Ranking of project risks based on the PMBOK standard by fuzzy DEMATEL

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1 University of East Anglia From the SelectedWorks of Amin Vafadarnikjoo Summer September, 2012 Ranking of project risks based on the PMBOK standard by fuzzy DEMATEL S.M. Ali Khatami Firouzabadi Amin Vafadar Nikjoo Available at:

2 Proceedings of the 10 th International Conference on Manufacturing Research ICMR 2012 RANKING OF PROJECT RISKS BASED ON THE PMBOK STANDARD BY FUZZY DEMATEL S.M. Ali Khatami Firouzabadi Amin Vafadar Nikjoo Department of Management and Accounting Allameh Tabataba'i University Tehran, Iran ABSTRACT In project risk management, there is a need to rank project risks, because there are many complexities in nowadays projects and prioritizing these risks can help to achieve more effective and efficient projects. In this research, on the basis of the Risk Breakdown Structure (RBS) of the Project Management Body of Knowledge (PMBOK Guide) fourth edition, it is tried to prioritize the different kinds of project risks. Many questionnaires are filled out by varied project experts in different Iranian project-based firms, to acquire the knowledge and experience of them in project risk management field. Then, fuzzy set theory was applied in order to measure experts' subjective judgments. Afterwards, a proposed Fuzzy Decision making Trial and Evaluation Laboratory (FDEMATEL) was utilized to rank the project risks. Consequently, the ranking of project risks on the basis of PMBOK standard according to the results analysis of the proposed FDEMATEL method is presented. Keywords: Fuzzy set theory, DEMATEL, PMBOK Guide, project risk 1. INTRODUCTION Risk management is an essential practice in achieving the successful and on time delivery of projects. Identification and prioritizing the most important risks of projects as a part of risk management process is a significant matter for project-oriented organizations. Risk in projects can be defined as the chance of an event occurring that is likely to have a negative impact on project objectives and is measured in terms of likelihood and consequence (Wideman 1992; Carter et al. 1993; Chapman 1998). Risk categories provide a structure that ensures a comprehensive process of systematically identifying risks to a consistent level of detail. The Risk Breakdown Structure (RBS) lists the categories and subcategories within which risks may arise for a typical project (PMBOK Guide, 2008). Risk Breakdown Structure (RBS) according to the fourth edition of PMBOK Guide is shown in Figure 1. As can be seen in Figure 1, main risk categories are "Technical", "External", "Organizational" and "Project Management". Technical risks are those which appear in relation to applied technology in the project. Organizational risks usually come into being when shortage of organizational resources exists. In this case, if appropriate plan for decrease or omit of these risks is not prearranged then the project will be faced to delay. One example for organizational risk is the lack of defining organizational priorities during the project implementation. External risks are not in the project managers' authority like inflation rate, environmental factors etc. Project management risks are connected to managing tasks like estimating, planning, controlling and communication. In the real world, project risks are rarely independent and usually have a degree of interactive relations and with this knowledge that the DEMATEL method is proper to be applied for considering the complicated relations between criteria and fuzzy set theory for measuring human's subjective judgments, consequently we adopted Fuzzy DEMATEL (FDEMATEL) for this study to prioritize the different kinds

