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Full Paper Proc. of Int. Conf. on Advances in Computer Science and Application 2012

Maturity Assessment of an Information Technology Organization Based on OPM3 Using Fuzzy Expert System Saeede Vakhshoori1, Ahmad Nadali*2, Mahdieh Khalilinezhad3, Hamid EslamiNosratabadi4, Mansour Mirikalaniki5 1

Department of Business Management, North Tehran Branch, Islamic Azad University, Tehran,Iran Email: 2 Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran 3 Department of Computer Engineering, University of Qom, Qom, Iran 4 Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran, Iran 5 Department of Geopolitics, Central Tehran Branch, Islamic Azad University, Tehran, Iran * Corresponding Author Email: [4]. Maturity models in areas involving process and highperformance delivery are proving to be useful because they allow individuals and organizations to self-assess the maturity of various aspects of their processes against benchmarks. Many scholars have put forward the evaluation index system of project management capability. Rad, Parviz F. Levin, and Ginger divided the project management capacity into four areas: enterprise management, material management, people management and technical content management [5, 6]. Jian Lirong and Liu Sifeng applied the ideas of systems engineering to analyze the system of project management capabilities, and divided project management capabilities into four levels: capability to develop project management strategic planning, ability to choose the project management organization, systems engineering capabilities of project management, project management team ability [5,7].At present, many studies of the project management maturity focused on empirical research, examined the stage of enterprise project management, and determined the enterprise project management in which a certain stage of maturity model. James J. Jiang et al. investigated software enterprise management maturity, noted the high failure rate in the software design, and insisted that CMM can improve the success rate of software production [5,8]. E.S. Anderson and S.A. Jessen investigated the status of the project maturity in organization, pointed out that the maturity of the organization primarily composed of three steps: the project management, program management, and document management, and put forward that the attitudes and knowledge is more important than the action for an organization in terms of maturity management [5,9]. Pekka Berg proposed the Quality Maturity Model (QMM) to determine the management level of R&D projects [5, 10]. Mohammad Khoshgoftar and Omar Osman compared recent maturity models in terms of selected variables, and concluded that OPM3 is a more suitable model than others [5,11]As it was previously mentioned, there are some models that can measure level of maturity and OPM3 is one of the most applicable. If you are a project manager and you are concerned if the software of assessment of

Abstract—Despite of increasing interest of researchers to issues related to maturity and specially the role of organizational maturity and its impact on OPM3 there is not enough studies about maturity level measurement. The purpose of this research is maturity assessment of OPM3 of organizations by an intelligent system. Here, a Fuzzy Expert System has been designed with considering main effective variables on maturity assessment as Inputs variables and level of maturity as output. Then, the system rules have been extracted from some experts and the system has been developed with the use of FIS tool of MATLAB software. Finally, the presented steps have been run in an IT company as empirical study. Index Terms-Maturity Assessment, Fuzzy Expert System, OPM3.

I. INTRODUCTION Project Management can be described as “a general purpose management tool that can bring projects to successful completion and to the satisfaction of the project stakeholders, given the traditional constraints, of defined scope, desired quality, budgeted cost, and a schedule deadline. Hence, project management is applicable to any organization with the core objectives of scope, quality, schedule and cost” [1, 2]. The need for project Management and the benefits that are possible from implementing project management methodologies are well documented and in many industries project management has already become both a central activity and the third element of organizational management systems that is bringing balance, harmony, and success in global organizations [1, 2]. Project management provides a special and distinct role, due to the organizational form of traditional structures, which is highly bureaucratic and cannot respond rapidly enough to a changing environment [1, 3]. Maturity models are seen as models that reflect certain aspects of reality, often called capabilities, and define qualitative attributes which are used to classify a competence object into one of several clearly defined areas. These classes are typically brought into a sequential order © 2012 ACEEE DOI: 02.CSA.2012.01.513


