شناسایی و ارزیابی ریسک در پروژه های ساخت پتروشیمی در ایران؛ مطالعه موردی: هلدینگ پتروشیمی باختر

نوع مقاله : یادداشت پژوهشی

نویسندگان

دانشکده مهندسی صنایع و سیستم ها، دانشگاه تربیت مدرس، تهران، ایران

چکیده

یکی از اساسی‌ترین موضوعات مطرح در بسیاری از سازمان‌های پروژه‌محور، مدیریت ریسک پروژه‌های درحال انجام آن سازمان است. در این راستا در گام نخست می‌بایست ریسک‌های بالقوه پروژه‌ها، تحت ساختاری نظام‌مند و فراگیر، شناسایی شده و پس از آن با بهره‌گیری از روش‌های مناسب و مطلوب به ارزیابی آن‌ها از حیث احتمال رخداد و نیز میزان اثرگذاری آن‌ها بر اهداف پروژه پرداخته شود. در این مطالعه، با در نظرگیری شرکت هلدینگ پتروشیمی باختر به عنوان یکی از بزرگترین شرکت‌های ایجاد کننده و بهره‌بردار پروژه‌های پتروشیمی در ایران به عنوان مطالعه موردی، به شناسایی و ارزیابی عدم‌قطعیت‌ها و ریسک‌های جاری و محتمل پروژه‌های ساخت پتروشیمی در ایران پرداخته شده است. برای شناسایی ریسک‌ها از دو روش بررسی مستندات و مصاحبه بهره برده شده است. این فرآیند منجر به شناسایی و طبقه‌بندی 104 ریسک بالقوه در پروژه‌های پتروشیمی، در دو دسته و 8 زیردسته، شده است. همچنین از آن‌جا که ارزیابی ریسک‌ها یک فرآیند تصمیم‌گیری شهودی، ذهنی و وابسته به تجربیات گذشته است، برای این مرحله از روش منطق شهودی استفاده شده است. روش منطق شهودی که روشی مبتنی بر تصمیم‌گیری با معیارهای چندگانه و تئوری شواهد دمپستر-شافر است، برای مواجهه با چنین قضاوت‌های شهودی و نادقیقی که در فضایی نامطمئن و با نقص در دسترسی به اطلاعات و شواهد انجام می‌گیرد، روشی کارا و سودمند است. استفاده از این روش، علاوه بر ارزیابی و رتبه‌بندی ریسک‌های شناسایی شده، منجر به اندازه‌گیری سطح شناخت از ریسک‌های شناسایی شده نیز شده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Identification and assessment of risks in petrochemical projects in Iran; case study: Bakhtar Petrochemical Company

نویسندگان [English]

  • Mohammad Shariatmadari
  • Nasim Nahavandi
Faculty of Industrial & Systems Engineering, Tarbiat Modares University, Tehran, Iran
چکیده [English]

One of the most important issues in many project-oriented organizations is the risk management of the ongoing projects of that organization. In this regard, in the first step, the potential risks of projects should be identified under a systematic and comprehensive scheme, and then, using appropriate and desirable methods, they should be evaluated in terms of their probability and their impact on project goals. In this study, considering Bakhtar Petrochemical Company, a holding company, as one of the largest companies that construct and operate petrochemical projects in Iran as a case study, the current and potential uncertainties and risks of petrochemical projects in Iran have been identified and evaluated. Two methods, namely documentation review and interview, have been used to identify risks. This procedure has led to the identification and classification of 104 potential risks in petrochemical projects in two categories and eight sub-categories. Also, since the risk assessment is an evidential, intuitive, and empirical decision-making process, the Evidential Reasoning approach has been used for this stage. Evidential Reasoning, based on the Multi-Attribute Decision-Making approach and Dempster-Shafer's Theory of Evidence, is a useful and effective method to deal with overly intuitive and inaccurate judgments made in this uncertain environment. Using this method, in addition to assessing and ranking the identified risks, has also produced a measurement of the level of recognition of the identified risks.

کلیدواژه‌ها [English]

