type and/or kernel parameters to achieve the desired accuracy level.

3. Extract and deploy the model.

See Figure 13.10, p. 609, for a pictorial representation.

4.

What are the major advantages of SVM? What are its major limitations?

Benefits of SVM The following are some of the major benefits of SVM: • They differ radically from comparable approaches, such as neural networks.

SVM always find a global minimum, and their simple geometric interpretation provides fertile ground for further investigation.

• Because the SVM approach “automatically” solves the network complexity problem, the size of the hidden layer is obtained as the result of the quadratic programming procedure. Hidden neurons and support vectors correspond to each

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other, so the center problems of the RBF network are also solved, because the support vectors serve as the basis function centers.

• In problem situations where linear decision hyperplanes are not feasible, an input space is mapped into a feature space (corresponding to the hidden layer in ANN models), resulting in a nonlinear classifier.

• SVM, after the learning stage, create the same type of decision hyper surfaces as do some well-developed and popular ANN classifiers. Note that the training of these diverse models is different. However, after the successful learning stage, the resulting decision surfaces are identical.

• Unlike conventional statistical and neural network methods, the SVM approach does not attempt to control model complexity by limiting the number of features.

• Classical learning systems such as neural networks suffer from their theoretical weakness (e.g., back propagation usually converges only to locally optimal solutions). SVM can provide a significant improvement.

• In contrast to neural networks, SVM automatically select their model size (by selecting the support vectors).

• The absence of local minima from the algorithms marks a major departure from traditional systems such as neural networks.

• Although the weight decay term is an important aspect for obtaining good generalization in the context of neural networks for regression, the margin plays a somewhat similar role in classification problems.

• In comparison with traditional multilayer perceptron neural networks that suffer from the existence of multiple local minima solutions, convexity is an important and interesting property of nonlinear SVM classifiers.

Disadvantages of SVM SVM are not immune to problems and limitations. Following are some of the most commonly observed ones: • One of the biggest limitations of SVM lies in the choice of the kernel.

Determination of the best kernel type for a specific dataset may require a laborious trial-and-error approach, especially when the dataset is rather large with a lot of variables.

• Speed and size are a problem, both in training and testing. SVM models tend to take more time to develop, compared to their counterparts.

• Development of discrete data is a time-consuming and tedious task.

• The optimal design for multi-class SVM classifiers is an ongoing research effort.

• Although SVM have good generalization performance, they can be abysmally slow in test phase, making them less desirable for situations requiring a quick response.

• Another important practical question that remains unsolved is the optimal selection of the kernel function parameters.

• From a practical point of view, perhaps the most serious problem with SVM is the high algorithmic complexity and extensive memory requirements of the quadratic optimization programming in large-scale modeling tasks.

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List and discuss some applications of SVM. DIAGNOSING NERVE DISEASE Polat et al. (2009) compared several classifier algorithms, including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM), and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from electroretinography signal patterns. Electroretinography signal patterns were obtained from 106 subjects who had optic nerve and/or macular disease. In order to show the unbiased test performance of the classifier algorithms, a number of measures were used, including classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, a confusion matrix, and 10-fold crossvalidation. The classification results obtained were 81.82, 85.9, and 100 percent for the AIRS classifier, C4.5 decision tree classifier, and the LS-SVM classifier, respectively, using a 10-fold cross-validation methodology. The study showed that the SVM classifier was significantly better than the other two classifiers in correctly diagnosing macular and optic nerve diseases. It has also proven to be a robust and efficient classifier system.

IDENTIFYING THE WINNER OF COMPETITIVE EVENTS Use of predictive modeling for the identification of winners in competitive events (e.g., games, races, elections, etc.) has been an interesting and exceptionally challenging problem for researchers and practitioners alike. A study by Lessmann et al. (2009) used prediction modeling to identify the winners at horse races. The goal of such modeling is to appraise the informational efficiency of betting markets. The prevailing approach involves forecasting the runners’ finish positions by means of discrete or continuous-response regression models.

However, theoretical considerations and empirical evidence suggest that the information contained within finish positions might be unreliable, especially among minor placements. Lessmann et al. proposed a classification-based modeling paradigm to alleviate this problem that relied only on data distinguishing winners and losers. To assess its effectiveness, an experiment was conducted using data from a UK racetrack. The results demonstrated that their SVM-based classification model compared favorably with the state-of-the-art alternatives and confirmed the reservations of relying on rank-ordered finishing data. Simulations conducted to further explore the origin of the model’s success evaluated the marginal contribution of its constituent parts.

ANALYZING METABOLITE CONCENTRATIONS Metabolomics, the systematic study of the unique chemical fingerprints that specific cellular processes leave behind, is an emerging field that is providing invaluable insight into physiological processes. It is an effective tool to investigate disease diagnosis or conduct toxicological studies by observing changes in metabolite concentrations in various biological fluids.

Traditionally, multivariate statistical analysis has been employed with nuclear magnetic resonance (NMR) or mass spectrometry (MS) data to determine differences between groups (e.g., diseased versus healthy individuals).

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Mahadevan et al. (2008) thought it might be possible to build distinctive predictive models based on a set of training data that are able to predict whether new data falls into a specific class. They obtained metabolomic data by performing NMR spectroscopy on urine samples obtained from healthy subjects (both male and female) and patients suffering from Streptococcus pneumonia, a human pathogenic bacterium recognized as a major cause of pneumonia.