Full Paper Proc. of Int. Conf. on Advances in Computer Science and Application 2012 organizational project management maturity model (opm3) is not available, or you anxious to achieve to the intuition without using the particular software, what can you do to convince senior management of the benefits of adopting a different model? Can you trust your own intuition and experience? Where can you look for evidence that there is a better way of approaching the maturity of project management across an organization? [12] This paper presents the results of an investigation into the using a different way to foresight levels of maturity of project management based on the OPM3. This article mainly focuses on the evaluation of “OPM3” in an IT organization. The aim of this study is to design an Expert System which evaluating the maturity level of an IT company as Output based on major factors as Input variables. The factors consist of four main variables. Some project managers as the research experts identify the innovation culture level according to linguistic variables based on different situations of these four main factors. Since the experts’ judgment is explained with linguistic variables, using fuzzy functions and Fuzzy Inference system can be advantageous to build a basic knowledge system for assessing maturity situation in organization. The remainder of this paper is structured as follows: in the next section, the literature review about Project management maturity models and OPM3 are represented, in section 3 the concept of fuzzy expert systems is outlined, in section 4 the proposed system & empirical study process of this research is presented as a case study, In Section 5 the results and discussion are presented. Finally the article conclusions are drawn in Section 6.

addition to providing benchmarking information [14,20]. There are many maturity models developed for organizations. CMM, as the first maturity model, can be regarded as a onedimensional framework. The model defines five maturity development levels including Initial Repeatable, Defined, Managed and Optimizing to assess the maturity of software processes .Most maturity models are two-dimensional which keep in closer relationship with project management than CMM. PMS-PMMM is a matrix model. The first dimension reflects five maturity levels based on the SEI CMM. The second dimension depicts the key areas of project management concerned and adopts the Structure of nine PMI knowledge areas.[18] In November 2003, the U.S. Project Management Institute (PMI) launched the Organizational Project Management Maturity Model (OPM3), and promoted as the industry standard [5,21].At present, many studies of the project management maturity focused on empirical research, examined the stage of enterprise project management, and determined the enterprise project management in which a certain stage of maturity model.[14].OPM3 differs from other models in that it is three dimensional, so it is possible to determine the maturity of an organization from different perspectives in different ways on the basis of OPM3[18]. In a word, superior to any other management maturity model, OPM3 gives consideration to project management, program management and portfolio management, focuses on the continuous improvement of organizational project management, and creatively develops continuous structure by means of logic relevance. In addition, compared with CMM, PMSPMMM and P3M3 (P2MM), OPM3 enjoys some advanced features. The opm3 proposed by PMI provides a framework and guideline to assess the organizational project management capability [18].

II. THE LITERATURE REVIEW A. Project Management Maturity ModelsThe Literature A project is a temporary endeavor undertaken to create a unique product, service, or result [13]. Project management maturity models, as a subset of strategic planning for project management provide a means of identifying key steps, the tasks that need to accomplish, and the sequence of events needed to realize meaningful and measurable results. Basically, the purpose of the maturity model is to provide a framework for improving an organization’s business result by assessing the organization’s project management strengths and Weaknesses, enabling comparisons with similar organizations, and a measure of the correlation between an organization’s project management level and actual project performance [14, 15,16,17] A large numbers of complex and systematic projects are being established each year.[18] The concept of process maturity was born in the Total Quality Management (TQM) movement, where the application of statistical process control (SPC) techniques showed that improving the maturity of any technical process leads to two things: a reduction in the variability inherent in the process, and an improvement in the mean performance of the process [12,19]. Project management maturity models are important assessment tools for the profession. Maturity models identify organizational strengths and weakness in © 2012 ACEEE DOI: 02.CSA.2012.01. 513

B. OPM3 The Organizational Project Management Maturity Model (OPM3) falls naturally within the sequence of Standards published by the Project Management Institute (PMI). Organizational Project Management Maturity Model (OPM3) is to formulate such a standard, applicable to different sizes and forms, different industries and cultural organizations, to guide the organization to cultivate and enhance project management capabilities, namely, to achieve the organizational strategic goals by the success of the project [5]. OPM3 is comprised of three general elements: knowledge, presenting the contents of the Standard; Assessment, providing a Method for comparison with the Standard; and, improvement setting the Stage for possible organizational changes [22]. There are three basic elements to applying OPM3 in an organization: 1) The Knowledge element describes organizational project management and organizational project management maturity, explains why they are important, and how project management maturity can be recognized. 2) The Assessment element presents methods, processes and procedures that an organization can use to self-assess its maturity. 3) The Improvement element provides a process for 65

