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  • ارزیابی ریسک های زنجیره تأمین پایدار با روش تحلیل حالات و دلایل شکست در محیط فازی (مطالعه موردی: صبا باطری)

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آدرس مقاله


کد مقاله : IMJ-1903-1205 (R2) بازدید : 124 صفحه: 97 - 112

نوع مقاله: پژوهشی

ارزیابی ریسک های زنجیره تأمین پایدار با روش تحلیل حالات و دلایل شکست در محیط فازی (مطالعه موردی: صبا باطری)

محورهای موضوعی : مدیریت صنعتی

Mojtaba Farrokh 1 , mohsen zabihi jamkhaneh 2 , mehdi sholeh 3

1 - Assistant Professor, Department of Operation Management, Kharazmi University, Tehran, Iran
2 - University of Tehran- farabi Branch
3 - Candidate of PhD in Management- University of Tehran- Faraby Branch

تاریخ دریافت : 1397/12/13 تاریخ پذیرش : 1398/06/27 تاریخ انتشار : 1398/08/08

کلید واژه: روابط ترجیح فازی, فاکتورهای ریسک, زنجیره تأمین پایدار, اعداد اولویت ریسک فازی,

چکیده مقاله :

با وجود مزایای متعدد جهانی شدن و ورود تکنولوژی‌های پیشرفته، این عوامل زنجیره تامین پایدار را در معرض ریسک‌های اجتماعی، اقتصادی و زیست محیطی قرار می‌دهد. هدف پژوهش حاضر، توسعه یک رویکرد جدید برای شناسایی و اولویت‌بندی ریسک‌های موجود در زنجیره تأمین پایدار با استفاده از روش تحلیل حالت و دلایل شکست (FMEA) است. برای تحقق این هدف، ابتدا ریسک‌های موجود در زنجیره های پایدار صنعت باطری سازی شناسایی شده، سپس با نظر تیم‌های بین وظیفه ای و با بهره گیری از روش پیشنهادی این ریسک‌ها اولویت بندی می شوند. با توجه به اینکه فاکتورهای ریسک شامل احتمال، شدت و قابلیت شناسایی به صورت متغیرهای فازی بوده، در این پژوهش از اعداد اولویت ریسک فازی (FRPN) برای بررسی حالات شکست استفاده می‌شود. اعداد اولویت ریسک فازی به صورت میانگین هندسی موزون فازی فاکتورهای ریسک تعریف و با کمک مجموعه‌های سطوح برش آلفا و مدل برنامه ریزی خطی محاسبه می شود. سپس با توجه به ناکارایی رویکردهای فازی اولویت-بندی عوامل ریسک پایداری، در رویکرد پیشنهادی از روابط ترجیح فازی برای این منظور استفاده می شود. نتایج نشان دهنده آن است که رویکرد پیشنهادی قادر است با کارایی محاسباتی بالایی نتایج مشابهی را ارائه دهد.

چکیده انگلیسی:

Despite the numerous benefits of globalization and the emergence of advanced technologies, they have put the sustainable supply chain in subject to the social, economic, environmental risks. The aim of this study is to develop a new approach to identify and prioritize the risks involved in sustainable supply chain by using the failure mode and effect analysis (FMEA) technique. In this way, the fuzzy sets theory is applied to calculate the risk priority numbers with regard to the fuzzy importance coefficients of risk factors including probability of occurrence, severity and detectability for each risk factor. However, proposed approaches have a computational inefficiency in ranking the fuzzy priority numbers. The fuzzy weighted geometric mean and linear programming model is used in a different way to determine the fuzzy risk priority numbers and then the fuzzy preference relations is applied to compare these numbers for prioritizing sustainability risk factors. Risk factors of the sustainable supply chain in the battery industry are identified and then prioritized by the cross-functional team by using the proposed method. The results show that the approach is capable to provide similar results than other ones with a high computational performance.

