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Reshore or not Reshore: A Stochastic Programming Approach to Supply Chain Optimization

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  • Sawik, Tadeusz

Abstract

This paper presents a scenario-based stochastic mixed integer programming model for risk-neutral or risk-averse optimization of supply chain reshoring to domestic region, under the ripple effect propagated from a foreign disruption source region. The reshoring decisions with respect to tier-one suppliers of parts and tier-zero OEM (Original Equipment Manufacturer) assembly plants are considered under disruptions in supply, manufacturing, logistics and demand rippling across the entire supply chain. The proposed innovative approach integrates strategic supply chain reshoring and operational supply chain scheduling, which allows the decision maker to evaluate the operational impact of the strategic decision. Results of computational experiments, partially modeled after a supply chain reshoring problem in the smartphone industry, are provided. The findings indicate that reshoring decisions are strongly dependent on the level of government subsidy for capital expenditure and for risk-neutral reshoring, a portfolio of supply chain nodes with positive expected net savings can be considered only. In general, the reshored supply chain can better meet domestic market demand. Moreover, full reshoring of a supply chain improves its business as usual performance and even partial reshoring mitigates the impact of the ripple effect. However, for risk-averse decision-making, if reshoring is incapable of reducing worst-case cost, in particular, worst-case lost sales, no reshoring is selected.

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  • Sawik, Tadeusz, 2023. "Reshore or not Reshore: A Stochastic Programming Approach to Supply Chain Optimization," Omega, Elsevier, vol. 118(C).
    • Handle: RePEc:eee:jomega:v:118:y:2023:i:c:s0305048323000282
    DOI: 10.1016/j.omega.2023.102863
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    References listed on IDEAS

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    1. Sawik, Tadeusz, 2022. "Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study," Omega, Elsevier, vol. 109(C).
    2. Jan Stentoft & Jan Olhager & Jussi Heikkilä & Lisa Thoms, 2016. "Manufacturing backshoring: a systematic literature review," Operations Management Research, Springer, vol. 9(3), pages 53-61, December.
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    4. Sabet, Ehsan & Yazdani, Baback & Kian, Ramez & Galanakis, Kostas, 2020. "A strategic and global manufacturing capacity management optimisation model: A Scenario-based multi-stage stochastic programming approach," Omega, Elsevier, vol. 93(C).
    5. Kim, Yun Geon & Chung, Byung Do, 2022. "Closed-loop supply chain network design considering reshoring drivers," Omega, Elsevier, vol. 109(C).
    6. Pearce, John A., 2014. "Why domestic outsourcing is leading America's reemergence in global manufacturing," Business Horizons, Elsevier, vol. 57(1), pages 27-36.
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    8. Sawik, Tadeusz, 2015. "On the fair optimization of cost and customer service level in a supply chain under disruption risks," Omega, Elsevier, vol. 53(C), pages 58-66.
    9. Tadeusz Sawik, 2023. "A stochastic optimisation approach to maintain supply chain viability under the ripple effect," International Journal of Production Research, Taylor & Francis Journals, vol. 61(8), pages 2452-2469, April.
    10. Ivanov, Dmitry, 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, Elsevier, vol. 136(C).
    11. Paolo Barbieri & Albachiara Boffelli & Stefano Elia & Luciano Fratocchi & Matteo Kalchschmidt & Danny Samson, 2020. "What can we learn about reshoring after Covid-19?," Operations Management Research, Springer, vol. 13(3), pages 131-136, December.
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    1. Liu, Ming & Ding, Yueyu & Chu, Feng & Dolgui, Alexandre & Zheng, Feifeng, 2024. "Robust actions for improving supply chain resilience and viability," Omega, Elsevier, vol. 123(C).

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