Full Paper Proc. of Int. Conf. on Advances in Computer Science and Application 2012 moving from its current Maturity to increased maturity. The Improvement element is what clearly differentiates OPM3 from other products in the marketplace [22] a “maturity model” is a conceptual framework, with constituent parts, that defines maturity in the area of interest—in this case, organizational project management. In some cases, such as with OPM3, a maturity model may also describe a process whereby an organization can develop or achieve something desirable, such as a set of Capabilities or practices. This process can result in a more highly evolved organizational state; in other words, a more mature organization. In OPM3, this is reflected by the combination of Best Practices achieved within the Project, Program, and Portfolio domains. A Best Practice is an optimal way currently recognized by industry to achieve a stated goal or objective. For organizational project management, this includes the ability to deliver projects predictably, consistently, and successfully to implement organizational strategies. Furthermore, Best Practices are dynamic because they evolve over time as new and better approaches are developed to achieve their stated goal. Using Best Practices increases the probability that the stated goal or objective will be achieved. OPM3 is a maturity model describing the incremental Capabilities that aggregate to Best Practices, and which are prerequisite to effective organizational project management. The progression of increasing maturity designed into OPM3 consists of several dimensions, or different ways of looking at an organization’s maturity. One dimension involves viewing Best Practices in terms of their association with the progressive stages of process improvement—from standardization to measurement to control and, ultimately, to continuous improvement. Another dimension involves the progression of Best Practices associated with each of the domains first addressing Project Management, then Program Management, and finally, Portfolio Management. Each of these progressions is a continuum along which most organizations aspire to advance. Also, within these two dimensions is the progression of incremental Capabilities leading to each Best Practice. Taken as a whole, these three dimensions constitute valuable reference points when an organization assesses its maturity in organizational project management and considers possible plans for improvement. OPM3 was intentionally designed without an overall system of “levels” of maturity. Establishing specific maturity levels can be relatively straightforward if the progression of maturity is one-dimensional. For example, as just discussed, there is a progression of four stages of process maturity from process standardization through continuous process improvement. OPM3, however, is multidimensional. In addition to the three dimensions described above, OPM3 also categorizes the Capabilities in terms of their association with the five project management process groups (Initiating, Planning, Executing, Controlling, and Closing), permitting evaluation of a fourth dimension of maturity. Multiple perspectives for assessing maturity allow flexibility in applying the model to the unique needs of an organization. This approach also produces a more robust body of information than is possible with a simpler, linear system of levels, giving the organization greater detail in © 2012 ACEEE DOI: 02.CSA.2012.01. 513

support of decisions and plans for improvement. III. FUZZY EXPERT SYSTEM METHODOLOGY Fuzzy expert systems use fuzzy data, fuzzy rules and fuzzy inference, in addition to the standard ones implemented in the ordinary expert systems [24]. Fuzzy inference systems can express human expert knowledge and experience by using fuzzy inference rules represented in “if-then” statements. The fuzzy inference process has five steps: Fuzzify inputs, apply fuzzy operator, apply implication method, aggregate all outputs and Defuzzify. In order to obtain a good FIS it is necessary that the researchers possess domain knowledge; the knowledge has to be represented in a symbolic form, be complete, correct and consistent [25]. Following the fuzzy inference mechanism, the output can be a fuzzy set or a precise set of certain features. Fuzzy inference infers the results from the existing knowledge base. 1) Fuzzy concept base: This contains the terminology and relevant predicate of a linguistic expression. Terminology is in the domain of the fuzzy set, possesses many pre-defined dismemberment values denoted by predicates. 2) Fuzzy proposition base: Membership functions accrue to the fuzzy proposition, which was induced from fuzzy concept base. There are numerous types of membership functions, such as S-shape, Z-shape, and P-shape, all easily definable with equations and parameters. There are different types of fuzzy systems are introduced. Mamdani fuzzy systems and TSK fuzzy systems are two types of fuzzy systems commonly used in literature that has different ways of knowledge representation. TSK (Takagi-SugenoKang) fuzzy system was proposed in an effort to develop a systematic approach to generate fuzzy rules from a given input–output data set. Numeric analysis approach of fuzzy system was first presented by Takagi and Sugeno and then a lot of studies have been made [26]. A basic Takagi–Sugeno fuzzy inference system is an inference scheme in which the conclusion of a fuzzy rule is constituted by a weighted linear combination of the crisp inputs rather than a fuzzy set and the rules have the following Structure: If x is A1 and y is B1, then

z1 = p1x + q1y + r1 .