  • Petrochemical Projects in Iran
  • Risk Identification
  • Risk Assessment
  • Dempster-Shafer’s Theory of Evidence
  • Evidential Reasoning Approach
  • Zhi, H. (1995). Risk management for overseas construction projects. International Journal of Project Management, 13(4), 231-237.
  • Ling, F. Y. Y., & Hoi, L. (2006). Risks faced by Singapore firms when undertaking construction projects in India. International Journal of Project Management, 24(3), 261-270.
  • Peckiene, A., Komarovska, A., & Ustinovicius, L. (2013). Overview of risk allocation between construction parties. Procedia Engineering, 57, 889-894.
  • Choudhry, R. M., Aslam, M. A., Hinze, J. W., & Arain, F. M. (2014). Cost and schedule risk analysis of bridge construction in Pakistan: Establishing risk guidelines. Journal of Construction Engineering and Management, 140(7), 04014020.
  • Adafin, J., Rotimi, J. O., & Wilkinson, S. (2016). Risk impact assessments in project budget development: architects’ perspectives. Architectural Engineering and Design Management, 12(3), 189-204.
  • Ou-Yang, C., & Chen, W.-L. (2017). Applying a risk assessment approach for cost analysis and decision-making: a case study for a basic design engineering project. Journal of the Chinese Institute of Engineers, 1-13.
  • Du, L., Tang, W., Liu, C., Wang, S., Wang, T., Shen, W., Zhou, Y. (2016). Enhancing engineer–procure–construct project performance by partnering in international markets: Perspective from Chinese construction companies. International Journal of Project Management, 34(1), 30-43.
  • KarimiAzari, A., Mousavi, N., Mousavi, S. F., & Hosseini, S. (2011). Risk assessment model selection in construction industry. Expert Systems with Applications, 38(8), 9105-9111.
  • Wang, J., Guan, S., & Lin, D.-q. (2010). Study on approach of cost risk assessment in bidding phase. Internet Technology and Applications, 2010 International Conference.
  • Arslan, G., Tuncan, M., Birgonul, M. T., & Dikmen, I. (2006). E-bidding proposal preparation system for construction projects. Building and Environment, 41(10), 1406-1413.
  • Lu, W., Zhang, L., & Pan, J. (2015). Identification and analyses of hidden transaction costs in project dispute resolutions. International Journal of Project Management, 33(3), 711-718.
  • Hwang, B.-G., Zhao, X., & Toh, L. P. (2014). Risk management in small construction projects in Singapore: status, barriers and impact. International Journal of Project Management, 32(1), 116-124.
  • institute, P. m. (2008). A guide to the project management body of knowledge.
  • Sukumaran, P., Bayraktar, M. E., Hong, T., & Hastak, M. (2006). Model for analysis of factors affecting construction schedule in highway work zones. Journal of transportation engineering, 132(6), 508-517.
  • Nieto-Morote, A., & Ruz-Vila, F. (2011). A fuzzy approach to construction project risk assessment. International Journal of Project Management, 29(2), 220-231.
  • Gładysz, B., Skorupka, D., Kuchta, D., & Duchaczek, A. (2015). Project risk time management–a proposed model and a case study in the construction industry. Procedia Computer Science, 64, 24-31.
  • Cárdenas, I. C., Al‐Jibouri, S. S., Halman, J. I., & Tol, F. A. (2014). Modeling Risk‐Related Knowledge in Tunneling Projects. Risk analysis, 34(2), 323-339.
  • El-Sayegh, S. M. (2008). Risk assessment and allocation in the UAE construction industry. International Journal of Project Management, 26(4), 431-438.
  • Taylan, O., Bafail, A. O., Abdulaal, R. M., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-116.
  • Chapman, R. J. (2001). The controlling influences on effective risk identification and assessment for construction design management. International Journal of Project Management, 19(3), 147-160.
  • Kartam, N. A., & Kartam, S. A. (2001). Risk and its management in the Kuwaiti construction industry: a contractors’ perspective. International Journal of Project Management, 19(6), 325-335.
  • Serpell, A., Ferrada, X., Rubio, L., & Arauzo, S. (2015). Evaluating risk management practices in construction organizations. Procedia-Social and Behavioral Sciences, 194, 201-210.
  • Dziadosz, A., & Rejment, M. (2015). Risk Analysis in Construction Project-Chosen Methods. Procedia Engineering, 122, 258-265.
  • Lyons, T., & Skitmore, M. (2004). Project risk management in the Queensland engineering construction industry: a survey. International Journal of Project Management, 22(1), 51-61.
  • Kuo, Y.-C., & Lu, S.-T. (2013). Using fuzzy multiple criteria decision making approach to enhance risk assessment for metropolitan construction projects. International Journal of Project Management, 31(4), 602-614.
  • El, M. S. B. A. A., El Nawawy, O. A. M., & Abdel-Alim, A. M. (2015). Identification and assessment of risk factors affecting construction projects. HBRC Journal.
  • Baghdadi, A., & Kishk, M. (2015). Saudi Arabian aviation construction projects: Identification of risks and their consequences. Procedia Engineering, 123, 32-40.
  • Liu, Z.-z., Zhu, Z.-w., Wang, H.-j., & Huang, J. (2016). Handling social risks in government-driven mega project: An empirical case study from West China. International Journal of Project Management, 34(2), 202-218.
  • Samantra, C., Datta, S., & Mahapatra, S. S. (2017). Fuzzy based risk assessment module for metropolitan construction project: An empirical study. Engineering Applications of Artificial Intelligence.
