مهندسی سیستم و بهره‌وری

مهندسی سیستم و بهره‌وری

مدل تصمیم‌گیری فازی ترکیبی برای مدیریت ریسک و ارتقای تاب‌آوری زنجیره تأمین محصولات گوشتی و پروتئینی (یک مطالعه موردی)

نوع مقاله : پژوهشی

نویسندگان
گروه مهندسی صنایع، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
چکیده
تاب‌آوری زنجیره تأمین کشاورزی–غذایی به‌دلیل مواجهه با ریسک‌های متعدد و اختلالات پیش‌بینی‌نشده اهمیت ویژه‌ای دارد. پژوهش حاضر با هدف ارائه مدل تصمیم‌گیری فازی ترکیبی برای شناسایی و ارزیابی ریسک‌ها و اولویت‌بندی راهبردهای بهبود تاب‌آوری انجام شد. این پژوهش از نظر هدف کاربردی و از نظر ماهیت توصیفی–پیمایشی است. داده‌های پژوهش از دیدگاه پنج خبره و مدیر آشنا با بخش محصولات گوشتی و پروتئینی که به روش نمونه‌گیری گلوله‌برفی انتخاب شدند، گردآوری شد. ابتدا ریسک‌ها و راهبردهای تاب‌آوری از طریق مرور ادبیات استخراج و با روش دلفی فازی غربالگری شدند. سپس، برای وزن‌دهی ریسک‌ها از OPA-F و برای اولویت‌بندی راهبردها از FQFD استفاده شد. نتایج نشان داد ریسک‌های زیرساختی و لجستیکی با وزن 3849/0 مهم‌ترین بعد ریسک و نبود یا کمبود توسعه فناوری با وزن 2012/0 مهم‌ترین ریسک فرعی است. همچنین، سرمایه‌گذاری در زیرساخت‌ها، پذیرش و توسعه فناوری و استانداردسازی فرآیندها به ‌ترتیب مهم‌ترین راهبردهای بهبود تاب‌آوری شناخته شدند. یافته‌ها نشان می‌دهند مدل پیشنهادی می‌تواند مدیران را در شناسایی ریسک‌های بحرانی، تخصیص هدفمند منابع، اولویت‌بندی اقدامات اصلاحی و تقویت آمادگی، پاسخگویی و بازیابی زنجیره تأمین در برابر اختلالات احتمالی به‌طور مؤثر یاری کند.

تازه های تحقیق

  • توسعه مدل OPA-F-FQFD برای مدیریت ریسک و بهبود تاب‌آوری
  • ریسک‌های زیرساختی و لجستیکی مهم‌ترین بعد شناخته شدند.
  • سرمایه‌گذاری در زیرساخت‌ها مهم‌ترین راهبرد ارتقای تاب‌آوری زنجیره تأمین نتیجه شد.

کلیدواژه‌ها
موضوعات

عنوان مقاله English

A Hybrid Fuzzy Decision-Making Model for Risk Management and Resilience Enhancement in Meat and Protein-Product Supply Chains (A Case Study)

نویسندگان English

Mahnaz Zarei
Amir Imanfar
Department of Industrial Engineering, Shi.C., Islamic Azad University, Shiraz, Iran
چکیده English

Supply chain resilience in agri-food industries is of particular importance due to exposure to multiple risks and unforeseen disruptions. This study aims to develop a hybrid fuzzy decision-making model for identifying and assessing the importance of risks and formulating strategies to improve resilience in agri-food supply chains. The research is applied in terms of purpose and descriptive-survey in nature. The study data were collected from five experts and managers familiar with the meat and protein-products sector, who were selected through snowball sampling. First, influential risks and resilience strategies were extracted through a literature review and then evaluated and screened using the Fuzzy Delphi Method (FDM). Subsequently, the Fuzzy Ordinal Priority Approach (OPA-F) was employed to determine the importance and assign weights to risks, while the Fuzzy Quality Function Deployment (FQFD) approach was applied to identify appropriate resilience improvement strategies. The OPA-F results indicated that infrastructure and logistics risks, with a weight of 0.3849, constituted the most important risk dimension in the supply chain under study. Moreover, “insufficient technology development,” with a weight of 0.2012, was identified as the most critical sub-risk. The FQFD results revealed that "investment in infrastructure," "technology adoption and development," and "process standardization" are the most important strategies for improving resilience, respectively. The findings demonstrate that the application of hybrid fuzzy decision-making models can assist agri-food industry managers in identifying critical risks, prioritizing corrective actions, and enhancing supply chain resilience.