Where p1, q1, and r1 are linear parameters. TSK Takagi–Sugeno Kang fuzzy controller usually needs a smaller number of rules, because their output is already a linear function of the inputs rather than a constant fuzzy set [27,28]. Mamdani fuzzy system was proposed as the first attempt to control a steam engine and boiler combination by a set of linguistic control rules obtained from experienced human operators. Rules in Mamdani fuzzy systems are like these: If x1 is A1 AND/OR x2 is A2 Then y is B1 (2) Where A1, A2 and B1 are fuzzy sets. The fuzzy set acquired from aggregation of rules’ results will be defuzzified using defuzzification methods like centroid (center of gravity), max membership, mean-max, and weighted average. The 66

Full Paper Proc. of Int. Conf. on Advances in Computer Science and Application 2012 centroid method is very popular, in which the ‘‘center of mass’’ of the result provides the crisp value. In this method, the defuzzified value of fuzzy set A, d (A), is calculated by the formula (3) d(A)=


where is the membership function of fuzzy set A .Regarding our problem in which various possible conditions of parameters are stated in form of fuzzy sets, the Mamdani fuzzy systems will be utilized due to the fact that the fuzzy rules representing the expert knowledge in Mamdani fuzzy systems, take advantage of fuzzy sets in their consequences, while in TSK fuzzy systems, the consequences are expressed in form of a crisp function [29, 30].

Fig.1. Three Gaussian Membership function for Standardize

IV. THE PROPOSED SYSTEM AND EMPIRICAL STUDY In this section we try to evaluate the maturity level of opm3 in organization, considering the level of each other of major effected variables on it .This expert system is designed for assessing the maturity degree of organizations based on PMI criteria. As it was mentioned in the Second section, OPM3 dimensions are included: Standardize (stan), Measure (meas), Control (cont), Continuous improve (Impr), Organizational Enabler (OE) [8]. Here the purpose is assessment of Maturity Level (ML) as output factor in an Iranian IT company according to the situation of these five dimensions as main input factors. Since the obtained ideas by the experts, managers and IT consultants, about the relation between the maturity level and each criterion, are not precise and have ambiguity, evaluation is done by linguistic variables. To do this, a Mamdani’s Fuzzy Expert system has been designed. This system is designed based on a set of obtained rules from experts regarding the relation between input variables and output. Some of the selected rules are shown in Table 1.

Fig.2. Three Gbell Membership function for Measure

Fig.3. Three Gaussian2 Membership function for Control


Fig.4. Three Gbell Membership function for Continuous improve

After specifying, input and output variables, a membership function is defined for each of them. These membership functions are illustrated in Figures 1 to 6. Fig.5. Five Gbell Membership function for Organizational Enabler

© 2012 ACEEE DOI: 02.CSA.2012.01.513


Full Paper Proc. of Int. Conf. on Advances in Computer Science and Application 2012

Fig.6. Five Gaussian Membership function for Maturity Level

In the next step, the discussed fuzzy expert system is designed by MATLAB software according to the obtained rules from the organization experts about the relation between Input variables and Output which are shown in table 1. To create a Fuzzy Inference System (FIS), MATLAB fuzzy logic toolbox provides a user friendly interface in which they can choose the intended specification from drop-down menus. >>fis = readfis (‘OPM3’) name: ‘OPM3’ type: ‘mamdani’ and Method: ‘min’ orMethod: ‘max’ defuzzs Method: ‘centroid’ imp Method: ‘min’ agg Method:’max’ input: [1*5 struct] output: [1*1 struct] rule: [1*12struct] Variables Range: [0 1]

Fig.8.Assessed Maturity level of Project by designed system

Fig.9.Assessed Maturity level of Portfolio by designed system

According to the experts’ opinions as the inputs, the following results have been identified: As a result, Maturity Level (ML) of IT Company for Plan would be 0.524 out of 1 and Maturity Level of IT Company for Project would be 0.28 out of 1 and finally Maturity Level of IT Company for Portfolio would be 0.485 out of 1. We can determine the Maturity Level of company by simple averaging of 3 Maturity Levels: (0.524+0.28+0.485)/ 3=0.43 Therefore Maturity Level of IT Company is 0.43 out of 1.

