نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مدیریت صنعتی، دانشکده اقتصاد، مدیریت و علوم اداری، دانشگاه سمنان، سمنان، ایران.

چکیده

هدف: پژوهش حاضر به دنبال رتبه­‌بندی ریسک‌های زنجیره تامین با استفاده از رویکرد ترکیبی بهینه‌سازی روش تجزیه‌وتحلیل عوامل شکست و آثار آن و تئوری خاکستری در واحدهای صنایع غذایی مشهد است.
روش‌شناسی پژوهش: این پژوهش به جهت ماهیت، در دسته پژوهش­‌های توصیفی _تحلیلی، با متغیرهای کیفی و از منظر هدف، در زمره­ی پژوهش­‌های کاربردی است. در این پژوهش، ابتدا ریسک‌­های موجود در زنجیره تامین صنایع غذایی شناسایی شدند؛ بدین گونه که ابتدا ریسک‌­های موجود در زنجیره تامین از طریق مطالعات کتابخانه‌­ای و ادبیات پژوهش شناسایی و سپس، توسط پرسش‌نامه دلفی فازی در اختیار خبرگان قرار گرفت تا با طیف لیکرت مورد امتیازبندی قرار گیرند. به دلیل ﻣﺤﺪودﯾﺖ زﻣﺎﻧﯽ و نقص در تجزیه‌وتحلیل و همچنین داده­‌های اﻃﻼﻋﺎﺗﯽ ﻣﺒﻬﻢ، ﻧﺎﻗﺺ و ﻧﺎﻣﻄﻤﺌﻦ، این امتیازها به بازه­ی خاکستری تبدیل شدند. سپس، توسط روش تجزیه‌وتحلیل عوامل شکست و آثار آن امتیاز اولویت خطرپذیری به‌منظور بررسی حالات بالقوه شکست محاسبه شد؛ به‌گونه‌­ای که شاخص‌­ها و ابعاد ریسک­‌های زنجیره تامین با امتیاز اولویت خطرپذیری رتبه‌بندی شد. ریسک­‌های با امتیاز اولویت خطرپذیری بیشتر، از ریسک‌­پذیری بالاتری برخوردار و لزوم توجه بیشتری را نیازمند است.
یافته‌ها: نتیجه­­ امتیازبندی و محاسبات مشخص کرد که بعد اقتصادی بیشترین ریسک را در زنجیره تامین داراست. پس از بعد اقتصادی، به ترتیب، ابعاد قانونی، استراتژیک، فردی، سیاسی و طبیعی ابعاد دوم تا ششم و بعد فرهنگی و اجتماعی در جایگاه هفتم و بعد اطلاعاتی رتبه‌­ی هشتم را به خود اختصاص دادند.
اصالت/ارزش افزوده علمی: یافته­‌های این پژوهش به مدیران کمک می­‌کند با توجه به محدود بودن منابع، جهت کنترل و مدیریت، خصوصا در شرایط عدم قطعیت، با اولویت­‌بندی ریسک­‌های زنجیره تامین خود با توجه به میزان ریسک‌پذیری هریک، همچنین در نظر گرفتن اقدامات پیشگیرانه در خصوص این ریسک‌­ها، از صدمات جبران‌ناپذیر و بحرانی احتمالی پیشگیری نمایند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Rating Supply Chain Risks Using the Combined Approach of FMEA Optimization and Gray Theory (Case Study: Mashhad Food Industry Units)

نویسندگان [English]

  • Mohsen Shafiei Nikabadi
  • Leila Helalian

Department of Industrial Management, Faculty of Economics, Management and Administrative Sciences, Semnan University, Semnan, Iran.

چکیده [English]

Purpose: The ranking of supply chain risks using a combined approach is to optimize the method of analyzing failure factors and their effects and gray theory in Mashhad food industry units.
Methodology: Due to its nature, the present research belongs to the category of descriptive-analytical researches, with qualitative variables, and from the point of view of the objective, it belongs to the category of applied researches. In this research, the risks in the supply chain of the food industry were first identified. In such a way that; first, the risks in the supply chain were identified through library studies and research literature, and then they were given to the experts using a fuzzy Delphi questionnaire to be rated by the Likert scale. Due to the time limitation and defects in the analysis as well as vague, incomplete and uncertain information data, these points became a gray area. Then, using the method of analysis of failure factors and its effects, the risk priority score number was calculated in order to investigate potential failure situations. In this way, the indicators and dimensions of supply chain risks were ranked with the number of the priority score of risk taking. Risks with a higher risk priority score have a higher risk tolerance and require more attention.
Findings: The result of scoring and calculations determined that the economic dimension has the highest risk in the supply chain. After the economic dimension, the legal, strategic, individual, political and natural dimensions are the second to the sixth, and the cultural and social dimensions are the seventh and the information dimension is the eighth.
Originality/Value: The findings of this research will help managers, considering the limited resources, for control and management, especially in conditions of uncertainty, by prioritizing the risks of their supply chain. According to the level of risk-taking of each, as well as considering preventive measures regarding these risks, to prevent possible irreparable and critical injuries.

