The Risk Analysis of Oil Projects Using Fuzz ANP Technique

Transcription

1 International Research Journal of Management Sciences. Vol., 2 (2), 55-61, 2014 Available online at ISSN x 2014 The Risk Analysis of Oil Projects Using Fuzz ANP Technique (Case Study: 18-inch Pipeline Repair Project from Cheshme Khosh to Ahwaz) Mohammad Moradi Majd 1, Adel Fatemi 2, Heresh Soltanpanah 3 1 MA of Executive Management of Islamic Azad University, Sanandaj Branch 2 Assistant Professor of Management Department of Islamic Azad University, Sanandaj Branch 3 Assistant Professor of Management Department of Islamic Azad University, Sanandaj Branch ABSTRACT: Risk management is one of the main topics in project management which provides the possibility of risk ranking based on their criticality and timely response. This is much more important in big industrial projects including oil projects. To this end, the present study first identifies 116 main risks in big 18-inch pipeline repair project from Cheshme Khosh to Ahwaz based on experts' panels and interview and ranks five main evaluation indices, i.e. time, quality, safety, cost and environmental effects based on fuzzy TOPSIS. Then the probability of occurrence and effectiveness of each identified risk on five indices were investigated through questionnaire and at the end, the final ranking of each risk was identified. The results of the study showed that five identified critical risk in the studied project are sanction, inflation, rapid changes in rate of materials and equipment, rapid changes of exchange rate and at the end, impossibility of having access and transaction for supplying foreign goods. At the end, some practical and research suggestions have been provided. Keywords: Risk Management, ANP technique and fuzzy logic. INTRODUCTION Project management is among those sciences where decision making doesn t follow certain system. From the emergence of topics such as planning and project control, this fact that times are estimated and cost coefficients have indefinite nature reinforced the use of statistics and probabilities in this regard. However, various statistical distributions have not the ability to apply directly the mental inferences of experts from the time of performing activities, their costs and things like thin. As defined, risk includes those events which in case of occurrence can lead to deviation of project from main and predetermined objectives. The presence of risk in project indicates that there is uncertainty on implementation and execution of projects (Mubin et al., 2008). Fuzzy calculations are good instrument for modeling and measuring these uncertainties. Making use of fuzzy calculations in ranking of projects' risk includes the following benefits: 1. Direct application of mental inferences of experts in the model; 2. More compatibility of allocated weights to target criteria in final decision-making; 3. Accessibility to more objective and real results in risk analysis and ranking (Jafar Nejad et al., 2008). In this study, fuzzy ANP new approach will be presented for measuring probable risks in a pipeline repair project and their rankings. Accurate identification and prioritization of governing risks on one project provide the possibility of planning and designing a proper plan for responding and controlling risks in project and have great effect in successful implementation of projects. Project management is the systematic application of managerial policies and related activities to analysis, evaluation and risk control. Risk management includes the documentation process of final decisions and the identification and use of criteria which can be used to direct risk to an acceptable level (Asadi, 2009). Project risk management includes "all processes related to identification, analysis and responding to any unreliability including maximization of the results of desired events and minimization of undesired results (Haleh & Karimian, 2010). Corresponding Author: address: tanin85@gmail.com 55