They compared the performance of traditional partial-least-squared discriminant analysis (PLS-DA) to support SVM on two case studies: (1) a case where nearly complete distinction can be seen (healthy versus pneumonia) and (2) a case where distinction was more ambiguous (male versus female). They showed that SVM were superior to PLS-DA in both cases in terms of predictive accuracy using the least number of features. SVM were able to generate more robust models with better predictive accuracy with fewer features when compared to PLS-DA. Section 13.7 Review Questions 1. Define intelligent agents. Why are there so many different definitions and names for intelligent agents?

An intelligent agent (IA) is an autonomous computer program that observes and acts upon an environment and directs its activity toward achieving specific goals.

Intelligent agents may have the ability to learn by using and expanding the knowledge embedded into them.

Several names have been used to describe intelligent agents, including software agents, wizards, software daemons, softbots (for “intelligent software robots”), or simply bots. These terms sometimes refer to different types of agents or agents with different capabilities and intelligence levels. Throughout this chapter and this book, we use the terms intelligent agent, software agent, and intelligent software agent interchangeably.

2. What are the main features of intelligent agents?

Intelligent agents often are associated with the following features (Wooldridge, 2009; Stenmark, 1999): • Reactiveness. Intelligent agents are able to perceive their environment and respond in a timely fashion to changes that occur in it in order to satisfy their design objectives.

• Proactiveness. Intelligent agents are able to exhibit goal-directed behavior by taking the initiative in order to satisfy their design objectives.

• Sociability. Intelligent agents are capable of interacting with other agents (and possibly humans) in order to satisfy their design objectives.

• Autonomy. Intelligent agents must have control over their own actions and be able to work and launch actions independently of the user or other actors.

• Intelligence. Intelligent agent must learn from their experiences to continually get better at achieving their design objectives.

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3. Describe the major components of intelligent agents.

Intelligent agents are autonomous computer programs that are generally associated with the following components: • Owner. The owner is the user name, parent process name, or master agent name.

Intelligent agents can have several owners. Either humans or the process itself can spawn agents (e.g., a stockbroker or the brokerage process using intelligent agents) to monitor prices, and other intelligent agents can spawn their own supporting agents.

• Author. The author is the development owner, service, or master agent.

Intelligent agents can be created by people or processes and then supplied as templates for users to personalize.

• Account. An intelligent agent must have an anchor to an owner’s account and an electronic address for tracing and billing purposes.

• Goal. A clear statement of successful agent task completion is necessary, as are metrics for determining a task’s point of completion and the value of the results.

Measures of success can include simple completion of a transaction within the boundaries of the stated goal or a more complex measure. Often agents have recurring tasks that lasts indefinitely.

• Subject description. The subject description details the agent’s purpose and attributes. These attributes provide the boundaries of the agent, task, possible resources to call on, and class of need (e.g., stock purchase price, airline ticket price).

• Creation and duration. The creation and duration are the time dependent attributes associated with an agent that describes when an instance of an agent should to be created and when it should be terminated.

• Background. Intelligent agents are to run in the background, pursuing specific tasks without interfering with the ordinary activities of the operating system.

• Intelligent subsystem. An intelligent subsystem, such as a rule-based ES or a neural computing system, provides the agent’s learning capabilities.

4. List and briefly comment on different categories of agents.

CLASSIFICATION BY APPLICATION TYPE Franklin and Graesser (1996) used a taxo-nomic tree to classify autonomous agents (see Figure 13.11). Relevant to managerial decision making is the category of computational agents, software agents, and task-specific agents. They can be further differentiated by the nature of applications, as specified below.

Organizational and Personal Agents Organizational agents execute tasks on behalf of a business process or computer application. Personal agents perform tasks on behalf of individual users. For example, corporate use of agent monitoring software is becoming a key component in the drive to cut support costs and increase computer productivity.

Intelligent agents can search through e-mail messages for certain keywords.

Depending on what keywords are contained in a message, the agent automatically

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sends out answers based on FAQ files. A company can use such an agent to help customers obtain answers to their questions quickly (e.g., egain.com and brightware.com). Another example of an organizational intelligent agent is an automatic e-mail sorting system. When a new message comes in, it is automatically routed to the right file and folder. Personal agents are very powerful. They allow users to go directly to the information they want on the Internet, saving time for busy people.

Private and Public Agents A private (or personal) agent works for only one user who creates it. Public agents are created by a designer for the use of anybody who has access to the application, network, or database.

Software Agents and Intelligent Agents According to Lee et al. (1997), truly intelligent agents must be able to learn and exhibit high-level of autonomy.

However, most Internet and e-commerce agents do not exhibit these characteristics yet, at least not at the level that they are expected. Therefore, they are often called software agents. The second generation of Internet and e-commerce being developed today includes more and more agents with learning capabilities (see media.mit.edu/research/groups/ software-agents).

Another classification by Wooldridge (2002) included the following different categories: • Agents for workflow and business process management • Agents for distributed sensing • Agents for retrieval and management • Agents for e-commerce • Agents for human–computer interaction • Agents for virtual environments • Agents for social simulation 5. Describe the differences between software agents and intelligent agents.

None. Throughout this chapter and this book, we use the terms intelligent agent, software agent, and intelligent software agent interchangeably. Software Agents and Intelligent Agents According to Lee et al. (1997), truly intelligent agents must be able to learn and exhibit high-level of autonomy. However, most Internet and e-commerce agents do not exhibit these characteristics yet, at least not at the level that they are expected. Therefore, they are often called software agents. The second generation of Internet and e-commerce being developed today includes more and more agents with learning capabilities (see media.mit.edu/research/groups/ software-agents).