منابع و مأخذ:


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_||_

  1. Anderson, D. R. (2006). The critical importance of sustainability risk management. Risk Management, 53(4), 66.
    2.
  2. Anderson, D. R., & Anderson, K. E. (2009). Sustainability risk management. Risk Management and Insurance Review, 12(1), 25-38.
    3.
  3. Afgan, N. H., & Carvalho, M. G. (2004). Sustainability assessment of hydrogen energy systems. International Journal of Hydrogen Energy, 29(13), 1327-1342.
    4.
  4. Bonissone, P. P. (1980). A fuzzy sets based linguistic approach: theory and applications. In Proceedings of the 12th conference on Winter simulation. IEEE Press.
    5.
  5. Braglia, M., Frosolini, M., & Montanari, R. (2003). Fuzzy TOPSIS approach for failure mode, effects and criticality analysis. Quality and reliability engineering international, 19(5), 425-443.
    6.
  6. Ben-Daya, M., & Raouf, A. (1996). A revised failure mode and effects analysis model. International Journal of Quality & Reliability Management, 13(1), 43-47.
    7.
  7. Clift, R. (2003). Metrics for supply chain sustainability. Clean Technologies and Environmental Policy, 5(3-4), 240-247.
    8.
  8. Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: moving toward new theory. International journal of physical distribution & logistics management, 38(5), 360-387.
    9.
  9. Cousins, P. D., Lamming, R. C., & Bowen, F. (2004). The role of risk in environment-related supplier initiatives. International Journal of Operations & Production Management, 24(6), 554-565.
    10.
  10. Chang, C. L., Wei, C. C., & Lee, Y. H. (1999). Failure mode and effects analysis using fuzzy method and grey theory. Kybernetes, 28(9), 1072-1080.
    11.
  11. Chen, L. H., & Ko, W. C. (2009). Fuzzy approaches to quality function deployment for new product design. Fuzzy sets and systems, 160(18), 2620-2639.
    12.
  12. Chopra, S., & Sodhi, M. S. (2004). Managing risk to avoid supply-chain breakdown. MIT Sloan management review, 46(1), 53.
    13.
  13. Dubois, D., & Prade, H. (1980). Systems of linear fuzzy constraints. Fuzzy sets and Systems, 3(1), 37-48.
    14.
  14. Dües, C. M., Tan, K. H., & Lim, M. (2013). Green as the new Lean: how to use Lean practices as a catalyst to greening your supply chain. Journal of cleaner production, 40, 93-100.
    15.
  15. Giannakis, M., & Papadopoulos, T. (2016). Supply chain sustainability: A risk management approach. International Journal of Production Economics, 171, 455-470.
    16.
  16. Godfrey, P. C., Merrill, C. B., & Hansen, J. M. (2009). The relationship between corporate social responsibility and shareholder value: An empirical test of the risk management hypothesis. Strategic management journal, 30(4), 425-445.
    17.
  17. Gurnani, H., Mehrotra, A., & Ray, S. (2012). Supply chain disruptions: Theory and practice of managing risk. London: Springer.
    18.
  18. Hallikas, J., Virolainen, V. M., &Tuominen, M. (2002). Risk analysis and assessment in network environments: A dyadic case study. International journal of production economics, 78(1), 45-55.
    19.
  19. Hofmann, H., Busse, C., Bode, C., & Henke, M. (2014). Sustainability‐related supply chain risks: conceptualization and management. Business Strategy and the Environment, 23(3), 160-172.
    20.
  20. Halldórsson, Á., Kotzab, H., & Skjøtt-Larsen, T. (2009). Supply chain management on the crossroad to sustainability: a blessing or a curse? Logistics Research, 1(2), 83-94.
    21.
  21. Krysiak, F. C. (2009). Risk management as a tool for sustainability. Journal of business ethics, 85, 483-492.
    22.
  22. Liu, H. C., Liu, L., Liu, N., & Mao, L. X. (2012). Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Systems with Applications, 39(17), 12926-12934.
    23.
  23. Jiang, W., Xie, C., Zhuang, M., & Tang, Y. (2017). Failure mode and effects analysis based on a novel fuzzy evidential method. Applied Soft Computing, 57, 672-683.
    24.