کلیدواژه‌ها English

Agri-food supply chain
Meat and protein products
Risk management
Resilience
Fuzzy Ordinal Priority Approach
Fuzzy Quality Function Deployment

Copyright © Mahnzaz Zarei, Amir Imanfar

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

Abolghasemian, M., Kheiri, A. O., & Saberifard, N. (2024). Prioritizing factors affecting the flexibility and performance of the digital supply chain system in the Iranian food industry. System Engineering and Productivity, 4(1), 41-57. https://doi.org/10.22034/msb.2024.2025240.1194
Afrasiabi, A., Chalmardi, M. K., & Balezentis, T. (2022). A novel hybrid evaluation framework for public organizations based on employees’ performance factors. Evaluation and Program Planning, 91, 102020. https://doi.org/10.1016/j.evalprogplan.2021.102020
Afrasiabi, A., Tavana, M., & Di Caprio, D. (2022). An extended hybrid fuzzy multi-criteria decision model for sustainable and resilient supplier selection. Environmental Science and Pollution Research, 29(25), 37291-37314. https://doi.org/10.1007/s11356-021-17851-2
Ali, I., Sadiddin, A., & Cattaneo, A. (2023). Risk and resilience in agri-food supply chain SMEs in the pandemic era: a cross-country study. International Journal of Logistics Research and Applications, 26(11), 1602-1620. https://doi.org/10.1080/13675567.2022.2102159
Ali, Z., Siddiqui, M. S., Khan, S., & Ali, R. (2025). A multi-method analysis of risk mitigation strategies for the livestock supply chain. Sustainability, 17(15), 6741. https://doi.org/10.3390/su17156741
Alqahtani, A. Y., & Makki, A. A. (2023). A DEMATEL-ISM integrated modeling approach of influencing factors shaping destination image in the tourism industry. Administrative Sciences, 13(9), 201. https://doi.org/10.3390/admsci13090201
Ataei, Y., Mahmoudi, A., Feylizadeh, M. R., & Li, D.-F. (2020). Ordinal priority approach (OPA) in multiple attribute decision-making. Applied soft computing, 86, 105893. https://doi.org/10.1016/j.asoc.2019.105893
Behzadi, G., O’Sullivan, M. J., Olsen, T. L., & Zhang, A. (2018). Agribusiness supply chain risk management: A review of quantitative decision models. Omega, 79, 21-42. https://doi.org/10.1016/j.omega.2017.07.005
Bevilacqua, M., Ciarapica, F. E., & Giacchetta, G. (2006). A fuzzy-QFD approach to supplier selection. Journal of Purchasing and Supply Management, 12(1), 14-27. https://doi.org/10.1016/j.pursup.2006.02.001
Bottani, E., & Rizzi, A. (2006). Strategic management of logistics service: A fuzzy QFD approach. International Journal of Production Economics, 103(2), 585-599. https://doi.org/10.1016/j.ijpe.2005.11.006
Darko, A. P., Zhang, B., Agbodah, K., Antwi, C. O., Asamoah, K. O., Ren, K., & Ren, J. (2025). Evaluating mental health apps under uncertainty: a decision-support framework integrating linguistic distributions, disappointment theory, and double normalization-based multi-aggregation. Humanities and Social Sciences Communications, 12(1), 1357. https://doi.org/10.1057/s41599-025-05625-x
Darmian, S. M., Afrasiabi, A., & Yazdani, M. (2023). Multi-criteria evaluation of agro-processing industries for sustainable local economic development in East of Iran. Expert Systems with Applications, 230, 120607. https://doi.org/10.1016/j.eswa.2023.120607
Davoudi, S., Stasinopoulos, P., & Shiwakoti, N. (2024). Two decades of advancements in cold supply chain logistics for reducing food waste: A review with focus on the meat industry. Sustainability, 16(16), 6986. https://doi.org/10.3390/su16166986
Dehghani Filabadi, A., Nahid Titkanlue, H., & Jamali, S. (2026). Identification and ranking of key factors in total quality management (TQM) adoption using a hybrid fuzzy Delphi-SWARA approach: A case study of Shiraz petrochemical company. System Engineering and Productivity, 6(2), 261-282. https://doi.org/10.22034/sep.2025.2071569.1394
Ebrahimi, S., & Bridgelall, R. (2021). A fuzzy Delphi analytic hierarchy model to rank factors influencing public transit mode choice: A case study. Research in Transportation Business & Management, 39, 100496. https://doi.org/10.1016/j.rtbm.2020.100496
Eshghali, M., & Jafarian, A. (2024). A causal model to analyze the interactions between farmer and factory in the agri-food value chain. Strategic Value Chain Management, 1(1), 72-95. https://doi.org/10.22075/svcm.2025.35017.1002
