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Risk diversification and risk pooling in supply chain design

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  • Ho-Yin Mak
  • Zuo-Jun Shen

Abstract

Recent research has pointed out that the optimal strategies to mitigate supply disruptions and demand uncertainty are often mirror images of each other. In particular, risk diversification is favorable under the threat of disruptions and risk pooling is favorable under demand uncertainty. This article studies how dynamic sourcing in supply chain design provides partial benefits of both strategies. Optimization models are formulated for supply chain network design with dynamic sourcing under the risk of temporally dependent and temporally independent disruptions of facilities. Using computational experiments, it is shown that supply chain networks that allow small to moderate degrees of dynamic sourcing can be very robust against both disruptions and demand uncertainty. Insights are attained on the optimal degree of dynamic sourcing under different conditions.

Suggested Citation

  • Ho-Yin Mak & Zuo-Jun Shen, 2012. "Risk diversification and risk pooling in supply chain design," IISE Transactions, Taylor & Francis Journals, vol. 44(8), pages 603-621.
  • Handle: RePEc:taf:uiiexx:v:44:y:2012:i:8:p:603-621
    DOI: 10.1080/0740817X.2011.635178
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    Cited by:

    1. Cecil Ash & Uday Venkatadri & Claver Diallo & Peter Vanberkel & Ahmed Saif, 2023. "PPE Supply Optimization Under Risks of Disruption from the COVID-19 Pandemic," SN Operations Research Forum, Springer, vol. 4(2), pages 1-29, June.
    2. Ho-Yin Mak & Zuo-Jun Max Shen, 2014. "Pooling and Dependence of Demand and Yield in Multiple-Location Inventory Systems," Manufacturing & Service Operations Management, INFORMS, vol. 16(2), pages 263-269, May.
    3. Kulkarni, Onkar & Dahan, Mathieu & Montreuil, Benoit, 2022. "Resilient Hyperconnected Parcel Delivery Network Design Under Disruption Risks," International Journal of Production Economics, Elsevier, vol. 251(C).
    4. Fattahi, Mohammad & Govindan, Kannan & Keyvanshokooh, Esmaeil, 2017. "Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 176-200.
    5. Parajuli, Anubhuti & Kuzgunkaya, Onur & Vidyarthi, Navneet, 2017. "Responsive contingency planning of capacitated supply networks under disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 102(C), pages 13-37.
    6. Ramezani, Javaneh & Camarinha-Matos, Luis M., 2020. "Approaches for resilience and antifragility in collaborative business ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    7. Ahmadi-Javid, Amir & Seddighi, Amir Hossein, 2013. "A location-routing problem with disruption risk," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 53(C), pages 63-82.
    8. Cheramin, Meysam & Saha, Apurba Kumar & Cheng, Jianqiang & Paul, Sanjoy Kumar & Jin, Hongyue, 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, Elsevier, vol. 155(C).
    9. Zhu, Xiaoyan & Cao, Yunzhi, 2021. "The optimal recovery-fund based strategy for uncertain supply chain disruptions: A risk-averse two-stage stochastic programming approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    10. Yu, Guodong & Haskell, William B. & Liu, Yang, 2017. "Resilient facility location against the risk of disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 82-105.
    11. Fattahi, Mohammad & Govindan, Kannan, 2018. "A multi-stage stochastic program for the sustainable design of biofuel supply chain networks under biomass supply uncertainty and disruption risk: A real-life case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 534-567.
    12. Fattahi, Mohammad & Govindan, Kannan & Maihami, Reza, 2020. "Stochastic optimization of disruption-driven supply chain network design with a new resilience metric," International Journal of Production Economics, Elsevier, vol. 230(C).
    13. Yu, Guodong & Zhang, Jie, 2018. "Multi-dual decomposition solution for risk-averse facility location problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 70-89.
    14. Heckmann, Iris & Comes, Tina & Nickel, Stefan, 2015. "A critical review on supply chain risk – Definition, measure and modeling," Omega, Elsevier, vol. 52(C), pages 119-132.
    15. David Simchi-Levi & William Schmidt & Yehua Wei & Peter Yun Zhang & Keith Combs & Yao Ge & Oleg Gusikhin & Michael Sanders & Don Zhang, 2015. "Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain," Interfaces, INFORMS, vol. 45(5), pages 375-390, October.
    16. Woldt, Jason & Godfrey, Michael, 2022. "Is there a home field advantage? The impact of shareholder wealth from U.S. manufacturing location decisions: A comparative analysis," International Journal of Production Economics, Elsevier, vol. 248(C).
    17. Zhao, Kena & Ng, Tsan Sheng & Tan, Chin Hon & Pang, Chee Khiang, 2021. "An almost robust model for minimizing disruption exposures in supply systems," European Journal of Operational Research, Elsevier, vol. 295(2), pages 547-559.
    18. Fattahi, Mohammad, 2021. "Resilient procurement planning for supply chains: A case study for sourcing a critical mineral material," Resources Policy, Elsevier, vol. 74(C).
    19. Chaolin Yang & Zhenyu Hu & Sean X. Zhou, 2021. "Multilocation Newsvendor Problem: Centralization and Inventory Pooling," Management Science, INFORMS, vol. 67(1), pages 185-200, January.
    20. Xiao Zhao & Xuhui Xia & Lei Wang & Guodong Yu, 2018. "Risk-Averse Facility Location for Green Closed-Loop Supply Chain Networks Design under Uncertainty," Sustainability, MDPI, vol. 10(11), pages 1-17, November.

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