Section 13.8 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

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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. 623. 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.

3. What are the pros and cons of integrating intelligent systems?

These include the system described in the opening vignette, where voice recognition and natural language processing were combined with inference engines to develop automated reading tutors. Other integrated intelligent systems include a supplier selection system that uses fuzzy logic and neural networks (Golmohammadi et al., 2009); a clustering system that uses neural networks and fuzzy logic to identify different muscle movements (Karlik et al., 2009); the United Technologies Carrier product-reliability system that integrates a rule-based system and a neural network (Deng and Tsacle, 2000); a construction-price estimation tool that integrates an ES and a neural network (Li and Love, 1999); a forecasting tool that integrates genetic algorithms and fuzzy logic (Li and Kwan, 2003); and a prediction and optimization system for a ceramic-casting process that integrates neural networks and fuzzy logic (Kuo and Chen, 2004). Many similar examples can be found with Web searches as well as on digital library databases.

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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?

Why or why not?

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 system based on genetic algorithm-based 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

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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.

•

•

•

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 unrelated, 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

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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.

Discuss the pros and cons of SVM as they compare to ANN? Similar to artificial neural networks (ANN), SVM possess the well-known ability of being universal approximators of any multivariate function to any desired degree of accuracy. Therefore, they are of particular interest to modeling highly nonlinear, complex systems and decision situations.

Compared to ANN, SVM always find the optimal mode, and their simple geometric interpretation provides fertile ground for further investigation.

8.

For what types of problems are SVM most appropriate? Discuss your choices in the context or requirements of those problem scenarios.

In addition to their solid mathematical foundation in statistical learning theory, SVM have demonstrated highly competitive performance in numerous real-world applications, including medical diagnosis, bioinformatics, face recognition, financial predictions, and text mining, which has established SVM as one of the most popular tools for knowledge discovery and data mining.

9.

Why is the use of intelligent agents increasing exponentially?

Intelligent agents have the ability to learn by using and expanding the knowledge embedded into them. Intelligent agents are powerful tools for overcoming the most critical burden of the Internet—information overload—and making ecommerce a viable organizational tool.

10.

Why are there so many different names and definitions for intelligent agents?

Does this help or hurt the development of the technology? Explain.

See Question 1, Section 13.7

ANSWERS TO END OF CHAPTER APPLICATION CASE 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

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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.

.

Chapter 14: Management Support Systems: Emerging Trends and Impacts

Learning Objectives for Chapter 14 1.

Explore some of the emerging technologies that may impact MSS 2.

Know how RFID data analysis can help improve supply-chain management and other operations 3.

Describe how massive data acquisition techniques can enable reality mining 4.

Describe how virtual-world technologies can be used for decision support and the associated advantages and disadvantages 5.

Describe how virtual-world applications can result in additional data for BI applications

Describe the potential of cloud computing in business intelligence 6.

Understand Web 2.0 and its characteristics as related to MSS 7.

Understand social networking concepts, selected applications, and their relationship to BI 8.

Describe organizational impacts of MSS 9.

Learn the potential impacts of MSS on individuals 10.

Describe societal impacts of MSS 11.

List and describe major ethical and legal issues of MSS implementation CHAPTER OVERVIEW This, the last chapter in the book, wraps it all up. This chapter introduces several emerging technologies that are likely to have major impacts on the development and use of business intelligence applications. Many other interesting technologies are also emerging, but the focus is on some trends that have already been realized and others that are about to impact MSS further. Using a crystal ball is always a risky proposition, but this chapter provides a framework for analysis of emerging trends and ethical and legal issues. Covered in this chapter are emerging technologies, current applications, relationship to MSS, organizational, personal, legal, ethical, and societal impacts of support systems that may affect their implementation. When students finish this chapter, they should have a well-earned sense of accomplishment.

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CHAPTER OUTLINE 14.1

OPENING VIGNETTE: COCA-COLA’S RFID-BASED DISPENSER SERVES A NEW TYPE OF BUSINESS INTELLIGENCE  Questions for the Opening Vignette A. WHAT WE CAN LEARN FROM THIS VIGNETTE

14.2

RFID AND NEW BI APPLICATION OPPORTUNITIES  Section 14.2 Review Questions

14.3

REALITY MINING  Section 14.3 Review Questions

14.4

VIRTUAL WORLDS ⧫ Technology Insights 14.1: Second Life as a Decision  Section 14.4 Review Questions

14.5

THE WEB 2.0 REVOLUTION A. REPRESENTATIVE CHARACTERISTICS OF WEB 2.0 B. WEB 2.0 COMPANIES AND NEW BUSINESS MODELS  Section 14.5 Review Questions

14.6

VIRTUAL COMMUNITIES A. CHARACTERISTICS OF TRADITIONAL ONLINE COMMUNITIES AND THEIR CLASSIFICATION 1.

Types of Communities  Section 14.6 Review Questions

14.7

ONLINE SOCIAL NETWORKING: BASICS AND EXAMPLES A. A DEFINITION AND BASIC INFORMATION 1.

2.

B.

MOBILE SOCIAL NETWORKING 1.

2.

C.

THE SIZE OF SOCIAL NETWORK SITES SOCIAL NETWORK ANALYSIS SOFTWARE MOBILE ENTERPRISE NETWORKS MOBILE COMMUNITY ACTIVITIES

MAJOR SOCIAL NETWORK SERVICES: FACEBOOK AND ORKUT 1.

FACEBOOK: THE NETWORK EFFECT 2.

ORKUT: EXPLORING THE VERY NATURE OF SOCIAL NETWORKING SITES

D.