2 Theoretical principles ANP technique is an extended form of AHP. While AHP deals with providing a framework or unidirectional hierarchical communication, ANP consider the more complex inner communication between decisions and ratio levels. In ANP approach with feedback, networks have been replaced for chain where communication between levels is not easily offered as higher or lower, dominant or dominating, direct and indirect. As an example, not only the significance of criteria determines the significance of options as a chain, but only the significance of options might be effective on the significance of criteria. Thus, a top-down linear hierarchical structure is not applicable for a complex system (Persian, 2008). In this study, fuzzy ANP (FANP) was used for ranking of risks. This method is appropriate in cases when the dependency between selection criteria of possible options is very high, such that FANP easily determines the relation between criteria (Mohanty et al., 2005). In this study, since risks are effective on each other, this method will be very good. In this method, pair comparison matrixes between the criteria of each row are completed using triangular fuzzy numbers. Based on this method, the values of parameters will be obtained in form of triangular fuzzy numbers and calculated in form of fuzzy. In pair comparison of options (criteria), the decision maker (expert) can use triangular fuzzy numbers to determine the priority of options. Clockwise 1-9 spectrum is used for pair comparisons in ANP. Although this spectrum can be easily used, this spectrum does not include uncertainty and ambiguity of the perceptions and judgment of individuals of the degree of priority. In other words, the decision maker might not be able to express certain number as priority level in comparing some options. This is due to the fact that a 1-9 fuzzy spectrum can be used for triangular fuzzy numbers instead of 1-9 logical spectrum. When it i is to be compared with j, show the equal priority between compared criteria, respectively, and the lower priority of I in comparison with j, strong priority of I to j, much stronger priority and absolute priority of i rather than j. To evaluate the priority of decision maker, the pair comparison matrix is constituted using triangular fuzzy numbers (l, m, u). m*n matrix with triangular fuzzy numbers can be shown as follow: [ ] In this matrix, indicate the significance of i-the row element in comparison with j-the column component. If is a paired comparison matrix, it is assumed that the components of this matrix is reverse in terms of main diameter. Thus, can be allocated to. Thus, the pair comparison matrix becomes as follow: [ ] There are many ways for estimation of fuzzy weights based on matrix with approximate value of such that is obtained per i= 1, 2,, n. One of these methods, it the least square logarithm (Chen et al., 1992) which is the basis for fuzzy weight calculations in this project. In this method, triangular fuzzy weights can be calculated for criteria, options etc, such that the output of the weights of this method could be used in fuzzy TOPSIS approach for ranking of used options (Semih, 2009). Least square logarithm method for calculation of fuzzy weights has been shown as follow: ( ) Such that: [ ] { } Furthermore, concerning the internal relation between criteria, use of ANP method for responding to risk is more appropriate than AHP technique; since ANP technique is a more complete method rather than AHP technique. In fact 56

3 the process of network analysis leads to improvement of hierarchical analysis through replacing the network for hierarchy (Saati, 2004). Review of literature Josie and Irankhahi (2010) investigated the environmental risk of gas transfer pipelines through AHP mixed method. They proposed a mixed method from indexing system and hierarchical analysis process for evaluation of environmental risk of gas transfer pipelines. Indexing system is a comprehensive and applicatory system which has been constituted based on determining the effect index and scoring. Through this method, it is possible to quantitatively classify and prioritize the environmental risks of pipelines based on determined indices and criteria. Furthermore, in Jebel Ameli et al (2007) study, the ranking of project risk has been investigated through multivariate decision-making methods and techniques. The case study presented in this study deals with risk ranking through TOPSIS method. Furthermore, ANP technique is one of the effective techniques for ranking of project risks. Dory et al (2010) studied the mixed approach in risk analysis in another study. ANP-FMEA considers mutual communication susceptible to risk and by providing developed structure, provide a systemic and flexible approach in risk management. This method expands the simple concept of risk priority and considers various degree of significance for FMEA parameters. In Mubin et al study (2008), entitled the transforming of project risk management to project unreliability management, the researchers divide the unreliability range in the project to 5 categories after defining uncertainty and its management in the project: 1. High variation range in estimation (time, cost, etc.), 2. Uncertainty on the used principles in estimations, 3. Uncertainty of designing and provision, 4. Uncertainty of objectives and priorities and 5. Uncertainty related to main communication between various parts of project. The repair projects are the biggest and most important projects in oil industry and are naturally full of small and big risks for which certain planning should be considered. In this regard, 18-inch pipeline repair project from Cheshme Khosh to Ahwaz is one of the main repair projects which has been carried out with the aim of higher utilization in oil transfer. In this study, it is tried to make relation between risk management and fuzzy logic and introduce new method with proper applicability. Then, this method is implemented in 18-inch pipeline repair project from Cheshme Khosh to Ahwaz. All the identified risks are ranked through fuzzy method proportionate to the effect size and probability of occurrence. The length of 18-inch oil pipeline from Cheshme Khosh to Ahwaz is 156 km, extended from oil utilization unit of Cheshme Khosh in 15 km of Dasht Abbas (south of Ilam province) to Ahwaz pump house 3 in 20 km of Ahwaz. Its task is to transfer the produced oil of seven oil fields of Dehloran, Danan, Dalpori, Cheshme Khosh, Paydar Qarb and Aban with nominal capacity of 150,000 gallons daily. The main aim of this study is fuzzy ranking of risk management of oil projects using fuzzy ANP technique. METHODOLOGY Since this study has been carried out in a real, objective and dynamic organization and its results can be practically used, this is an practical study. It is a descriptive- analytical survey in terms of methodology. To identify risks, early indices related to conceptual model of the study were extracted from experts idea in form of experts panel, and then they were collected and classified in two main groups: 1. Feasibility study (before implementation) and 2. Operational (during implementation) and 12 subcategories: 1. Natural, local, regional; 2. Laws, regulations and bylaws; 3. Economic; 4. Political; 5. Planning; 6. Detailed design; 7. Contractual; 8. Evaluation; 9. Organizational; 10. Supervision on implementation; 11. Technical and executive and 12. Contractor. To this end, various criteria have been introduced for project risk management through investigation of local and foreign studies. Then, five criteria were selected as evaluation criteria after performing required investigation and knowing the opinions of experts. By influencing them, the project risks can affect the overall objectives of project. These criteria include 1. Time; 2. Cost; 3. Safety coefficient; 4. Quality and 5. Environmental issues. Five mentioned indices were weighted based on experts opinion through fuzzy ANP method. The identified risks and the above indices have been investigated and evaluated as the main variables of this study. The research questions have been responded through quantification and analysis. In this research, laboratorial study was used for familiarization with the main practical concepts of the study (such as, risk management, project risk, risk ranking and etc). Furthermore, experts panel was used for identification of risks and fuzzy ANP questionnaire was used for ranking of indices. Furthermore, reliability and validity of research were investigated through Cronbach's Alpha. Population and statistical sample Sampling method of this study has been census and the sample size equals to population, i.e. 32 individuals. These 32 individuals have been active in all 18-inch pipeline repair projects of Cheshme Khosh to Ahwaz and informed of various aspects of project. 13 out of 32 individuals had BA degree and 8 had MA degree. 11 individuals were engaged 57

