Risk Management of Disruption and Sustainable Development of Supply Chains

Document Type : Research Paper


1 MA Student of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran

2 Assistant Professor, Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran

3 Associate Professor, Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran


This study proposes a multi-stage supply chain model with direct and reverse flows of goods to assess the effects of risk on the profit of a supply chain network and the realization of demand. The studied network aims to maximize profit, minimize unmet demand, reduce delivery time, alleviate disruption risks in facilities and transportation, and decrease supply chain visibility. We created a system for quantifying the disruption risk ratings of supply chain components. To help the company better understand its suppliers, address essential network components, and prioritize risk management initiatives, the evaluation may be useful. For our supply chain optimization models, we rely on the predicted disruption risk ratings as a basis. Goal programming is used to solve the multi-criteria model. The resiliency of the supply chain network is shown numerically. In order to build the model, the designer had to make strategic judgments. Risk mitigation methods such as extra inventory and backup suppliers are adopted to increase the supply chain network’s resiliency. Short-term disruptions may be mitigated by stockpiling additional raw materials to avoid component shortages. A cost-benefit analysis shows that every risk reduction strategy is worthwhile.


Main Subjects

Article Title [Persian]

مدیریت ریسک اختلال و توسعه پایدار زنجیره‌های تامین

Authors [Persian]

  • سید رضا هاشمی 1
  • عبدالله آراسته 2
  • محمدمهدی پایدار 3
1 دانشجوی کارشناسی ارشد، گروه مهندسی صنایع، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران
2 استادیار، گروه مهندسی صنایع، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران
3 دانشیار، گروه مهندسی صنایع، دانشگاه صنعتی نوشیروانی بابل، بابل، ایران
Abstract [Persian]

این مطالعه یک مدل زنجیره تأمین چند مرحله‌ای با جریان‌های مستقیم و معکوس کالا را برای ارزیابی اثرات ریسک بر سود شبکه زنجیره تأمین و تحقق تقاضا پیشنهاد می‌کند. هدف شبکه مورد مطالعه به حداکثر رساندن سود، به حداقل رساندن تقاضای برآورده نشده، کاهش زمان تحویل، کاهش خطرات اختلال در تأسیسات و حمل و نقل و کاهش دید زنجیره تأمین است. سیستمی برای تعیین رتبه‌بندی ریسک اختلال اجزای زنجیره تأمین ایجاد شد. برای کمک به شرکت برای درک بهتر تأمین کنندگان خود، رسیدگی به اجزای ضروری شبکه و اولویت‌بندی ابتکارات مدیریت ریسک، ارزیابی ممکن است مفید باشد. برای مدل‌های بهینه‌سازی زنجیره تأمین، به رتبه‌بندی ریسک اختلال پیش‌بینی‌شده به عنوان مبنایی تکیه می‌کنیم. برای حل مدل چند معیاره از برنامه‌ریزی آرمانی استفاده می شود. انعطاف پذیری شبکه زنجیره تأمین به صورت عددی نشان داده شده است. برای ساخت مدل، طراح باید قضاوت استراتژیک نماید. روش‌های کاهش ریسک مانند موجودی اضافی و تأمین‌کنندگان پشتیبان برای افزایش انعطاف‌پذیری شبکه زنجیره تأمین اتخاذ می‌شوند. اختلالات کوتاه مدت ممکن است با ذخیره مواد خام اضافی برای جلوگیری از کمبود قطعات کاهش یابد. تجزیه و تحلیل هزینه و فایده نشان می دهد که هر استراتژی کاهش ریسک ارزشمند است.