IMPLICATIONS OF BUSINESS AND ENTERPRISE SOCIAL NETWORKS 1.

FINDING AND RECRUITING WORKERS

⧫ Application Case 14.1: Using Intelligent Software and Social Networking to Improve Recruiting Processes 2.

3.

4.

MANAGEMENT ACTIVITIES AND SUPPORT TRAINING KNOWLEDGE MANAGEMENT AND EXPERT LOCATION

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5.

6.

7.

3

ENHANCING COLLABORATION USING BLOGS AND WIKIS WITHIN THE ENTERPRISE USING TWITTER TO GET A PULSE OF THE MARKET



Section 14.7 Review Questions

14.8

CLOUD COMPUTING AND BI  Section 14.8 Review Questions

14.9

THE IMPACTS OF MANAGEMENT SUPPORT SYSTEMS: AN OVERVIEW  Section 14.9 Review Questions

14.10 MANAGEMENT SUPPORT SYSTEMS IMPACTS ON ORGANIZATIONS A. NEW ORGANIZATIONAL UNITS B. ORGANIZATIONAL CULTURE C. RESTRUCTURING BUSINESS PROCESSES AND VIRTUAL TEAMS 1.

SIMULATION MODELING AND ORGANIZATIONAL RESTRUCTURING

D. E.

THE IMPACTS OF ADS SYSTEMS OTHER ORGANIZATIONAL IMPACTS  Section 14.10 Review Questions

14.11 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 14.11 Review Questions ⧫ Application Case 14.1: Using Intelligent Software and 14.12 AUTOMATING DECISION MAKING AND THE MANAGER’S JOB A. THE EFFECT OF MSS ON MANAGERS’ ACTIVITIES AND THEIR PERFORMANCE B. CAN MANAGERS’ JOBS BE FULLY AUTOMATED?  Section 14.12 Review Questions 14.13 ISSUES OF LEGALITY, PRIVACY, AND ETHICS A. LEGAL ISSUES B. PRIVACY 1.

Collecting Information About Individuals 2.

The Web and Information Collection 3.

Mobile User Privacy 4.

Homeland Security and Individual Privacy C. ETHICS IN DECISION MAKING AND SUPPORT 1.

Non-Work-Related Use of the Internet  Section 14.9 Review Questions

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14.14 Resources, Links, and the Teradata University Network Connection 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: Continental Continues to Score with Data Warehouse  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.

The primary purpose of this chapter is to introduce several emerging technologies that will provide new opportunities for application and extension of business intelligence techniques and management support systems. This part also briefly explores the individual, organizational, and societal impacts of these technologies, especially the ethical and legal issues in MSS implementation. After describing many of the emerging technologies or application domains, we will focus on organizational issues.

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 14.1 Review Questions 1. What is the benefit of RFID for reducing inventory in this case?

RFID technology allows Coca-Cola to track sales and customer taste preferences.

It takes time to replenish inventory, which is why safety stocks of inventory would be needed. By tracking inventory in real-time, Coca-Cola knows when to replenish.

They rely on RFID information instead of inventory safety stock to avoid out-of-stock conditions.

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2. How would a restaurant benefit from having RFID-enabled syrup cartridges?

They can test new drink flavors and concepts and keep track of the amounts they are drinking. By being able to use the flavors in multiple combinations through the dispensers, the restaurant can see what new combinations are most popular. This process saves money. Instead of stocking bottles in numerous flavors, a restaurant can save space, reduce inventory, and not be out of a flavor customers want-- giving better customer service.

3. What benefit would a customer derive from the new dispenser?

Customers will always be able to get the drink they want, even if it’s brand new, and the beverage will probably be fresher.

4. What issues might impact the widespread acceptance of this dispenser?

Price and maintenance cost of the dispenser, ease of use, easy-to-clean, reliability, and good taste of the drinks.

Section 14.2 Review Questions 1. What is RFID? RFID is a generic technology that refers to the use of radio frequency waves to identify objects. Fundamentally, RFID is one example of a family of automatic identification technologies, which also include the ubiquitous barcodes and magnetic strips. Since the mid-1970s, the retail supply chain (and many other areas) has used barcodes.

RFID data can provide useful analytics for supply chain management and performance measurement. Sensor-based RFID can help analyze product conditions within a supply chain.

2. What kinds of data are read/recorded through RFID? Kinds of data that are read from the RFID tag includes manufacturer, product, and serial number information, and data that uniquely identifies the item. Currently, most RFID tags contain 96 bits of data in the form of serialized global trade identification numbers (SGTIN) for identifying cases or serialized shipping container codes (SSCC) for identifying pallets (although SGTINs can also be used to identify pallets).

Whatever data is coded into the tag can be read through RFID. (See diagram on p.

637.)

3. What can a company learn by reading RFID at a distribution center?

Sensor-based RFID can help analyze product conditions within a supply chain.

See Figure 14.2 on page 638. Figure 14.2 represents the typical functions performed

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in a distribution center (DC) and in a retail store with respect to the flow of materials between these supply units. In a DC, the primary functions are receiving, put-away, picking, and shipping. Receiving is the collection of all the activities related to the orderly receipt of materials/goods, inspection for quantity and quality, and dispersion of the received goods to storage/put-away and/or to cross-docking for immediate shipment.

RFID can track DC operations and the location of tagged items once a truck backs up to the receiving door. Those operations are: (1) unloading the contents of the trailer; (2) verification of the receipt of goods against expected delivery (purchase order); (3) sorting of the damaged goods and documentation of the discrepancy in count and/or product type to be settled later; (4) if needed, application of labels to the pallets, cases, or items so that units can be tracked inside the warehouse; and (5) sorting of goods for put-away or cross-dock based on current demand and schedule.