Eskandari, M., Darabi, M., & Asghari, H. (2025). Identification and assessment of risks in the sales barriers to reduce food supply chain crises. Supply Chain Management, 27(86), 19-29. https://dor.isc.ac/dor/20.1001.1.20089198.1404.27.86.3.5
Focker, M., van Wagenberg, C., van Asselt, E., & van der Fels-Klerx, H. J. (2024). The resilience of the pork supply chain to a food safety outbreak: The case of dioxins. Risk Analysis, 44(4), 785-801. https://doi.org/10.1111/risa.14205
Freund, A., Jámbor, Z., & Nagy, J. (2026). A capability hierarchy for building resilience in multi-actor agri-food supply chains: a digitalisation perspective. Journal of Manufacturing Technology Management, 37(9), 19-39. https://doi.org/10.1108/JMTM-12-2024-0718
Hobbs, J. E. (2021). The Covid-19 pandemic and meat supply chains. Meat Sci, 181, 108459. https://doi.org/10.1016/j.meatsci.2021.108459
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285-307. https://doi.org/10.1016/j.tre.2019.03.001
Ibrahim, M. F., Santoso, I., Mustaniroh, S. A., Astuti, R., & Rau, H. (2026). Prioritizing proactive risk mitigation strategies in agri-food supply chains: an integrated fuzzy decision-support framework. Journal of Industrial and Production Engineering, 1-27. https://doi.org/10.1080/21681015.2026.2653968
Ikasari, D. M., Suef, M., & Vanany, I. (2025). A bibliometric analysis of risk management and sustainability in the agri-food supply chain: future directions. Engineering Proceedings, 84(1), 13. https://doi.org/10.3390/engproc2025084013
Joshi, S., Sharma, M., Ekren, B. Y., Kazancoglu, Y., Luthra, S., & Prasad, M. (2023). Assessing supply chain innovations for building resilient food supply chains: An emerging economy perspective. Sustainability, 15(6), 4924. https://doi.org/10.3390/su15064924
Karimi, A., Hassanpoor, H.-a., & Mosadegh Khah, M. (2024). Providing a model for analysis of disorders and resilience of the food supply chain. Journal of Improvement Management, 18(2), 48-73. https://doi.org/10.22034/jmi.2024.449228.3074
Kazancoglu, Y., Sezer, M. D., Ozbiltekin-Pala, M., Lafçı, Ç., & Sarma, P. R. S. (2024). Evaluating resilience in food supply chains during COVID-19. International Journal of Logistics Research and Applications, 27(5), 688-704. https://doi.org/10.1080/13675567.2021.2003762
Keramydas, C., Papapanagiotou, K., Vlachos, D., & Iakovou, E. (2015). Risk management for agri-food supply chains. In Supply Chain Management for Sustainable Food Networks (pp. 255-292). https://doi.org/10.1002/9781118937495.ch10
Khalilzadeh, M., Ghasemi, P., Afrasiabi, A., & Shakeri, H. (2021). Hybrid fuzzy MCDM and FMEA integrating with linear programming approach for the health and safety executive risks: a case study. Journal of Modelling in Management, 16(4), 1025-1053. https://doi.org/10.1108/jm2-12-2019-0285
Krstić, M., Elia, V., Agnusdei, G. P., De Leo, F., Tadić, S., & Miglietta, P. P. (2023). Evaluation of the agri-food supply chain risks: the circular economy context. British Food Journal, 126(1), 113-133. https://doi.org/10.1108/BFJ-12-2022-1116
Kumar, A., Mangla, S. K., Kumar, P., & Song, M. (2021). Mitigate risks in perishable food supply chains: Learning from COVID-19. Technological Forecasting and Social Change, 166, 120643. https://doi.org/10.1016/j.techfore.2021.120643
Kumar, P., & Kumar Singh, R. (2022). Strategic framework for developing resilience in agri-food supply chains during COVID 19 pandemic. International Journal of Logistics Research and Applications, 25(11), 1401-1424. https://doi.org/10.1080/13675567.2021.1908524
Mahmoudi, A., Javed, S. A., & Mardani, A. (2022). Gresilient supplier selection through fuzzy ordinal priority approach: decision-making in post-COVID era. Operations Management Research, 15(1), 208-232. https://doi.org/10.1007/s12063-021-00178-z
Mahmoudi, A., Sadeghi, M., & Deng, X. (2025). Performance measurement of construction suppliers under localization, agility, and digitalization criteria: fuzzy ordinal priority approach. Environment, Development and Sustainability, 27(9), 21961-21986. https://doi.org/10.1007/s10668-022-02301-x
Mohammadpour, M., Afrasiabi, A., & Yazdani, M. (2024). Identifying and prioritizing the barriers to TQM implementation in food industries using group best-worst method (a real-world case study). International Journal of Productivity and Performance Management, 73(10), 3335-3362. https://doi.org/10.1108/IJPPM-11-2023-0602