کلیدواژه‌ها [English]

  • Supply chain risks
  • Analysis of potential errors
  • Gray theory
  1. Ghasempoor Anaraki, M., Vladislav, D. S., Karbasian, M., Osintsev, N., & Nozick, V. (2021). Evaluation and selection of supplier in supply chain with fuzzy analytical network process approach. Journal of fuzzy extension and applications2(1), 69-88. http://www.journal-fea.com/article_126927.html
  2. Sorourkhah, A., Azar, A., Babaie-Kafaki, S., & Shafiei Nik Abadi, M. (2017). Using weighted-robustness analysis in strategy selection (case study: Saipa automotive research and innovation center). Industrial management journal9(4), 665-690.
  3. Farbod, E., Hamidieh, A., & Amininia, H. (In Press). Investigating the impact of supply chain dynamics on financial performance with supply chain disruption structure approach. Innovation management and operational strategies. (In Persian). DOI: 22105/imos.2022.345942.1235
  4. Bogataj, D., & Bogataj, M. (2007). Measuring the supply chain risk and vulnerability in frequency space. International journal of production economics108(1-2), 291-301.
  5. Saberhoseini, S. F., Edalatpanah, S. A., & Sorourkhah, A. (2022). Choosing the best private-sector partner according to the risk factors in neutrosophic environment. Big data and computing visions2(2), 61-68. https://www.bidacv.com/article_150242.html
  6. Sorourkhah, A. (2022). Coping uncertainty in the supplier selection problem using a scenario-based approach and distance measure on type-2 intuitionistic fuzzy sets. Fuzzy optimization and modeling journal3(1), 64-71. https://doi.org/10.30495/fomj.2022.1953705.1066
  7. Mohammadi, A., Mosleh Shirazi, A., Ahmadi, M. B., & Shojaei, P. (2014). Interpretive structural modeling for project supply chain risks in state gas company. Journal of industrial management perspective3(4), 9-37. (In Persian). https://jimp.sbu.ac.ir/article_87297.html?lang=en
  8. Tang, C. S. (2006). Perspectives in supply chain risk management. International journal of production economics103(2), 451-488. https://doi.org/10.1016/j.ijpe.2005.12.006
  9. Mazaheri, A., Karbasian, M., & Shiviezad, H. (1390). Identifying and prioritizing the supply chain in production organizations using the hierarchical analysis process. Supply chain management quarterly, 34, 28-37. (In Persian). https://www.magiran.com/paper/1018509
  10. Karimi, T., Bandesi, S. (2021). Service Supply chain risk assessment applying rough set theory approach: case of payment service providers. Management research in Iran, 22(1), 69-94. (In Persian). https://mri.modares.ac.ir/article_447.html?lang=en
  11. Ziegenbein, A., & Nienhaus, J. (2004). Coping with supply chain risks on strategic, tactical and operational level. In Global project and manufacturing management: the symposium proceedings May (pp. 163-177). MIP. https://www.research-collection.ethz.ch/handle/20.500.11850/84090
  12. Hosseinzadeh, M., Mehregan, M. R., & Ghomi, M. (2019). Identifying and analyzing supply chain risks of saipa automobile company using the coso model and social network analysis (SNA). Production and operations management10(1), 111-132. https://jpom.ui.ac.ir/article_23625_en.html?lang=fa
  13. Garvey, M. D., & Carnovale, S. (2020). The rippled newsvendor: a new inventory framework for modeling supply chain risk severity in the presence of risk propagation. International journal of production economics228, 107752. https://doi.org/10.1016/j.ijpe.2020.107752
  14. Diaz, R., Smith, K., Acero, B., Longo, F., & Padovano, A. (2021). Developing an artificial intelligence framework to assess shipbuilding and repair sub-tier supply chains risk. Procedia computer science180, 996-1002. https://doi.org/10.1016/j.procs.2021.01.363
  15. Gomes Filho, N., Rego, N., & Claro, J. (2021). Supply chain flows and stocks as entry points for cyber-risks. Procedia computer science181, 261-268. https://doi.org/10.1016/j.procs.2021.01.145
  16. Saffari, H., Abbasi, M., & Gheidar-Kheljani, J. (In Press). A sustaiable, reliable model for iron and steel closed-loop network design by considering risk, lateral transmission and solving by developing a multi objective planning method. Journal of decisions and operations research. DOI: 22105/dmor.2022.331970.1583
  17. Habibi, A. (2017). Gray analysis and gray theory. Pars modir marketing quarterly, 4(12). 24-41. (In Persian). http://parsmodir.ir/pmq-971204/
  18. Tzeng, G. H., & Tasur, S. H. (1994). The multiple criteria evaluations of grey relation model. The journal of grey system6(2), 87-108.
  19. Voskoglou, M., & Broumi, S. (2022). A hybrid method for the assessment of analogical reasoning skills. Journal of fuzzy extension and applications3(2), 152-157. http://www.journal-fea.com/article_149915.html
  20. Liu, H. C., You, J. X., Fan, X. J., & Lin, Q. L. (2014). Failure mode and effects analysis using D numbers and grey relational projection method. Expert systems with applications41(10), 4670-4679.
  21. Halvani, GH., & Zare, M. (2011). Safety system engineering and risk management. Asare Sobhan Publication. (In Persian). https://www.gisoom.com/book/