4. Search online for applications of RFID in health care, entertainment, and sports.

Students’ search results will vary.

Section 14.3 Review Questions 1. Define reality mining.

Reality mining is a new type of data mining that builds on the idea that these datasets from network-connected devices (that send out their location information) could provide remarkable real-time insight into aggregate human activity trends.

2. What types of data are used in reality mining?

Data are used from many devices in use by consumers and businesspeople that constantly send out their location information. Cars, buses, taxis, mobile phones, cameras, and personal navigation devices all transmit their locations thanks to network-connected positioning technologies such as GPS, WiFi, and cell tower triangulation.

3. Briefly describe how the data are used to create profiles of users.

By analyzing and learning from these large-scale patterns of movement, it is possible to identify distinct classes of behaviors in specific contexts, called “tribes. “ Macrosense is an application platform developed by Sense Networks that takes the data being generated by all of these mobile devices and, after spatial and time-based cleaning, applies proprietary clustering algorithms to these massive datasets to classify the incoming data streams as belonging to different types of customers/clients/etc. This approach allows a business to better understand its customer patterns and also to make more informed decisions about promotions, pricing, and so on.

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4. What other applications can you imagine if you were able to access cell phone location data? Do a search on location-enabled services.

Students’ answers will vary.

Section 14.4 Review Questions 1. What is a virtual world?

Virtual worlds are artificial worlds created by computer systems in which the user has the impression of being immersed. The intention is to achieve a feeling of telepresence and participation from a distance.

2. What are the advantages and disadvantages of providing decision support through virtual worlds?

See Technology Insights 14.1 on pages 645-646. Advantages include: • Easy access and low cost.

• Experienced and dedicated designer/builders.

• Impression management and creativity enhancement.

• Time compression.

• Disadvantages include: Disadvantages include: • Learning time and training costs.

• Distractions are numerous.

• Pranksters and spam are common.

• Technology problems persist.

3. What activities of a physical tradeshow can be experienced in a virtual event? Which ones cannot be replicated?

Virtual world technology may be useful in replicating a tradeshow participation experience by organizing virtual events that can extend the reach of the event to include many more attendees and exhibitors. The virtual tradeshow may have other components, such as a virtual Web conference, a collection of Web seminars, or other educational presentations. Visitors fill out an online registration form to create an online badge before entering the virtual exhibit hall to visit various booths. The virtual booths often look like real-world tradeshow booths, with desks and displays that users can relate to easily. Detailed tracking mechanisms enable organizers to record and analyze the flow of traffic in the virtual tradeshow. Virtual tradeshows can serve as international tradeshows, business matchmakers, procurement fairs, and product launches. The experience also translates well for other applications, such as virtual job fairs, virtual benefits fairs, online employee networks, distributor fairs, and venture capital fairs. See Figure 14.4 on page 647.

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4. What type of data analysis might you perform on data about users in a specific virtual-world setting (e.g., a company island in Second Life, a virtual tradeshow booth)?

Students’ answers will differ.

Section 14.5 Review Questions 1. Define Web 2.0.

Web 2.0 is the popular term for describing advanced Web technologies and applications, including blogs, wikis, RSS, mashups, user-generated content, and social networks. A major objective of Web 2.0 is to enhance creativity, information sharing, and collaboration.

2. List the major characteristics of Web 2.0.

The following are representative characteristics of the Web 2.0 environment: • The ability to tap into the collective intelligence of users. The more users contribute, the more popular and valuable a Web 2.0 site becomes.

• Data is made available in new or never-intended ways. Web 2.0 data can be remixed or “mashed up,” often through Web service interfaces, much the way a dance-club DJ mixes music.

• Web 2.0 relies on user-generated and user-controlled content and data.

• Lightweight programming techniques and tools let nearly anyone act as a Web site developer.

• The virtual elimination of software-upgrade cycles makes everything a perpetual beta or work-in-progress and allows rapid prototyping, using the Web as an application development platform.

• Users can access applications entirely through a browser.

• An architecture of participation and digital democracy encourages users to add value to the application as they use it.

• A major emphasis on social networks and computing.

• Strong support of information sharing and collaboration.

3. What new business model has emerged from Web 2.0?

A new business model that has emerged from Web 2.0 is the accumulation of the “power of the crowd.” The potential of such a business model is unlimited. For example, Wikia is working on community-developed Web searches.

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Section 14.6 Review Questions 1. Define virtual (Internet) communities and describe their characteristics.

A virtual (Internet) community is one in which the interaction takes place over a computer network, mainly the Internet. Virtual communities parallel typical physical communities, such as neighborhoods, clubs, or associations, but people do not meet face-to-face. Instead, they meet online. A virtual community is a social network organized around a common interest, idea, task, or goal; members interact across time, geographic location, and organizational boundaries to develop personal relationships. Virtual communities offer several ways for members to interact, collaborate, and trade (see Table 14.2 on page 651).

2. List the major types of virtual communities.

Most common are public and private communities. Most of the social networks, including MySpace and Facebook belong to the public category. Table 14.3 on page 652 lists other types of communities, which are Transaction and other business activities, purpose or interest, relations or practices, fantasy, social networks, and virtual worlds.

3. Distinguish between private and public communities.

Communities can be designated as public, meaning that their membership is open to anyone. Communities can be designated as public, meaning that their membership is open to anyone.