3 of project risks according to risk breakdown structure of the 4 th PMBOK Guide to achieve more reasonable evaluation. The rest of this paper is organized as follows. In section 2, we show literature review of the research. The research methodology is described in section 3. Section 4 presents Results. Finally, the concluding remarks and future researches are provided in section 5. Figure 1: Risk Breakdown Structure (RBS) 2. LITERATURE REVIEW Here, we are going to review some researches that are related to risk ranking of projects. Baccarini and Archer (2001) described the use of a methodology for the risk ranking of projects undertaken by the Department of Contract and Management Services (CAMS)- a government agency in Western Australia (WA). Baccarini, Salm and Love (2004) determined 27 risks in IT projects by means of in-depth interviews with IT professionals from leading firms in Western Australia and also literature review. The two highest ranked risks, both in the literature and their survey, were "personnel shortfalls" and "unrealistic schedule and budget". Parker and Mobey (2004) used research in a major UK company on the introduction of an electronic document management system to explore perceptions of and attitudes to risk. Their paper identifies a number of factors, and builds a framework that should support a greater understanding of risk assessment and project management by the academic community and practitioners. Cervone (2006) tried to develop an understanding of the issues related to risk management in digital library projects as well as techniques for mitigating risk in these projects. Ebrahimnejad, Mousavi and Seyrafianpour (2010) used Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) and Fuzzy Linear Programming Technique for Multidimensional Analysis of Preference (FLINMAP) methods in order to rank high risks in Build-Operate-Transfer (BOT) projects. 3. RESEARCH METHODOLOGY This study tries to use fuzzy DEMATEL which is the proposed method for project risks ranking in Iranian project-based organizations. 3.1 Fuzzy Set Theory In decision-making problems, the evaluations given by experts are in linguistic expressions that are based on their experiences. These linguistic evaluations are ambiguous, that are hard to analyze. Hence, fuzzy set theory can be implemented to measure vague concepts associated with human's subjective judgments.

4 A fuzzy set is a class of objects with grades of membership. A membership function is a real number in the interval. Among the various shapes of fuzzy number, triangular fuzzy number (TFN) is the most popular one. A triangular fuzzy number can be defined as a triplet (a, b, c) where. The parameters a, b and c respectively, indicate the smallest possible value, the middle possible value and the largest possible value that describe a fuzzy event. The membership function of the fuzzy number, is defined as follows (see Figure 2) (Zadeh L. 1965, 1976; Zimmermann 2001). (1) Figure 2: Membership function of triangular fuzzy number A Defuzzification refers to the selection of a specific crisp element based on the output fuzzy set, which converts fuzzy numbers into crisp score. The converting fuzzy data into crisp scores (CFCS) which initiated by Opricovic and Tzeng (2003) will be used in this paper to calculate the left and right scores by fuzzy minimum and maximum and the total score is determined as a weighted average according to the membership functions. means the degree of criterion i that affects criterion j and fuzzy questionnaires k (k=1, 2, 3,, k). The CFCS method involves three-step procedure described as follows: 1. Normalization = (2) = (3) = (4) Where = (5) 2. Determination of right (rs), left (ls) normalized and total normalized crisp value: = (6) = (7) = (8) 3. Computation of final normalized and integrated crisp values of k respondent: = (9) = (10) 3.2 DEMATEL Method DEMATEL is a method for producing structural model among complex factors. This method was developed between 1972 and 1976 and was utilized for sophisticated problems. DEMATEL divides all factors into two groups: cause and effect; by using influence values between factors of the system. This