  22. Ritchie, B., & Brindley, C. (2007). Supply chain risk management and performance: a guiding framework for future development. International journal of operations & production management, 27(3), 303-322. https://doi.org/10.1108/01443570710725563
  23. Hendricks, K. B., & Singhal, V. R. (2011). Supply chain disruptions and corporate performance. In Supply chain disruptions: theory and practice of managing risk (pp. 1-19). London: Springer London.
  24. Wu, T., Blackhurst, J., & Chidambaram, V. (2006). A model for inbound supply risk analysis. Computers in industry57(4), 350-365. https://doi.org/10.1016/j.compind.2005.11.001
  25. Kleindorfer, P. R., & Saad, G. H. (2005). Managing disruption risks in supply chains. Production and operations management14(1), 53-68.
  26. Christopher, M., Mena, C., Khan, O., & Yurt, O. (2011). Approaches to managing global sourcing risk. Supply chain management: an international journal16(2), 67-81. https://doi.org/10.1108/13598541111115338
  27. Gomes Filho, N., Rego, N., & Claro, J. (2021). Supply chain flows and stocks as entry points for cyber-risks. Procedia computer science181, 261-268. https://doi.org/10.1016/j.procs.2021.01.145
  28. Chopra, s., & Sodhi, M.S. (2004). Managing risk to avoid supply-chain breakdown. MIT sloan management review, 46(1), 53-61. https://sloanreview.mit.edu/article/managing-risk-to-avoid-supplychain-breakdown/
  29. Ziegenbein, A., & Nienhaus, J. (2004). Coping with supply chain risks on strategic, tactical and operational level. In Global project and manufacturing management: the symposium proceedings May 2004(pp. 163-177). MIP. https://www.research-collection.ethz.ch/handle/20.500.11850/84090
  30. Kleindorfer, P. R., & Van Wassenhove, L. N. (2004). Managing risk in global supply chains (10012014UPSC000015CAP). https://www.researchgate.net/publication/283769459_Managing_risk_in_global_supply_chains
  31. Manuj, I., & Mentzer, J. T. (2008). Global supply chain risk management strategies. International journal of physical distribution & logistics management, 38, 192-223.
  32. Zsidisin, G., Ellarm, L., Carter, J., & Cavinato, J. (2004). An analysis of supply risk assessment techniques. International journal physics distribution logistic management, 34, 397-413.
  33. Qureshi, M. N., Kumar, D., & Kumar, P. (2007). Modeling the logistics outsourcing relationship variables to enhance shippers' productivity and competitiveness in logistical supply chain. International journal of productivity and performance management56(8), 689-714. https://doi.org/10.1108/17410400710833001
  34. Deane, J. K., Craighead, C. W., & Ragsdale, C. T. (2009). Mitigating environmental and density risk in global sourcing. International journal of physical distribution & logistics management39(10), 861-883. https://doi.org/10.1108/09600030911011450
  35. Lee, C. K. M., Ching Yeung, Y., & Hong, Z. (2012). An integrated framework for outsourcing risk management. Industrial management & data systems112(4), 541-558. https://doi.org/10.1108/02635571211225477
  36. Barnes, P., & Oloruntoba, R. (2005). Assurance of security in maritime supply chains: conceptual issues of vulnerability and crisis management. Journal of international management, 11(4), 519-540.
  37. Elzarka, S. (2013). Supply chain risk management: the lessons learned from the Egyptian revolution. International journal of logistics research and applications, 16(6), 482-492.
  38. Sodhi, M., Son, B., & Tang, C. (2012). Perspective on supply chain risk management. International journal of production and operations management, 21, 1-13.
  39. Paulsson, U. (2004). Supply chain risk management. Ashgate Publishing Limited. https://portal.research.lu.se/en/publications/supply-chain-risk-management-2
  40. Deleris, L., & Erhun, F. (2007). Risk management in a supply network: a case study based on engineering risk analysis concepts. Handbook of production planning. Kluwer Academic Publishers.
  41. Cucchiella, F., & Gastaldi, M. (2006). Risk management in supply chain: a real option approach. Journal of manufacturing technology management, 17, 700-720.
  42. Guertler, B., & Spinler, S. (2015). Supply risk interrelationships and the derivation of key supply risk indicators. Technological forecasting and social change, 92, 224-236.
  43. Wang, X., Chan, H. K., Yee, R. W., & Diaz-Rainey, I. (2012). A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain. International journal of production economics, 135(2), 595-606.
  44. Christopher, M. (2004). Creating resilient supply chains. Logistics Europe, 2, 1-21.
  45. Amirghodsi, S., Naeini, A. B., & Makui, A. (2020). An integrated Delphi-DEMATEL-ELECTRE method on gray numbers to rank technology providers. IEEE transactions on engineering management, 69(4), 1348-1364.
  46. Jüttner, U., Peck, H., & Christopher, M. (2003). Supply chain risk management: outlining an agenda for future research. International journal of logistics: research and applications, 6(4), 197-210.