4. Distinguish between internal and external communities.

Internal private communities exist within organizations. Such communities include employees, retirees, suppliers, and customers who share a common interest.

The focus of such communities is on knowledge sharing, collaboration, expert location, and knowledge creation. Companies such as Pfizer, FedEx, Caterpillar, Wells Fargo, and IBM have such communities.

External private communities include one organization and its business partners, government agencies, and prospects. The participants share information on a variety of issues.

Section 14.7 Review Questions 1. Define social network.

A social network as a place where people create their own space, or homepage, on which they write blogs (Web logs); post pictures, videos, or music; share ideas; and link to other Web locations they find interesting.

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2. List some major social network sites.

Students will have different answers, including MySpace and Facebook, Wikipedia and YouTube.

3. Describe the global nature of social networks.

Students’ answers will differ. They many state that the global nature is related to the mass adoption of social networking Web sites pointing to an evolution in human social interaction regardless of location or in all locations.

4. Describe mobile social networking.

Mobile social networking refers to social networking where members converse and connect with one another using cell phones or other mobile devices.

5. Identify Facebook’s major strategic issues (e.g., look at the marketing efforts at insidefacebook.com and at facebook.com).

Students’ answers will differ, but may refer to mobile services and how to grow or maintain their membership. Strategic issues change so their answers may reflect issues that are not yet crucial.

6. Much of Facebook’s early success was due to the close affiliation of its members’ networks. How does Facebook expand into new markets without losing what originally made the site popular and alienating existing users?

Facebook realized that it could only keep college and university users for 4 years.

In 2006, Facebook opened its doors to anyone age 13 or older with a valid e-mail address. Expanding to a global audience has enabled Facebook to compete directly with MySpace. Today, Facebook has a number of applications that support photos, groups, events, marketplaces, posted items, and notes. Facebook also has an application called “People You May Know,” which helps users connect with people they might know. More applications are being added constantly.

Section 14.8 Review Questions 1. Define cloud computing. How does it relate to PaaS, SaaS, and IaaS? Cloud computing offers the possibility of using software, hardware, platform, and infrastructure, all on a service-subscription basis. Cloud computing enables a more scalable investment on the part of a user. Like PaaS, etc., cloud-computing offers organizations the latest technologies without significant upfront investment.

In some ways, cloud computing is a new name for many previous related trends: utility computing, application service provider grid computing, on-demand computing, software as a service (SaaS), and even older centralized computing with dumb terminals. But the term cloud computing originates from a reference to the Internet as a

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“cloud” and represents an evolution of all of previous shared/centralized computing trends.

2. Give examples of companies offering cloud services.

Companies offering such services include 1010data, LogiXML, and Lucid Era. These companies offer feature extract, transform, and load capabilities as well as advanced data analysis tools. Other companies, such as Elastra and Rightscale, offer dashboard and data management tools that follow the SaaS and DaaS models.

3. How does cloud computing affect business intelligence?

Cloud-computing-based BI services offer organizations the latest technologies without significant upfront investment.

Section 14.9 Review Questions 1. How can MSS affect particular individuals and jobs or the work structure of departments and units within an organization?

Students’ answers will differ because MSS can affect organizations in many ways, as stand-alone systems or integrated among themselves or with other computerbased information systems. The impact of MSS on individuals varies; it can be positive, neutral, or negative.

2. How can MSS affect organizational structures, entire industries, communities, and society as a whole?

Students’ answers will differ. MSS could reduce the need for supervision by providing more guidelines to employees electronically. The same is true for society as a whole.

3. What are the major stimuli for change in the MSS environment?

The major change stimuli relevant to MSS are usually strategy and technology, especially when BI or ES are introduced.

4. List some of the major social impacts of MSS. The following are some of the major areas of social impact: • Changing role of home-bound people • Computer crime and fraud • Consumers • The digital divide • Employment levels

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• Opportunities for the disabled • Quality of life • Work in hazardous environments Section 14.10 Review Questions 1. Describe new organizational units that are created because of MSS. One change in organizational structure is the possibility of creating a management support department, a BI department (unit), an artificial intelligence department, or a knowledge management department in which MSS play a major role. This special unit can be combined with or replace a quantitative analysis unit, or it can be a completely new entity.

2. Relate MSS to changes in organizational culture.

Organizational culture can affect the diffusion rate of technology and can be influenced by it. In addition, virtual teams can meet anytime and anyplace. People can join a virtual team for as long as the project lasts or whenever their expertise is needed. When a project is completed, the team can disband. Automated decision support (ADS) applications can empower lower-level frontline employees, giving them more autonomy. They may also reduce the size of organizations and change the organization’s culture.

3. How can MSS affect restructuring of business processes?

When a company introduces a MSS such as a data warehouse and BI, the information flows and related business processes (e.g., order fulfillment) change because information flows change. For example, before IBM introduced eprocurement, it restructured all related business processes, including decision making, searching inventories, reordering, and shipping.

4. Describe the impacts of ADS systems.

Automated decision support (ADS) applications can empower lower-level frontline employees, giving them more autonomy. They may also reduce the size of organizations and change the organization’s culture.

Section 14.11 Review Questions 1. How can MSS affect job satisfaction?

MSS has positive or negative impacts. Although many jobs may be substantially enriched by MSS, other jobs may become more routine and less satisfying. But a study by Ryker and Ravinder showed that IT has had a positive effect on four of the five core job dimensions: identity, significance, autonomy, and feedback.

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2. In what ways may MSS affect inflexibility, dehumanization, stress, and anxiety?