Mohseni, E., & Mohammadi Zanjirani, D. (2024). Integration and development of fuzzy QFD for evaluation and selection of biofuel development strategies. Journal of Industrial Management Perspective, 14(3), 189-211. https://doi.org/10.48308/jimp.14.3.189
Motevalli, S. H., Nazarizadeh, F., & Mir Shahvelayati, F. (2024). Identifying and evaluating strategic options for advancing the resilience of the Kaleh dairy company's supply chain. System Engineering and Productivity, 3(4), 106-135. https://doi.org/10.22034/msb.2024.2021449.1176
Mousavi Ramezanzadeh, A., & Nazari, M. (2025). Identifying and prioritizing effective nudges for social acceptance of green electricity: A fuzzy Delphi study. System Engineering and Productivity, 5(4), 149-167. https://doi.org/10.22034/sep.2025.2065794.1352
Nguyen, P.-H. (2022). Agricultural supply chain risks evaluation with spherical fuzzy analytic Hierarchy process. Computers, Materials \& Continua, 73(2). https://doi.org/10.32604/cmc.2022.030115
Pishdar, M., & Habibi, A. (2023). Identifying and prioritizing factors affecting transportation risk management in the food supply chain using gray Delphi and gray COPRAS. Industrial Management Studies, 21(71), 263-295. https://doi.org/10.22054/jims.2023.73344.2858
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
Roy, R., Islam, K., Rahman, M., Ahmed, T., Muhammad, S., & Ghosh, S. K. (2024). Assessment of barriers towards a sustainable and resilient poultry industry supply chain: A developing country viewpoint after COVID-19. Cleaner Logistics and Supply Chain, 13, 100184. https://doi.org/10.1016/j.clscn.2024.100184
Saaty, T. L. (2004). Fundamentals of the analytic network process—Dependence and feedback in decision-making with a single network. Journal of Systems science and Systems engineering, 13(2), 129-157. https://doi.org/10.1007/s11518-006-0158-y
Sahoo, S. K., & Goswami, S. S. (2023). A comprehensive review of multiple criteria decision-making (MCDM) methods: advancements, applications, and future directions. Decision Making Advances, 1(1), 25-48. https://doi.org/10.31181/dma1120237
Sharma, J., & Tripathy, B. B. (2023). An integrated QFD and fuzzy TOPSIS approach for supplier evaluation and selection. The TQM Journal, 35(8), 2387-2412. https://doi.org/10.1108/TQM-09-2022-0295
Shen, F., & Jiang, F. (2026). The resilience of agricultural product supply chain: An empirical analysis based on spatial spillover and threshold effects. Sustainability, 18(4), 1975. https://doi.org/10.3390/su18041975
Singh, R., & Dwivedi, G. (2025). Resilience in agri-food supply chains: a framework for risk assessment and strategy development. International Journal of Logistics Research and Applications, 28(12), 1659-1690. https://doi.org/10.1080/13675567.2024.2389050
Whitehead, D., & Brad Kim, Y. H. (2022). The impact of COVID 19 on the meat supply chain in the USA: A review. Korean Journal for Food Science of Animal Resources, 42(5), 762-774. https://doi.org/10.5851/kosfa.2022.e39
Zarei, M., & Keshavarzi, M. (2026). Analyzing barriers to the development of sustainable predictive maintenance in industrial equipment of petrochemical complexes using FDM-DEMATEL (A Case Study). System Engineering and Productivity, 6(4), 251-279. https://doi.org/10.22034/sep.2026.2090483.1504
Zarei, M., Fakhrzad, M. B., & Paghaleh, M. J. (2011). Food supply chain leanness using a developed QFD model. Journal of food engineering, 102(1), 25-33. https://doi.org/10.1016/j.jfoodeng.2010.07.026
Zhang, M., & Yang, J. (2025). Agri-food supply chain resilience: An exploration of influencing factors based on fuzzy-DEMATEL-ISM analysis. PLOS ONE, 20(12), e0338492. https://doi.org/10.1371/journal.pone.0338492
Zhao, G., Olan, F., Liu, S., Hormazabal, J. H., Lopez, C., Zubairu, N., Zhang, J., & Chen, X. (2024). Links between risk source identification and resilience capability building in agri-food supply chains: A comprehensive analysis. IEEE Transactions on Engineering Management, 71, 13362-13379. https://doi.org/10.1109/TEM.2022.3221361

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انتشار آنلاین از 08 مهر 1405

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