Economically viable reshoring of supply chains under ripple effect

Tadeusz Sawik

Omega, 2025, vol. 131, issue C

Abstract: This paper presents stochastic mixed integer linear programming models for economically viable supply chain reshoring under the ripple effect, that is, for the integrated optimization of supply chain reshoring and supply chain viability. The problem objective is to select supply chain facilities for relocation and backup suppliers for recovery supplies to simultaneously minimize cost, maximize service and enhance viability. If the primary supplier is not selected for reshoring, the viability can alternatively be improved by recovery supplies from the backup supplier. The problem is formulated as a multi-portfolio stochastic optimization. The portfolio of suppliers/plants for reshoring is optimized along with an alternative recovery portfolio of backup supplies. This study indicates that reshoring decisions directly affect supply chain viability and have positive impact on both its average and worst-case performance. The more risk-aversive is viable reshoring (the confidence level greater than 0.90), the greater is also improvement of supply chain business-as-usual resilience. If increase of expected customer service is more important than cost reduction, supplier reshoring is preferable to backup sourcing. The findings also indicate that cost- and service-optimal reshoring decisions are getting closer when the government subsidy for reshoring significantly increases such that the reduced reshoring costs and domestic costs do not exceed the achieved reduction of lost sales and backup sourcing. Then both conflicting objectives can be simultaneously achieved, which is a highly desirable solution of the economically viable reshoring of supply chain. Numerical examples based on a real-world industrial supply chain prove practical usefulness of the proposed stochastic approach and its high computational efficiency.

Keywords: Supply chain; Disruption management; Resilience; Viability; Ripple effect; Stochastic programming (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.omega.2024.103228

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