A common criticism of traditional data-processing systems is their negative effects on people’s individuality. Such systems are criticized as being impersonal: They may dehumanize and depersonalize activities that have been computerized because they reduce or eliminate the human element that was present in noncomputerized systems. Some people feel a loss of identity; they feel like just another number. On the bright side, one of the major objectives of MSS is to create flexible systems and interfaces that allow individuals to share their opinions and knowledge and work together with computers. Despite all these efforts, some people are still afraid of computers, so they are stressed; others are mostly afraid of their employers watching what they do on the computer.

3. Describe the issue of experts’ cooperation in MSS. Human experts who are planning to give their knowledge to an organizational or to a problem-specific knowledge base may have reservations. Experts may believe that:: • 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.

This kind of thinking may cause the expert not to cooperate or even to give incorrect knowledge to the computer. To deal with such situations, management should motivate (and possibly compensate) the experts.

Section 14.12 Review Questions 1. List the impacts of MSS on decision making.

The following are some potential impacts of MSS on managers’ jobs: • Less expertise (experience) is required for making many decisions.

• Faster decision making is possible because of the availability of information and the automation of some phases in the decision-making process.

• Less reliance on experts and analysts is required to provide support to top executives; managers can do it by themselves with the help of intelligent systems.

• Power is being redistributed among managers. (The more information and analysis capability they possess, the more power they have.)

• Support for complex decisions makes them faster to make and of better quality.

• Information needed for high-level decision making is expedited or even selfgenerated.

2. List the impacts of MSS on other managerial tasks.

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Automation of routine decisions or phases in the decision-making process (e.g., for frontline decision making and using ADS) may eliminate some managers. In general, it has been found that the job of middle managers is the most likely job to be automated. Midlevel managers make fairly routine decisions, which can be fully automated 3. Explain the issues related to completely automating managers’ jobs.

Automation of certain tasks can save time, increase consistency, and enable better decisions to be made (see Davenport and Harris, 2005). Thus, the more tasks we automate in the process, the better. However, is it possible to completely automate the manager’s job?

In general, it has been found that the job of middle managers is the most likely job to be automated. Managers at lower levels do not spend much time on decision making. However, even if we completely automate their decisional role, we could not automate their jobs.

Section 14.13 Review Questions 1. List some legal issues of MSS. Consider the following specific legal issues: • 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? • 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?

2. Describe privacy concerns in MSS. In general, privacy is the right to be left alone and the right to be free from unreasonable personal intrusions. The Internet, in combination with large-scale databases, has created an entirely new dimension of accessing and using data. The inherent power in systems that can access vast amounts of data can be used for the good of society. For example, by matching records with the aid of a computer, it is possible to eliminate or reduce fraud, crime, government mismanagement, tax evasion, welfare cheating, family-support filching, employment of illegal aliens, and so on. The same is true on the corporate level. Private information about employees may aid in better decision making, but the employees’ privacy may be affected.

Similar issues are related to information about customers.

3. Explain privacy concerns on the Web.

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The Internet offers a number of opportunities to collect private information about individuals. Here are some of the ways it can be done: • By reading an individual’s newsgroup postings • By looking up an individual’s name and identity in an Internet directory • By reading an individual’s e-mail • By wiretapping wireline and wireless communication lines and listening to employees • By conducting surveillance on employees • By asking an individual to complete Web site registration • By recording an individual’s actions as he or she navigates the Web with a browser, using cookies or spyware The implications for online privacy are significant. The ability of law enforcement agencies to authorize installation of pen registers and trap-and-trace devices has increased. The U.S. PATRIOT Act also broadens the government’s ability to access student information and personal financial information without any suspicion of wrongdoing by attesting that the information likely to be found is pertinent to an ongoing criminal investigation 4. List ethical issues in MSS. Representative ethical issues that could be of interest in MSS implementations include the following: • Electronic surveillance • Ethics in DSS design (see Chae et al., 2005) • Software piracy • Invasion of individuals’ privacy (discussed earlier) • 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 • How much decision making to delegate to computers 5. Describe an ethical framework for MSS. See Figure 14.7 on page 670.

Section 14.14 Review Questions No review questions in Section 14.4

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ANSWERS TO QUESTIONS FOR DISCUSSION

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1. What are the potential benefits and challenges of using RFID in supply-chain management?

RFID provides the data and the insight needed for more efficient and effective supply chain management. It reduces uncertainty and waste (excess inventory and other inventory problems such as spoilage and loss) while increasing product availability. The challenges are the upfront RFID costs, cost of the tags, knowledge about placement of the RFID readers, database costs, the need (and costs) to standardize product codes, and the need for everyone in the chain to be using RFID. 2. Given that RFID data streams are large but only include basic tracking information, how would you derive useful information from these streams?

Use BI technology to derive useful patterns and insights from the huge data streams.

3. What are the benefits and issues of using virtual worlds for decision support?

See Technology Insights 14.1 on pages 645-646. Advantages include: • Easy access and low cost.

• Experienced and dedicated designer/builders.

• Impression management and creativity enhancement.

• Time compression.

• Disadvantages include: Disadvantages include: • Learning time and training costs.

• Distractions are numerous.

• Pranksters and spam are common.

• Technology problems persist.

4. If you had an opportunity to participate in a virtual career fair, what factors would motivate and inhibit your participation?

Students’ answers will differ. Motivations may be related to faster, cheaper, easier to reach a large employer base. Inhibitors may be lack of personal contact, feels like a waste of time, and too many attendees.