5 divide leads to better realization of system's elements and as a result finding solutions for solving complex system's problems (Gabus and Fontela, 1972, 1973; Herrera et al., 2000; Wang and Chuu, 2004). Steps of DEMATEL method (Fontela and Gabus, 1976) are explained below: 1. Generating the direct relation matrix. Expert team makes the pair-wise comparisons between criteria and the direct relation matrix A that is a n n matrix (n is the number of criteria) and (each element of matrix A) is a number that shows the impact value of criterion i on criterion j will be generated. 2. Normalizing the direct relation matrix. Normalized matrix of initial relationships can be calculated by using the following, (11) (12) 3. Attaining the total relation matrix. The total relation matrix (T) can be calculated by Eq. (13). I is denoted as the identity matrix. (13) 4. Producing a causal diagram. Sum of rows, D and sum of columns, R individually are calculated from matrix T by using Eq. (14) to (16): (14) The horizontal axis vector (D+R) is called "Prominence" which shows the relative significance of each criterion. The vertical axis (D-R) is called "Relation" and is made by subtracting D from R. Generally, when (D-R) is positive, then the corresponding criterion belongs to the cause group and when (D-R) is negative, the criterion is an element of effect group. 5. Obtaining the inner dependence matrix. In this step, the sum of each column in total relation matrix is equal to 1 by the normalization method, and then the inner dependence matrix can be resulted. 3.3 Proposed Fuzzy DEMATEL Method Step 1: Identifying expert team. The team consists of 8 experts who have rich knowledge and experience in project management in different Iranian project-based organizations. They are asked to fill out our questionnaire. Step 2: Identifying main project risks. In Figure 1 the Risk Breakdown Structure (RBS) on the basis of Project Management Body of Knowledge (PMBOK) fourth edition Guide is presented. Step 3: Determining relations Our experts were invited to fill out the questionnaire to evaluate the interrelationship of each risk using a five-point linguistic rating scale (i.e., 0 = no influence, 1 = very low influence, 2 = low influence, 3 = high influence, and 4 = very high influence), indicating the influence of each risk on other risk. Step 4: Replacing the linguistic information with fuzzy linguistic scale We use triangular fuzzy numbers (Table 1) for replacing the influence scores of linguistic information in direct relation matrix. Afterwards, we use equations (2) (10) for defuzzifying these fuzzy numbers and getting crisp values to use DEMATEL technique. (15) (16)

6 Table 1: The fuzzy evaluation scale Linguistic variable Influence score Triangular fuzzy numbers No influence 0 (0, 0.1, 0.3) Very low influence 1 (0.1, 0.3, 0.5) Low influence 2 (0.3, 0.5, 0.7) High influence 3 (0.5, 0.7, 0.9) Very high influence 4 (0.7, 0.9, 1.0) Step 5: Obtaining the causal diagram The normalized initial direct-relation matrix was produced by using Eq. 11 and 12.The total relation matrix was calculated by using Eq. 13 as shown in Table 2. The prominence and relation axes for cause and effect groups were computed by using Eq. 14 to 16 in MATLAB software are also presented in Table 2. If the (D-R) is negative, the risk is grouped into the effect group. Therefore, the causal diagram can be acquired by mapping the dataset of the (D+R, D-R), which presented in Figure 3. The causal diagram can give us valuable insight into the realization of the whole system and recognizing important risks. Table 2: Total relation matrix Technical External Organizational Project D R D+R D-R Management Technical External Organizational Project Management RESULTS Findings from the total relation matrix (Table 2) and the causal diagram (Figure 3) show that "Organizational" and "Project Management" risks are belong to effect group because their (D-R) scores are negative and they are tended to be easily impacted by other risks. On the other hand, "Technical" and "External" risks are in cause group because of positive scores of their (D-R) which means they are critical risks that can influence on the overall achievements of the organization. "Project Management" risk has the highest (D+R) score and it means its relative importance is greatest among other risks. In order to prioritize risks we must consider both prominence and relation axes. In overall consideration, we have the following ranking. "External" risk has gained the first rank because it has very high (D-R) score in comparison with others and the difference between "External" and "Project Management" risks in (D+R) score is relatively low. External > Technical > Project Management > Organizational 5. CONCLUSIONS In project risk management, there is a need to rank project risks, because there are many complexities in nowadays projects and prioritizing these risks can help to achieve more successful projects. In order achieve this goal we proposed a DEMATEL methodology in fuzzy environment. Our methodology considers interactions among different risks of projects according to risk breakdown structure of the 4 th PMBOK Guide. The result revealed that "external" risk is the most important risks in Iranian projectbased organizations. This study contains some limitations which can be further investigated for future researches. Number of experts can be increased to achieve more reasonable evaluations. To deal with uncertainty of the experts' judgments, different theories like gray theory and type-2 fuzzy set theory can be applied and compared. It is also worthwhile to rank sub-categories of main risks according to risk breakdown structure of the 4 th PMBOK Guide.

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