  • Dang, C. N., Dang, C. N., Le-Hoai, L., Le-Hoai, L., Kim, S.-Y., Kim, S.-Y., . . . Lee, Y.-D. (2017). Identification of risk patterns in Vietnamese road and bridge construction: contractor’s perspective. Built Environment Project and Asset Management, 7(1), 59-72.
  • Mhatre, T. N., Mhatre, T. N., Thakkar, J., Thakkar, J., Maiti, J., & Maiti, J. (2017). Modelling critical risk factors for Indian construction project using interpretive ranking process (IRP) and system dynamics (SD). International Journal of Quality & Reliability Management, 34(9), 1451-1473.
  • Van Thuyet, N., Ogunlana, S. O., & Dey, P. K. (2007). Risk management in oil and gas construction projects in Vietnam. International journal of energy sector management, 1(2), 175-194.
  • Mubin, S., & Mannan, A. (2013). Innovative Approach to Risk Analysis and Management of Oil and Gas Sector EPC Contracts from a Contractor's Perspective. Journal of Business & Economics, 5(2), 149.
  • El-Shehaby, M., Nosair, I., & Sanad, A. E.-M. (2014). Risk assessment and analysis for the construction of off shore oil & gas projects. J. Sci. Res. Educ.
  • Baloi, D., & Price, A. D. (2003). Modelling global risk factors affecting construction cost performance. International Journal of Project Management, 21(4), 261-269.
  • Asgari, S., Awwad, R., Kandil, A., & Odeh, I. (2016). Impact of considering need for work and risk on performance of construction contractors: An agent-based approach. Automation in Construction, 65, 9-20.
  • Zeng, J., An, M., & Smith, N. J. (2007). Application of a fuzzy based decision making methodology to construction project risk assessment. International Journal of Project Management, 25(6), 589-600.
  • Dikmen, I., Birgonul, M. T., & Han, S. (2007). Using fuzzy risk assessment to rate cost overrun risk in international construction projects. International Journal of Project Management, 25(5), 494-505.
  • Aydogan, G., & Koksal, A. (2013). An analysis of international construction risk factors on partner selection by applying ANP approach. ICCREM 2013: Construction and Operation in the Context of Sustainability (pp. 658-669).
  • Chemweno, P., Pintelon, L., Van Horenbeek, A., & Muchiri, P. (2015). Development of a risk assessment selection methodology for asset maintenance decision making: An analytic network process (ANP) approach. International Journal of Production Economics, 170, 663-676.
  • Fazli, S., Mavi, R. K., & Vosooghidizaji, M. (2015). Crude oil supply chain risk management with DEMATEL–ANP. Operational Research, 15(3), 453-480.
  • Wood, D. A. (2017). Gas and oil project time-cost-quality tradeoff: Integrated stochastic and fuzzy multi-objective optimization applying a memetic, nondominated, sorting algorithm. Journal of Natural Gas Science and Engineering.
  • Meidell, A., & Kaarbøe, K. (2017). How the enterprise risk management function influences decision-making in the organization–A field study of a large, global oil and gas company. The British Accounting Review, 49(1), 39-55.
  • Yang, J.-B., Wang, Y.-M., Xu, D.-L., & Chin, K.-S. (2006). The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties. European Journal of Operational Research, 171(1), 309-343.
  • Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3), 199-249.
  • Shafer, G. (1976). A mathematical theory of evidence (Vol. 1): Princeton University press
  • Yang, J.-B., & Sen, P. (1994). A general multi-level evaluation process for hybrid MADM with uncertainty. IEEE Transactions on Systems, Man, and Cybernetics, 24(10), 1458-1473.
  • Yang, J.-B., & Singh, M. G. (1994). An evidential reasoning approach for multiple-attribute decision making with uncertainty. IEEE Transactions on systems, Man, and Cybernetics, 24(1), 1-18.
  • Zhu, W.-d., Liu, F., Chen, Y.-w., Yang, J.-b., Xu, D.-l., & Wang, D.-p. (2015). Research project evaluation and selection: an evidential reasoning rule-based method for aggregating peer review information with reliabilities. Scientometrics, 105(3), 1469-1490.
  • Liu, H.-C., Liu, L., Bian, Q.-H., Lin, Q.-L., Dong, N., & Xu, P.-C. (2011). Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory. Expert Systems with Applications, 38(4), 4403-4415.
  • Ahmadzadeh, F., & Bengtsson, M. (2017). Using evidential reasoning approach for prioritization of maintenance-related waste caused by human factors—a case study. The International Journal of Advanced Manufacturing Technology, 90(9-12), 2761-2775.
  • Fu, C., & Yang, S. (2012). The combination of dependence-based interval-valued evidential reasoning approach with balanced scorecard for performance assessment. Expert Systems with Applications, 39(3), 3717-3730.
  • Liu, F., Zhu, W.-d., Chen, Y.-w., Xu, D.-l., & Yang, J.-b. (2017). Evaluation, ranking and selection of R&D projects by multiple experts: an evidential reasoning rule based approach. Scientometrics, 111(3), 1501-1519.
  • Monghasemi, S., Nikoo, M. R., Fasaee, M. A. K., & Adamowski, J. (2015). A novel multi criteria decision making model for optimizing time–cost–quality trade-off problems in construction projects. Expert Systems with Applications, 42(6), 3089-3104.
  • Ng, C. (2016). An evidential reasoning-based AHP approach for the selection of environmentally-friendly designs. Environmental Impact Assessment Review, 61, 1-7.
  • Yang, J.-B., & Xu, D.-L. (2002). On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 32(3), 289-304.