Soft modeling and explanation of causality between the risks affecting the return on investment in development of refinery units

Document Type : Original Article

Authors

1 PhD Student, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran

2 Professor, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran

3 Associate Professor, Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran

Abstract

Analysis and modelling the projects interdependency has become a vital and inevitable issue in project portfolio management. Present study focused on investment risks interdependency modelling in a project portfolio. Risk in projects as an integral element reduces the accuracy of the goals and the efficiency of the projects. Identifying, analyzing, prioritizing and having plans to deal with these potential negative elements play a significant role in the success of the projects. In this paper, using Delphi Method and fuzzy cognitive mapping (FCM) as a powerful tool in the field of soft knowledge domains occasionally soft operation research, the direct and indirect causality between the risks affecting the return on investment in the development of refineries in Iran are identified and explained. Thus, using a hybrid qualitative and methodological approach, cognitive processes and all the outcomes are based on the system of meaningfulness of the experts’ and professionals’ mental models in the field of refinery. Identified risks generally fall into four spheres of technology, marketing, finance and legal-political. Finally, using soft modeling, a network structure is presented as a potential negative factors affecting the return on investment, which leads to clarifying the dependencies and impact severity of forward and backward chaining. Furthermore, centrality criteria are used as a tool for static analyzing of created fuzzy cognitive map in order to interpret and give the meaning to the causal relationships between nodes.