5. “Location-tracking-based profiling (reality mining) is powerful but also poses privacy threats.” Comment.

Students’ answers will differ. Privacy threats relate to user-profiling, intrusive use of personal information, and not being able to control what is being collected.

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6. Is cloud computing “just an old wine in a new bottle”? How is it similar to other initiatives? How is it different?

In some ways, cloud computing is a new name for many previous related trends: utility computing, application service provider grid computing, on-demand computing, software as a service (SaaS), and even older centralized computing with dumb terminals. But the term cloud computing originates from a reference to the Internet as a “cloud” and represents an evolution of all of previous shared/centralized computing trends.

Cloud computing offers the possibility of using software, hardware, platform, and infrastructure, all on a service-subscription basis. Cloud computing enables a more scalable investment on the part of a user. Like PaaS, etc., cloud-computing offers organizations the latest technologies without significant upfront investment.

7. What are the major characteristics of Web 2.0? What are some of the advantages of Web 2.0 applications?

The following are representative characteristics of the Web 2.0 environment: • The ability to tap into the collective intelligence of users. The more users contribute, the more popular and valuable a Web 2.0 site becomes.

• Data is made available in new or never-intended ways. Web 2.0 data can be remixed or “mashed up,” often through Web service interfaces, much the way a dance-club DJ mixes music.

• Web 2.0 relies on user-generated and user-controlled content and data.

• Lightweight programming techniques and tools let nearly anyone act as a Web site developer.

• The virtual elimination of software-upgrade cycles makes everything a perpetual beta or work-in-progress and allows rapid prototyping, using the Web as an application development platform.

• Users can access applications entirely through a browser.

• An architecture of participation and digital democracy encourages users to add value to the application as they use it.

• A major emphasis on social networks and computing.

• Strong support of information sharing and collaboration.

Web 2.0 enhances creativity, information sharing, and collaboration.

8. Discuss the use of virtual communities to do business on the Internet.

A virtual community is a social network organized around a common interest, idea, task, or goal; members interact across time, geographic location, and organizational boundaries to develop personal relationships. Virtual communities offer several ways for members to interact, collaborate, and trade (see Table 14.2 on page 651).

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9. How can wikis be used to facilitate knowledge management?

Wikis provide a familiar and easy-to-use platform for sharing and distributing knowledge throughout an organization’s locations and time zones.

10. Discuss the relationship between mobile devices and social networking.

Mobile social networking enables social networking where members converse and connect with one another using cell phones or other mobile devices.

11. 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.

Students’ answers will differ.

A common criticism of traditional data-processing systems is their negative effects on people’s individuality. Such systems are criticized as being impersonal: They may dehumanize and depersonalize activities that have been computerized because they reduce or eliminate the human element that was present in noncomputerized systems. Some people feel a loss of identity; they feel like just another number. On the bright side, one of the major objectives of MSS is to create flexible systems and interfaces that allow individuals to share their opinions and knowledge and work together with computers. Despite all these efforts, some people are still afraid of computers, so they are stressed; others are mostly afraid of their employers watching what they do on the computer.

12. The U.S. Department of Transportation in a large metropolitan 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

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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.

13. 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, see Chapter 12) were used extensively, but few physicians use ES. Answer the following questions: a. Why do you think ES are little used by physicians?

Students’ answers will differ. Some reasons are: physicians do not understand and therefore do not trust the ES; malpractice insurance does not cover recommendations made by MYCIN; administrators will not invest in it; physicians fear they will be replaced or earn less. b. Assume that you are a hospital administrator whose physicians are salaried and report to you. What would you do to persuade them to use ES? Students’ answers will differ. Students should identify a combination of positive (more money) and negative (calm their fears) motivators c. If the potential benefits to society are so great, can society do something that will increase doctors’ use of ES? Probably not--or at least not yet.

14. Discuss the potential benefits of integrating KMS into a DSS. Students’ answers will differ. They many mention that KMS adds knowledge discovery, creation, maintenance, sharing, transfer, and dissemination capabilities to DSS. 15. Discuss the potential impacts of ADS systems on various types of employees and managers.

Automation of routine decisions or phases in the decision-making process (e.g., for frontline decision making and using ADS) may eliminate some managers. In general, it has been found that the job of middle managers is the most likely job to be automated. Midlevel managers make fairly routine decisions, which can be fully automated In general, it has been found that the job of middle managers is the most likely job to be automated. Managers at lower levels do not spend much time on decision

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making. However, even if we completely automate their decisional role, we could not automate their jobs.

ANSWERS TO END OF CHAPTER APPLICATION CASE QUESTIONS

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1. Visit teradatastudentnetwork.com and learn more about Continental’s experiences in data warehouses. Summarize your reading.

Students’ summaries of their reading will differ.

2. Wixom et al. call Continental’s work with business intelligence a journey rather than a destination. Comment.

A journey is something that continues and changes as the road changes. A destination implies an end. Students’ answers should reflect that analogy.

3. What other new applications of data warehouses can you imagine at an airline?

Students’ answers will differ. Some possible new types of applications of data warehouses are better security screening, more optimal pricing, labor scheduling, maintenance, and financing.

4. The case summary includes two example applications of BI at an airline. Can you relate these applications to your own organization’s domain?

Students’ answers will differ based on their jobs and experiences.

5. Comment on the list of facilitating success factors for a mature data warehouse.

Facilitators in continuing success of BI at Continental are • A common data foundation • “Open data” philosophy • A culture of data • Personnel who are business–IT hybrids Students’ answers will differ based on their jobs and experiences.

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