ارائه مدل عوامل کلیدی موفقیت برای مقابله با اثر موجی در زنجیره تأمین فرش ماشینی ایران: نگاهی بر همه‌گیری کرونا

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشگاه کاشان.

2 استادیار، دانشگاه شاهد.

3 دانشیار، دانشگاه ولی ‌عصر رفسنجان.

چکیده

زنجیره‌های تأمین برای بقا باید بتوانند با اختلالات مقابله کنند. همه‌گیری کرونا یکی از مهم‌ترین اختلالاتی بود که توانست به اثر موجی در زنجیره تأمین منجر شود. اثر موجی به اختلالی اشاره دارد که اثر آن به‌صورت دومینویی سایر قسمت‌های زنجیره تأمین را تحت تأثیر قرار می‌دهد و احتمال وقوع آن کم، اما اثرگذاری آن بالا است. این پژوهش به دنبال شناسایی عوامل کلیدی موفقیت برای مقابله با اثر موجی در زنجیره تأمین فرش ماشینی ایران است. در این پژوهش بر اساس مصاحبه‌های عمیق عوامل کلیدی موفقیت شناسایی شد؛ سپس با رویکرد نقشه شناختی فازی، مدل عوامل کلیدی موفقیت ایجاد شد. جامعه آماری این پژوهش شامل خبرگان صنعت فرش ماشینی است. نتایج نشان داد که 23 عامل کلیدی موفقیت برای مقابله با اثر موجی وجود دارد که در میان آن‌ها «دیجیتال‌سازی زنجیره تأمین»، «مدیران توانمند و کارآمد» و «هماهنگی در زنجیره تأمین» به‌عنوان مهم‌ترین عوامل اثرگذار و «هماهنگی در زنجیره تأمین»، «برنامه‌ریزی مناسب» و «انعطاف‌پذیری در تولید» به‌عنوان مهم‌ترین عوامل مرکزی باید موردتوجه ویژه قرار گیرند؛ درنتیجه به سیاست‌گذاران توصیه می­شود برای ارتقای این موارد به رفع مشکلات هوشمندی کسب‌وکار و زنجیره تأمین، مأموریت­گرا­کردن دانشگاه‌ها، برگزاری کارگاه‌های توانمندسازی مدیران و رفع موانع رفتاری زنجیره تأمین توجه داشته باشند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Presenting a Model of Critical Success Factors to Cope with the Ripple Effect in Iran's Machine-Made Carpet Supply Chain: Corona Pandemic Effects

نویسندگان [English]

  • Esmaeil Mazroui Nasrabadi 1
  • Amin Habibirad 2
  • Abbas Shoul 3
1 Assistant professor, university of Kashan.
2 Assistant professor, Shahed university.
3 Associate Professor, Vali-e-Asr University of Rafsanjan.
چکیده [English]

Supply chains that are meant to survive must be able to cope with disruptions. The COVID-19 pandemic was one of the most important disruptions that could cause a ripple effect in the supply chain. A ripple effect refers to a disruption that affects other parts of the supply chain like a domino effect, and while the probability of its occurrence is low, its impact is high. This study aims to identify the key success factors for coping with the ripple effect in Iran's machine-made carpet supply chain. In this research, the critical success factors were identified based on in-depth interviews. Then, using the fuzzy cognitive map approach, a model of the critical success factors was created. The statistical population of this research consists of experts in the machine-made carpet industry. The findings showed that there are 23 critical success factors to cope with the ripple effect in the machine-made carpet supply chain. Among them, "supply chain digitization", "capable and efficient managers", and "supply chain collaboration" were identified as the most essential influencing factors, while "supply chain collaboration", "proper planning", and "flexibility in production" were identified as the most important central factors that require special attention. Therefore, policymakers are recommended to focus on enhancing these areas by promoting business intelligence and supply chain management, making universities more mission-oriented, organizing empowerment workshops for managers, and removing supply chain behavioral barriers.

