Enhancing Supply Chain Resilience in Disruption Caused by Catastrophes Such as Pandemics: An Integrated Grey Analysis-Stochastic Optimization Model

Fady Ahmed; Ibrahim Abdelfadeel Shaban

doi:10.31081/dmame8220251649

Authors

Fady Ahmed Department of Mechanical and Aerospace, United Arab Emirates University, Al Ain, UAEU.
Ibrahim Abdelfadeel Shaban Department of Mechanical and Aerospace, United Arab Emirates University, Al Ain, UAEU

DOI:

https://doi.org/10.31081/dmame8220251649

Keywords:

Supplier selection; Grey relational analysis; Stochastic Programming; Inventory resilience; Supply chain disruption.

Abstract

This research presents a comprehensive decision-making model for optimizing global supply chain operations during disruptions caused by catastrophes, e.g., pandemics, making a significant contribution to the supply chain management literature. The model integrates Grey optimal ranking and employs a scenario-based stochastic Mixed-Integer Programming approach to enhance resilience. It addresses the 'ripple effect' of regional disruptions by modelling simultaneous impacts on supply, demand, and logistics. Computational examples, based on a real-world case during pandemic, validate the model's effectiveness in minimizing costs and ensuring availability, quality, and emission levels. The study reveals the numerical significance of traditional resilience measures, such as pre-positioning Recovery Materials Inventory and utilizing recovery supplies. Results demonstrate the model's capacity to mitigate the impacts of multi-regional disruptions. In essence, the research provides a quantifiable and practical contribution, offering insights for resilient supply chain management in the face of pandemic disruptions.

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References

[1] Burkacky, O., Deichmann, J., P, P., & Werra, J. (2022). Semiconductor shortage: How the automotive industry can succeed. https://www.mckinsey.com/industries/semiconductors/our-insights/semiconductor-shortage-how-the-automotive-industry-can-succeed

[2] Chen, L. M., Liu, Y. E., & Yang, S. J. S. (2015). Robust supply chain strategies for recovering from unanticipated disasters. Transportation Research Part E: Logistics and Transportation Review, 77, 198-214. https://doi.org/10.1016/j.tre.2015.02.015

[3] Cheramin, M., Saha, A. K., Cheng, J. Q., Paul, S. K., & Jin, H. Y. (2021). Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition. Transportation Research Part E: Logistics and Transportation Review, 155, 102505. https://doi.org/10.1016/j.tre.2021.102505

[4] Chowdhury, M. T., Sarkar, A., Paul, S. K., & Moktadir, M. A. (2022). A case study on strategies to deal with the impacts of COVID-19 pandemic in the food and beverage industry. Operations Management Research, 15(1), 166-178. https://doi.org/10.1007/s12063-020-00166-9

[5] Chowdhury, P., Paul, S. K., Kaisar, S., & Moktadir, M. A. (2021). COVID-19 pandemic related supply chain studies: A systematic review. Transportation Research Part E: Logistics and Transportation Review, 148, 102271. https://doi.org/10.1016/j.tre.2021.102271

[6] Freeman, N., Mittenthal, J., Keskin, B., & Melouk, S. (2018). Sourcing strategies for a capacitated firm subject to supply and demand uncertainty. Omega, 77, 127-142. https://doi.org/10.1016/j.omega.2017.06.004

[7] Gholami-Zanjani, S. M., Jabalameli, M. S., Klibi, W., & Pishvaee, M. S. (2021). A robust location-inventory model for food supply chains operating under disruptions with ripple effects. International Journal of Production Research, 59(1), 301-324. https://doi.org/10.1080/00207543.2020.1834159

[8] He, J., Alayifard, F., Iyanov, D., & Jahani, H. (2019). A real-option approach to mitigate disruption risk in the supply chain. Omega, 88, 133-149. https://doi.org/10.1016/j.omega.2018.08.008

[9] Hosseini, S., Tajik, N., Ivanov, D., Sarder, M. D., Barker, K., & Al Khaled, A. (2019). Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics, 213, 124-137. https://doi.org/10.1016/j.ijpe.2019.03.018