Keywords [Persian]

  • بهینه‌سازی چند هدفه
  • برنامه‌ریزی آرمانی
  • لکسیکوگرافی
  • روش وزن دهی
Bas, E. (2018). An integrated OSH risk management approach to surgical flow disruptions in operating rooms. Safety Science, 109, 281-293.
Benjamin, M. F. D., Tan, R. R., & Razon, L. F. (2015). Probabilistic multi-disruption risk analysis in bioenergy parks via physical input–output modeling and analytic hierarchy process. Sustainable Production and Consumption, 1, 22-33.
Boy, M., Karl, T., Turnipseed, A., Mauldin, R. L., Kosciuch, E., Greenberg, J., Rathbone, J., Smith, J., Held, A., & Barsanti, K. (2008). New particle formation in the Front Range of the Colorado Rocky Mountains. Atmospheric Chemistry and Physics, 8(6), 1577-1590.
Chakraborty, T., Shibata, Y., Zhou, L.-Y., Katsu, Y., Iguchi, T., & Nagahama, Y. (2011). Differential expression of three estrogen receptor subtype mRNAs in gonads and liver from embryos to adults of the medaka, Oryzias latipes. Molecular and Cellular Endocrinology, 333(1), 47-54.
Chan, H. K., & Chan, F. T. (2010). A review of coordination studies in the context of supply chain dynamics. International Journal of Production Research, 48(10), 2793-2819.
Charlesworth, S., De Miguel, E., & Ordóñez, A. (2011). A review of the distribution of particulate trace elements in urban terrestrial environments and its application to considerations of risk. Environmental geochemistry and health, 33(2), 103-123.
Choi, T.-M., Wen, X., Sun, X., & Chung, S.-H. (2019). The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era. Transportation Research Part E: Logistics and Transportation Review, 127, 178-191.
Davé, V. A., & Klein, R. S. (2022). The multitaskers of the brain: Glial responses to viral infections and associated post‐infectious neurologic sequelae. Glia.
Diabat, A., Jabbarzadeh, A., & Khosrojerdi, A. (2019). A perishable product supply chain network design problem with reliability and disruption considerations. International Journal of Production Economics, 212, 125-138.
El Baz, J., & Ruel, S. (2021). Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era. International Journal of Production Economics, 233, 107972.
Enyinda, C. I., Ogbuehi, A., & Briggs, C. (2008). Global supply chain risks management: A new battleground for gaining competitive advantage. Proceedings of ASBBS, 15(1), 278-292.
Giannakis, M., & Papadopoulos, T. (2016). Supply chain sustainability: A risk management approach. International Journal of Production Economics, 171, 455-470.
Hafezalkotob, A., Hafezalkotob, A., Liao, H., & Herrera, F. (2019). An overview of MULTIMOORA for multi-criteria decision-making: Theory, developments, applications, and challenges. Information Fusion, 51, 145-177.
Hosseini-Motlagh, S.-M., Ebrahimi, S., & Zirakpourdehkordi, R. (2020). Coordination of dual-function acquisition price and corporate social responsibility in a sustainable closed-loop supply chain. Journal of Cleaner Production, 251, 119629.
Ivanov, D., & Dolgui, A. (2019). New disruption risk management perspectives in supply chains: Digital twins, the ripple effect, and resileanness. IFAC-PapersOnLine, 52(13), 337-342.
Knisley, D., & Knisley, J. (2011). Predicting protein–protein interactions using graph invariants and a neural network. Computational biology and chemistry, 35(2), 108-113.
Lee, H. L. (2002). Aligning supply chain strategies with product uncertainties. California Management Review, 44(3), 105-119.
Loh, H. S., & Van Thai, V. (2015). Cost consequences of a port-related supply chain disruption. The Asian Journal of Shipping and Logistics, 31(3), 319-340.
Majumder, M. (2015). Multi criteria decision making. In Impact of urbanization on water shortage in face of climatic aberrations (pp. 35-47). Springer.
Manikandan, N. U. (2009). Modeling and analysis of a four stage multi-period supply chain.
Masud, A. S., & Ravindran, A. R. (2008). Multiple criteria decision making. In: CRC Press, An imprint of the Taylor and Francis Group.
Mazza, P. P., Lovari, S., Masini, F., Masseti, M., & Rustioni, M. (2013). A multidisciplinary approach to the analysis of multifactorial land mammal colonization of islands. BioScience, 63(12), 939-951.
Mensah, P., Merkuryev, Y., & Longo, F. (2015). Using ICT in developing a resilient supply chain strategy. Procedia Computer Science, 43, 101-108.