Keywords

References

[1]   Pena Gonzalez, M., Barrionuevo, C. & Cedeno Moran, F. (2016). Fuzzy cognitive maps and computing with words for modelling project portfolio risks interdependency. International journal of innovation and applied studies, 15(4): 737-742.
[2]   Liu, M. , Jin, F., Xie, Q. & Skitmore, M., (2016). Improving risk assessment in financial feasibility of international engineering projects: A risk driver perspective. International Journal of project management, 35: 204-211.
[3]   Xie, G., Yue, W., Wang, Sh. & Keung Lai, K., (2010). Dynamic risk management in petroleum project investment based on a variable precision rough set model. Technological forecasting & social change, 77: 891-901.
 [4]  Salehi, M. & Hosseinpour, Z., (2016). Providing a new model for investment management in selecting investment projects using error analysis and fuzzy network analysis process. Journal of risk modelling and financial engineering, 1(2): 244-263.(in Persian)
 [5]  Virclics, A., (2013).Investment decision making and risk. Procedia Econ. Finance, 6: 169-177.
[6] Mojtahedi, M., Mousavi, M. & Makui, A. (2010). Project risk identification and assessment simultaneously using multi-attribute group decision making technique. Safety science, 48: 499-507.
 [7]  Aven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1): 1-13.
[8]   Carter, B., Hancock, T., Morin, J., & Robins , N. (1996). Introducing RISKMAN: The European Project Risk Management Methodology. Stationery Office.
[9]   Wideman, R., & Max, E. (1992). Project and program risk management: A guide to managing project risks and opportunities. Project Management Institute.
[10] Mohaghar, A., Hosseini dehshiri, S. & Arab, A. (2017). Investigating and evaluating project risks based on the best-worst-practice method. Human Resources Management Research, 7(2): 157-173. (in Persian)
[11] Ward, S., & Chapman, C. (1996). Project risk management processes, techniques and insights. New York: John Wiley & sons.
[12] A Guide to the Project Management Body of Knowledge: (PMBOK Guide) 4th Edition. (2008). Maryland: Project Management Institute.
[13] Najafi, A. A., & Pourahmadi, Z. (2016). An efficient heuristic method for dynamic portfolio selection problem under transaction costs and uncertain conditions. Physica A, 448: 154-162.
[14] Suliman Bu-Qammaz, A., Dikmen, I., & Birgonul, M. (2009). Risk assessment of international construction projects using the analytic network process. Canadian journal of civil engineering, 36(7):1170-1181.
 [15] Kalahan, F., rezaie-Nik, A., Hassani, M., ramezanpour& H., Tajaddod, A.(2015). Identifying and prioritizing the risks of development projects of the country's power industry (Case study: Transmission and Distribution Zone of Khorasan Regional Electricity Company). Industrial Engineering Journal, 49 (1): 107-116. (in Persian)
[16] Xiao, Z., Chen, W. & Li, L. (2012). An integrated FCM and fuzzy soft set for supplier selection problem based on risk evaluation. Applied mathematical modelling, 36: 1444-1454.
[17] Grey, S., Cooper, D., Walker, P., & Geoffrey , R. (2005). Project risk management guidelines: managing risk in large projects and complex procurements. New York: John Wiley & sons.
[18] Irani, Z., Sharif, A., Love, P. & Kahraman, C. (2002). Applying concepts of fuzzy cognitive mapping to model: The IT/IS investment evaluation process. International journal of production economics, 75(1-2): 199-211.
[19] Lazzerini, B. & Mkrtchyan, L. (2010). Risk analysis using extended fuzzy cognitive maps. Intelligent computing and cognitive informatisc international conference.
[20] Salmeron, J. (2010). Fuzzy cognitive maps advanced in theory, methodologies, tools and applications. Berlin: Springer.
[21] Dejdash, G., Zin, R. & Ferwati, M. (2017). DEMATEL-ANP risk assessment in oil and gas construction projects. sustainability, 9: 1-24.
[22] Fazli, S., Kiani Mavi, R. & vosooghidizaji, M. (2015). Crude oil supply chain risk management with DEMATEL-ANP. Operational Research, 15(3): 453-480.
[23] Khatami Firouzabadi, A., Vafadar Nikjoo, H. & Shahabi, A. (2013). Determining most significant project risk's categories with considering causal relations between them in the fuzzy environment. Management research in Iran, 17(3): 49-69. (in Persian)
[24] Khayrullina, A., Mingazova, A. (2016).   Content analysis of the risks specific to the refining industry enterprises in the Russian Federation.  Academy of strategic management Journal, 15(2): 121-130.
[25] Sa'idi, E., Anvaripour, B., Jaderi, F., Nabhani, N. (2014).   Fuzzy risk modeling of process operations in the oil and gas refineries. Journal of loss prevention in the process industries, 30: 63-73.
[26] Dey, P.K. (2002).   Quantitative risk management aids refinery construction. Hydrocarbon processing, March: 85-95.
[27] Jingmin, W., & Sun, Y. (2012). The intuitionistic fuzzy sets on evaluation ofrisks in projects of energy management contract. Systems Engineering Procedia, 3, 30-35
[28] Sayadi, A., Hayati, M., & Azar, A. (2011). Assessment and ranking of risks intunneling projects using linear assignment technique. international Journal of Industrial Engineeering and production management, 22(1): 27-38.
[29] Jabal-Ameli, M., Rezaeifar, A., & Bakhsh Langroudi, A. (2008). Ranking project risks using MADM methodologies. Journal of faculty of engineering (university of Tehran), 41(7): 863-871.(in Persian)
[30] Wyk, V., Riaan, Bowen, P., & Akintoy, A. (2008). Project risk management practice: The case of a South African utility company. International Journal of Project Management, 26(2):149-163.
[31] Jiahao, Z., An, M., & Jon Smith, N. (2007). Application of a fuzzy based decision making methodology to construction project risk assessment. International journal of project management, 25(6): 589-600.
[32] Linda, W., Keil, M., & Rai, A. (2004). Understanding software project risk: a cluster analysis. Information & Management, 42(1): 115-125.
[33] Tarek M., Z., & Chang, L.-M. (2002). Prototype model for build-operate transfer risk assessment. Journal of Management in Engineering, 18(1): 7-16.
[34] Yousefi, O., Naseri, P. & Nilipour Tabatabaei, A. (2014). Project risk assessment model using multi-objective decision-making approach (Case Study: The earth dam project Asalooye). Journal of Industrial Engineering, 48(1): 125-135. (in Persian)
[35] Ana, N. M & Ruz-Vila, F. (2011). A fuzzy approach to construction project risk assessment. International Journal of Project management, 29(2): 220-231.
[36] Jafarnejad, A., & Usefi, Z. (2008). Presenting fuzzy risk ranking model in petropars company drilling projects. Industrial management journal, 21-38. (in Persian)
[37] Wang, Ying-Ming, & MS Elhag, T. (2007). A fuzzy group decision making approach for bridge risk assessment. Computers & Industrial Engineering, 53(1): 137-148.
[38] Min, L., & F. Wu. , F. (2007). Risk management in a competitive electricity market. International Journal of Electrical Power & Energy Systems, 29: 690-697.
[39] An, M., Baker, C., & Zeng, J. (2005). A fuzzy-logic-based approach to qualitative risk modeling in the construction process. World Journal of Engineering, 2: 1-12.
[40] Sid, G., & Jintanapakanont, J. (2004). Identifying and assessing the critical risk factors in an underground rail project in Thailand: a factor analysis approach. International Journal of Project Managemen, 22(8): 633-643.
[41] Azar, A., Khosravani, F. & Jalali, R. (2016). Soft operational research; problem structuring methods. Tehran: Industrial Management organization poblication. (in Persian)
[42] Pidd, M. (2009). Tools for Thinking; Modelling in Management Science. (3rd ed.) Chichester: John Wiley and Sons Ltd.
[43] Naghibi, H., Fareskhani, H., Kasaie, M. & Zandieh, M. (2016). Explaining the Influential Components in the World-Class Production Structure in the Automotive Industry. Modern researches in decision making, 1(4): 167-186. (in Persian)
[44] Azar, A. & Mostafaie, K. (2012). Fuzzy Cognitive Mapping A New Approach to Soft Modeling: Modeling at the Center of Statistics of Iran. Management research in Iran, 16(3): 83-103. (in Persian)
[45] Kosko, B. (1986). Fuzzy cognitive maps. Int. J. Man-Machine studies, 24: 65-75.
[46] Sadeghi-Moghaddam, M. (2012). Provide a model for coordinating the automotive supply network by applying a causal mapping (Case Study: Automotive Network Provision. Industrial management journal of Tehran university, 4(2): 87-112. (in Persian)
[47]  Barzdeh, S. & Taghavi-fard, M. (2013). Design and development of a law-based fuzzy expert system for assessing the credit of business customers. Commercial Management Journal of Tehran University, 5 (2): 17-46. (in Persian)
[48] Nasrabadi, A., Hosseinpour, M., & Ebrahimnejad, H. (2013). Strategy-alligned fuzzy approach for market segment evaluation selection: a modular decision support system by dynamic network process (DNP). Journal of Industrial Engineeering international, 9: 1-17.
[49] Jafari-Eskandari, M. & Farhang, M. (2015). Designing a Fuzzy Cognitive Map Model of Factors Affecting Cost-Time-Quality in South Pars Gas Complex Projects. Monthly Oil and Gas Exploration and Production, (125): 30-37. (in Persian)
 

Files

Share

How to cite

Statistics