  24. Maloni, M. J., & Brown, M. E. (2006). Corporate social responsibility in the supply chain: an application in the food industry. Journal of business ethics, 68(1), 35-52.
    25.
  25. Mandal, S., &Maiti, J. (2014). Risk analysis using FMEA: Fuzzy similarity value and possibility theory based approach. Expert Systems with Applications, 41(7), 3527-3537.
    26.
  26. Narasimhan, R., &Talluri, S. (2009). Perspectives on risk management in supply chains.
    27.
  27. Olson, D. L., & Dash Wu, D. (2010). A review of enterprise risk management in supply chain. Kybernetes, 39(5), 694-706.
    28.
  28. Porter, M. E., & Kramer, M. R. (2007). The Link Between Competitive Advantage and Corporate Social Responsibility. Harvard business review.
    29.
  29. Pullman, M. E., Maloni, M. J., & Carter, C. R. (2009). Food for thought: social versus environmental sustainability practices and performance outcomes. Journal of Supply Chain Management, 45(4), 38-54.
    30.
  30. Roberts, S. (2003). Supply chain specific? Understanding the patchy success of ethical sourcing initiatives. Journal of business ethics, 44(2), 159-170.
    31.
  31. Safari, H., Faraji, Z., &Majidian, S. (2016). Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR. Journal of Intelligent Manufacturing, 27(2), 475-486.
    32.
  32. Sodhi, M. S., & Tang, C. S. (2009). Managing supply chain disruptions via time-based risk management. In Managing supply chain risk and vulnerability. Springer, London.
    33.
  33. Song, W., Ming, X., & Liu, H. C. (2017). Identifying critical risk factors of sustainable supply chain management: A rough strength-relation analysis method. Journal of Cleaner Production, 143, 100-115.
    34.
  34. Spicher, P., von Clausbruch, J. C., & von Waldenfels, P. (2009). Sustainability in Finance–Banking on the Planet. Sustainable Solutions for Modern Economies, (4), 12.
    35.
  35. Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451-488.
    36.
  36. Tang, O., & Musa, S. N. (2011). Identifying risk issues and research advancements in supply chain risk management. International journal of production economics, 133(1), 25-34.
    37.
  37. Thun, J. H., &Hoenig, D. (2011). An empirical analysis of supply chain risk management in the German automotive industry. International Journal of Production Economics, 131(1), 242-249.
    38.
  38. Tummala, R., & Schoenherr, T. (2011). Assessing and managing risks using the supply chain risk management process (SCRMP). Supply Chain Management: An International Journal, 16(6), 474-483.
    39.
  39. Trkman, P., & McCormack, K. (2009). Supply chain risk in turbulent environments—A conceptual model for managing supply chain network risk. International Journal of Production Economics, 119(2), 247-258.
    40.
  40. Tuncel, G., & Alpan, G. (2010). Risk assessment and management for supply chain networks: A case study. Computers in industry, 61(3), 250-259.
    41.
  41. Vanany, I., Zailani, S., &Pujawan, N. (2009). Supply chain risk management: literature review and future research. IGI Global, 16-33.
    42.
  42. Vilko, J. P., &Hallikas, J. M. (2012). Risk assessment in multimodal supply chains. International Journal of Production Economics, 140(2), 586-595.
    43.
  43. Wang, Z., &Sarkis, J. (2013). Investigating the relationship of sustainable supply chain management with corporate financial performance. International Journal of Productivity and Performance Management, 62(8), 871-888.
    44.
  44. Wang, Y. M., Chin, K. S., Poon, G. K. K., & Yang, J. B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert systems with applications, 36(2), 1195-1207.
    45.
  45. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
    46.
  46. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 1(1), 3-28.
    47.
  47. Zhang, Z., & Chu, X. (2011). Risk prioritization in failure mode and effects analysis under uncertainty. Expert Systems with Applications, 38(1), 206-214.
    48.

 

 

 

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