کلیدواژه‌ها [English]

  • Supply Chain
  • Ripple Effect
  • Critical Success Factors
  • Corona Pandemic
  • Machine-Made Carpet

مراجع

  1. Dianat, Seyed Ali. (2020). Corona virus and its impact on the textile and carpet industry. Retrieved on 02/02/2022 from: http://www.kashanccima.org. (In Persian).
  2. Dolgui, A., Ivanov, D., & Rozhkov, M. (2020). Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain. International Journal of Production Research, 58(5), 1285-1301.‏
  3. Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: an analysis and recent literature. International Journal of Production Research, 56(1-2), 414-430.‏
  4. Giannoccaro, I., & Iftikhar, A. (2022). Mitigating ripple effect in supply networks: the effect of trust and topology on resilience. International Journal of Production Research, 60(4), 1178-1195.‏
  5. Hendricks, K. B., & Singhal, V. R. (2005). An empirical analysis of the effect of supply chain disruptions on long‐run stock price performance and equity risk of the firm. Production and Operations management, 14(1), 35-52.‏
  6. Islamic Republic News Agency. (2018). A fire broke out in a carpet factory in Kashan. Retrieved on 02/02/2022 from: irna.ir. (In Persian).
  7. Ivanov, D. (2017). Simulation-based ripple effect modelling in the supply chain. International Journal of Production Research, 55(7), 2083-2101.‏
  8. Ivanov, D. (2018). Supply Chain Risk Management: Bullwhip Effect and Ripple Effect. In Structural Dynamics and Resilience in Supply Chain Risk Management(pp. 19-44). Springer, Cham.‏
  9. Ivanov, D. Dolgui, A. & Sokolov, B. (2015). Supply chain design with disruption considerations: Review of research streams on the ripple effect in the supply chain. IFAC-PapersOnLine, 48(3), 1700-1707.‏
  10. Ivanov, D., & Dolgui, A. (2021). OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications. International Journal of Production Economics, 232, 107921.
  11. Jafari, T., Zarei, A., Azar, A., & Moghaddam, A. (2022). Designing a model for the impact of business intelligence on supply chain performance with an emphasis on integration and agility. The Journal of Industrial Management Perspective, 12(3), 279-315. (In Persian).
  12. Kamalian, M. H. (2020). The impact of Corona on the production and distribution of machine-made carpets has been great. Retrieved on 02/02/2022 from: https://kohanjournal.com. (In Persian).
  13. Khakzad, N. (2015). Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures. Reliability Engineering & System Safety, 138, 263-272.‏
  14. laguno, A., Mula, J., & Campuzano-Bolarin, F. (2022). State of the art, conceptual framework and simulation analysis of the ripple effect on supply chains. International Journal of Production Research, 60(6), 2044-2066.‏
  15. Lohmer, J., Bugert, N., & Lasch, R. (2020). Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study. International Journal of Production Economics, 228, 107882.‏
  16. Matsuo, H. (2015). Implications of the Tohoku earthquake for Toyota's coordination mechanism: Supply chain disruption of automotive semiconductors. International Journal of Production Economics, 161, 217-227
  17. Mohaghar, A., & Sadeghi Moghadam, M. (2012). Supply Chain Coordination in The Automotive Industry: A Grounded Theory Approach. The Journal of Industrial Management Perspective, 1(4), 29-63. (In Persian)
  18. Palma, F., Saucedo, J. A., & Marmolejo, J. A. (2018, October). Recovery Method of Supply Chain under Ripple Effect: Supply Chain Event Management (SCEM) Application. In International Conference on Intelligent Computing & Optimization(pp. 455-465). Springer, Cham.‏
  19. Qasam, Ali. (2020). Considerations of price increases and sales of machine-made carpets during the Corona era. Retrieved on 02/02/2022 from: https://kohanjournal.com. (In Persian).
  20. Rahimi, A., Akhavan, P., Philsofian, M., & darabi, A. (2022). Investigating the Effect of using Blockchain Technology on Collaborative Interactions and Performance Improvement in the Defense Industry Supply Chain. The Journal of Industrial Management Perspective, 12(1), 109-134. (In Persian)
  21. Rezaeizadeh, M; Ansari, M & Morphi, I. (2013).A practical guide to the research method: interactive management (IM) and Interpretive Structural Modeling (ISM). Tehran: Jihad Daneshgahi Publications, first edition.
  22. Sawik, T. (2017). A portfolio approach to supply chain disruption management. International Journal of Production Research, 55(7), 1970-1991.
  23. Schneider, M., Shnaider, E., Kandel, A., & Chew, G. (1998). Automatic construction of FCMs. Fuzzy sets and systems, 93(2), 161-172.‏
  24. Sokolov, B. Ivanov, D. Dolgui, A. & Pavlov, A. (2016). Structural quantification of the ripple effect in the supply chain. International Journal of Production Research, 54(1), 152-169.‏
  25. Yılmaz, Ö. F., Özçelik, G., & Yeni, F. B. (2021). Ensuring sustainability in the reverse supply chain in case of the ripple effect: A two-stage stochastic optimization model. Journal of cleaner production, 282, 124548.‏‏

 

 

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