4 in implementation section and 8 individuals were engaged in operational section. 11 individuals were in age range of year and 9 individuals had 20 years of working experience. RESULTS The identification process of risk includes the identification or effective risks on project and documentation of their characteristics. For realization of effective risks, experts panels, direct interview with managing director, mid manages and executive managers, designers, experts and contractors were used and various meetings were hold with them (totally 32 individuals). In these sessions, 116 risks were identified and determined. It should be noted that these interviews were done individually and in group in appropriate space after explaining the subject and expressing the objective. It has been tried to make respondent express their opinion about the status of 18-inch pipeline repair project from Cheshme Khosh to Ahwaz, regardless of their executive post and position, and identify the risks of this project. After the identification of risks, data collection procedure is related to probability of occurrence of a risk and the effect of each risk on each criterion including time, cost, quality, safety and environment. To this end, a questionnaire was prepared based on the list of identified risks and distributed among the individuals who were previously interviewed to determine the probability of occurrence, the effect on cost, effect on time, effect on quality, effect on safety and effect on environment through questionnaire. Figure 1. Research graph. The procedures for obtaining the weight of components through fuzzy network analysis include: To collect the experts' idea, the pair comparisons of respondents are geometrically averaged; The eigenvector was calculated; The eigenvector matrix was formed ( ): these matrix include eigenvectors which have been obtained from paired comparison of the second stage; The calculation of final weights of levels: to calculate final weight of components of each level ( ), we should multiply the eigenvector matrix of internal relation by the eigenvector of the final weight of the higher level. Table 1. The matrix of final weights of criteria in respect to objective (though fuzzy ANP method). Component Final fuzzy weight Final definite weight of components Cost (0.291, 0.378, 0.472) Safety (0.207, 0.279, 0.359) 0.28 Quality (0.135, 0.18, 0.237) Time (0.081, 0.106, 0.14) Environmental (0.046, 0.058, 0075)