[10] Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922

[11] Ivanov, D. (2022). Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, 319(1), 1411-1431. https://doi.org/10.1007/s10479-020-03640-6

[12] Kettering Global, O. (2018). The Impact of Political Instability on Global Supply Chain. https://online.kettering.edu/news/impact-political-instability-global-supply-chain

[13] Lei, Z., Lim, M. K., Cui, L., & Wang, Y. (2021). Modelling of risk transmission and control strategy in the transnational supply chain. International Journal of Production Research, 59(1), 148-167. https://doi.org/10.1080/00207543.2019.1698782

[14] Liu, M., Liu, Z., Chu, F., Zheng, F., & Chu, C. (2021). A new robust dynamic Bayesian network approach for disruption risk assessment under the supply chain ripple effect. International Journal of Production Research, 59(1), 265-285. https://doi.org/10.1080/00207543.2020.1841318

[15] Lücker, F., & Seifert, R. W. (2017). Building up resilience in a pharmaceutical supply chain through inventory, dual sourcing and agility capacity. Omega, 73, 114-124. https://doi.org/10.1016/j.omega.2017.01.001

[16] Lücker, F., Seifert, R. W., & Biçer, I. (2019). Roles of inventory and reserve capacity in mitigating supply chain disruption risk. International Journal of Production Research, 57(4), 1238-1249. https://doi.org/10.1080/00207543.2018.1504173

[17] Mishra, D., Dwivedi, Y. K., Rana, N. P., & Hassini, E. (2021). Evolution of supply chain ripple effect: a bibliometric and meta-analytic view of the constructs. International Journal of Production Research, 59(1), 129-147. https://doi.org/10.1080/00207543.2019.1668073

[18] Paul, S. K., & Chowdhury, P. (2020). Strategies for Managing the Impacts of Disruptions During COVID-19: an Example of Toilet Paper. Global Journal of Flexible Systems Management, 21(3), 283-293. https://doi.org/10.1007/s40171-020-00248-4

[19] Reuters, & Klayman, B. (2021). Ford details new production cuts due to global chip shortage. https://www.reuters.com/business/autos-transportation/ford-outlines-further-production-cuts-due-global-chip-shortage-2021-04-14/

[20] Sawik, T. (2011). Selection of a dynamic supply portfolio in make-to-order environment with risks. Computers & Operations Research, 38(4), 782-796. https://doi.org/10.1016/j.cor.2010.09.011

[21] Sawik, T. (2013). Integrated selection of suppliers and scheduling of customer orders in the presence of supply chain disruption risks. International Journal of Production Research, 51(23-24), 7006-7022. https://doi.org/10.1080/00207543.2013.852702

[22] Sawik, T. (2015). On the fair optimization of cost and customer service level in a supply chain under disruption risks. Omega: The International Journal of Management Science, 53, 58-66. https://doi.org/10.1016/j.omega.2014.12.004

[23] Sawik, T. (2018). Selection of a dynamic supply portfolio under delay and disruption risks. International Journal of Production Research, 56(1-2), 760-782. https://doi.org/10.1080/00207543.2017.1401238

[24] Sawik, T. (2020). A two-period model for selection of resilient multi-tier supply portfolio. International Journal of Production Research, 58(19), 6043-6060. https://doi.org/10.1080/00207543.2019.1665204

[25] Sawik, T. (2021). On the risk-averse selection of resilient multi-tier supply portfolio. Omega, 101, 102267. https://doi.org/10.1016/j.omega.2020.102267

[26] Sawik, T. (2022). Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study. Omega, 109, 102596. https://doi.org/10.1016/j.omega.2022.102596

[27] Zhao, M., & Freeman, N. K. (2019). Robust sourcing from suppliers under ambiguously correlated major disruption risks. Production and Operations Management, 28(2), 441-456. https://doi.org/10.1111/poms.12933

[28] Ivanov D. Transformation of supply chain resilience research through the COVID-19 pandemic. International Journal of Production Research. 2024 Dec 1;62(23):8217-38.