VI. RESULTS AND DISCUSSION This system is able to determine the innovation culture measure of organization based on the effective criteria. Regarding to the proposed fuzzy expert system, we have evaluated the maturity level in IT Co. for “Project”, “Plan” and “Portfolio” as Fig 7 to Fig 9.

CONCLUSIONS Assessing the maturity level of OPM3 based on effective criteria is the major fundamental in this paper. To reach this goal, a Mamdani’s Fuzzy expert system has been designed with considering the situation of four main effective factors on maturity in the organization as the Inputs and the maturity level as the output and Membership functions have been defined for the variables. Then, according to the rules which have been obtained from consultants and managers as the experts, the fuzzy expert system has been designed. This system has the ability to determine the organization OPM3 level based on criteria levels as an evaluator system. The most important advantage for this Fuzzy Expert system is predicting the degree of plan, project and portfolio. Finally, managers will be able to plan for the future works with considering the obtained results of the proposed system to improve their maturity current measure.

Fig.7.Assessed Maturity level of Plan by designed system

© 2012 ACEEE DOI: 02.CSA.2012.01.513


Full Paper Proc. of Int. Conf. on Advances in Computer Science and Application 2012 [15] M. Combe, “Standards committee tackles project management maturity models,” PM NETWORK. Vol.12, 1998 [16] F. Hartman, “Trends and improvements: Looking beyond modern project management,” The 28th Annual Project Management Institute Seminars & Symposium. Chicago, Project Management Institute, 1977. [17] W. Ibbs and Y.H. Kwak, “Assessing project management maturity”, Project Management Journal, vol. 31, 2000, pp. 32-43. [18] Guangshe, Jia, “Application of Organizational Project Management Maturity Model (OPM3) to Construction in China: An Empirical Study”, International Conference on Information Management, Innovation Management and Industrial Engineering, 2008, pp.56-62. [19] Cooke-Davies Terence J, Schlichter F. John, Bredillet Christophe, “Beyond the PMBOK1 Guide”, PMI Annual Seminars and Symposium, Nashville TS. 2001. [20] H. Supic, “Project management maturity of selected organizations in Croatia”, 8th International Conference on Telecommunications, Zagreb, Croatia, 2005. [21] Rose and Kenneth H, “Organizational project management maturity model (Book Review)”, Project Management Journal, pp.59-60, Jan.2004. [22] Project Management Institute (PMI), “Organizational Project Management Maturity Model Knowledge Foundation (OPM3)”, Project Management Institute, Newtown Square, 2003. [23] Project Management Institute (PMI), “Organizational Project Management Maturity Model Knowledge Foundation (OPM3)”, Project Management Institute, Newtown Square, 2008. [24] S.Pourdarab, H.Eslami Nosratabadi, M.Abbasian, “Design a Fuzzy Expert System to Evaluate Science and Technology parks”,4th international conference of Fuzzy Information and Technology,2010. [25] H.Iyatomi, M.Hagiwara , “Adaptive fuzzy inference neural network”, Pattern Recognition, No.37 (10) ,2004, pp. 20492057. [26] Y. S. Juang, S. S.Lin, H. P. Kao, “Design and implementation of a fuzzy inference system for supporting customer requirements”,Expert Systems with Applications, 32 (3) , 2007, pp. 868-878. [27] T.Takagi, M.Sugeno, “Fuzzy identification of systems and its applications of modeling and control”, IEEE Transactions of Systems Man and Cybernetics, USA,1985. [28] Pourdarab. S, EslamiNosratabadi.H, Nadali.A, “Risk Assessment of Information Technology Projects Using Fuzzy Expert System”, Communications in Computer and Information Science, Volume 166, Part 4, Springer2011, pp. 563-576. [29] A.Haji, M.Assadi, “Fuzzy expert systems and challenge of new product pricing”, Computers & Industrial Engineering,2009, 56(2), pp. 616-630. [30] Nadali.A ,Pourdarab. S, Mazloumi,A, EslamiNosratabadi.H, “Maturity Assessment of Business/IT Alignment Using Fuzzy Expert System”, Communications in Computer and Information Science, Volume 194, Part 20,Springer2011, pp.724-738.

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