5 criteria Figure 2. The graph of final weights of criteria in respect to objective. CONCLUSION Project management can be considered as use of knowledge, instruments and techniques through which it is possible to fulfill projects needs and achieve its objectives. Nowadays, projects implementation in the country faces many problems. These problems lead to emergence of delays and increase of projects crises. Thus, a huge part of national capital loses. The aim of the present study is to analyse the risk of oil projects using fuzzy ANP technique in 18-inch pipeline repair project from Cheshme Khosh and Ahwaz. The main problem in planning and decision making is related to risk management, uncertainty of the probability of occurrence of predicted phenomenon. In confronting with these uncertainties, the fuzzy management can be a new approach for solving the problems and responding to ambiguities in managerial systems by the use of fuzzy theory system. Fuzzy system theory can enter some parameters such as knowledge, experience, judgment and human decision making into the model and provide a gray picture of gray world in addition to creating flexibility in the model (Azar & Faraji, 2008). In 18-inch pipeline repair project from Cheshme Khosh to Ahwaz, 116 small and big risks were identified through experts panel and detailed interview with managers and experts. The identified risks were included in two main groups and 12 subgroups based on the influence of risk on each of five objectives, i.e. time, cost, quality, safety and environment. In fact, risk X in the project can be considered as critical risk. When it leads to increase of project time, it increases project cost, reduces the project quality based on predetermined objectives, endangers the safety of project and in case of occurrence damages environment. All information related to project risks including probability of occurrence or the effects on the project s objectives are somehow extracted from the minds of project s managers and experts. Thus, a method should be created which can transform the ideas of the managers to statistics and numbers which are usable for planning and risk management. In fact, here we face with a decision making issue. A review of related literature to decision-making indicates that fuzzy management can extract the mental decisions of managers and transform them to usable numbers (Baradaran Kazemi Zadeh, 2011). In this regard, the present study uses strong technique of fuzzy decision-making, i.e. fuzzy ANP for ranking of projects objectives. The final results obtained from this ranking for 20 first risks which can be critical risks are presented in table 2. 59

6 Table 2. Ranking of 20 first risks based on two fuzzy ANP methods. anking Risk explanation Risk Number 1 Sanction 24 2 Inflation 20 3 Rapid changes in rate of materials and equipment 19 4 Variation of exchange rate 21 5 Inaccessibility and transaction with foreign products supply 19 6 Lack of economic stability and market price oscillation 23 7 Failure to supply project timely 18 8 Resistance of owners and opponents of lands in pipeline path 50 9 Weakness in early planning and timing of project Weakness of employer s management and lack of expert forces in contractor 110 team and his executive agents 11 Flooding of drilled canals due to irrigation of agricultural lands Significance of project and the probability of winning of weak employers Lack of program and comprehensive demand for preparation and supplying of 68 all products in proper time 14 Weakness of supervisory system in control and implementation of related 67 standards in all project implementation phases 15 Inefficient time plan for project s implementation Lack of liquidity and delay in paying contractor s bill based on contract and 91 done work 17 Lack of materials and equipment and their accessibility Weak managerial attitude in reducing design time and rapid transfer to 31 implementation phase 19 Weakness of employer in supplying the equipment according to technical 109 standards of project 20 Lack of standards and quality control systems in contractor s organization 111 As seen in table 2, sanction and inflation are two first identified risks. Concerning the results of this study, the following recommendations seems necessary: removing of intermediaries and purchasing from known supplying local, attracting the sellers' trust from definite purchase and on time payment, making international agreements by government, collaboration of private sector in investment, attraction of various local capitals through selling bonds, making international agreement by the government, supporting local manufacturers, installation of R & D unit, making effective relation between knowledge-based institutions and science and technology parks. Furthermore, the future researchers in this area are recommended to study risk quantitative findings for calculation of timing and budgeting of projects. Moreover, other ranking methods and comparison of their results with the results of the present study can be significant. REFERENCES Asadi MM, Presenting a model for risk management of projects in oil and gas industry. 5th international conference of project management, August 2009, Tehran. Azar A, Faraji H, Fuzzy management science. Tehran: Ketab Mehraban Publication. Baradaran Kazemi Zadeh R, Sharif Mousavi SM, Presenting an evaluation model of fuzzy risk for evaluation of temporal risk of civil projects: reconstruction project of office of the railway line and technical buildings of I.R.I. Human Sciences Instructor. 1: Chen CL, Bullington SF, Development of a strategic research plan for an academic department through the use of quality function deployment. Computers & Industrial Engineering. 25(1-4): Dory B, Moazzazi H, Salami H Integrated approach to risk analysis using FMEA and FANA. Human Sciences Instructor. 14(4): Haleh H, Karimian H, The selection of the most appropriate structure to improve system reliability by using analytic network process (ANP). International Journal of Industrial Engineering and Production Management. 3(21): Jafar Nejad A, Yusofi Zonooz R, Presenting a fuzzy module for risk ranking in Perto Pars s drilling projects. Journal of Industrial Management. 1(1):

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