Moshood, T. D., Nawanir, G., Mahmud, F., Mohamad, F., Ahmad, M. H., & AbdulGhani, A. (2022). Sustainability of biodegradable plastics: New problem or solution to solve the global plastic pollution? Current Research in Green and Sustainable Chemistry, 100273.
Nahum, O. E., & Hadas, Y. (2020). Multi-objective optimal allocation of wireless bus charging stations considering costs and the environmental impact. Sustainability, 12(6), 2318.
Odu, G., & Charles-Owaba, O. (2013). Review of multi-criteria optimization methods–theory and applications. IOSR Journal of Engineering, 3(10), 01-14.
Oliveira, J. B., Jin, M., Lima, R. S., Kobza, J. E., & Montevechi, J. A. B. (2019). The role of simulation and optimization methods in supply chain risk management: Performance and review standpoints. Simulation Modelling Practice and Theory, 92, 17-44.
Ravindran, A. R., & Warsing Jr., D. P. (2016). Supply chain engineering: Models and applications. CRC Press.
Ri, J. S., Choe, S. H., Schleusener, J., Lademann, J., Choe, C. S., & Darvin, M. E. (2020). In vivo tracking of DNA for precise determination of the stratum corneum thickness and superficial microbiome using confocal Raman microscopy. Skin Pharmacology and Physiology, 33(1), 30-37.
Samvedi, A., Jain, V., & Chan, F. T. (2013). Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS. International Journal of Production Research, 51(8), 2433-2442.
Sawik, T. (2013). Selection of resilient supply portfolio under disruption risks. Omega, 41(2), 259-269.
Shu, T., Chen, S., Wang, S., & Lai, K. K. (2014). GBOM-oriented management of production disruption risk and optimization of supply chain construction. Expert Systems with Applications, 41(1), 59-68.
Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., & Shankar, R. (2008). Designing and managing the supply chain: Concepts, strategies and case studies. Tata McGraw-Hill Education.
Smith, S. A. (2012). A network planning process and inventory strategy for high-mix low-volume markets (Doctoral dissertation, Massachusetts Institute of Technology)..
Solo, C. J. (2009). Multi-objective, integrated supply chain design and operation under uncertainty. The Pennsylvania State University.
Teuscher, P., Grüninger, B., & Ferdinand, N. (2006). Risk management in sustainable supply chain management (SSCM): Lessons learnt from the case of GMO‐free soybeans. Corporate Social Responsibility and Environmental Management, 13(1), 1-10.
Torres-Ruiz, A., & Ravindran, A. R. (2019). Use of interval data envelopment analysis, goal programming and dynamic eco-efficiency assessment for sustainable supplier management. Computers & Industrial Engineering, 131, 211-226.
Valinejad, F., & Rahmani, D. (2018). Sustainability risk management in the supply chain of telecommunication companies: A case study. Journal of Cleaner Production, 203, 53-67.
Vandchali, H. R., Cahoon, S., & Chen, S.-L. (2021). The impact of supply chain network structure on relationship management strategies: An empirical investigation of sustainability practices in retailers. Sustainable Production and Consumption, 28, 281-299.
Wagner, S. M., & Bode, C. (2008). An empirical examination of supply chain performance along several dimensions of risk. Journal of Business Logistics, 29(1), 307-325.
Waters, D. (2011). Supply chain risk management: Vulnerability and resilience in logistics. Kogan Page Publishers.
Wide, P. (2020). Improving decisions support for operational disruption management in freight transport. Research in Transportation Business & Management, 37, 100540.
Xu, S., Zhang, X., Feng, L., & Yang, W. (2020). Disruption risks in supply chain management: A literature review based on bibliometric analysis. International Journal of Production Research, 58(11), 3508-3526.
Yang, T. (2006). Multi objective optimization models for managing supply risk in supply chains.
Yu, M.-C., & Goh, M. (2014). A multi-objective approach to supply chain visibility and risk. European Journal of Operational Research, 233(1), 125-130.
Zhao, K., Kumar, A., Harrison, T. P., & Yen, J. (2011). Analyzing the resilience of complex supply network topologies against random and targeted disruptions. IEEE Systems Journal, 5(1), 28-39.