Supply Chain Reshoring: Risk-Neutral vs. Risk-Averse Decision-Making

Tadeusz Sawik

Chapter Chapter 7 in Stochastic Programming in Supply Chain Risk Management, 2024, pp 219-260 from Springer

Abstract: Abstract This chapter presents a scenario-based stochastic MIP (mixed integer programming) models for risk-neutral and 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 the risk-neutral reshoring, a portfolio of supply chain nodes with the 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 the risk-averse decision-making, if reshoring is incapable of reducing worst-case cost, in particular, worst-case lost sales, no reshoring is selected. Major decision-making insights drawn from the theoretical and computational studies are summarized at the end of this chapter.

Keywords: Supply chain reshoring; Ripple effect; Scheduling and reshoring integration; Government subsidy; Expected net savings; Conditional cost-at-risk; Conditional service-at-risk (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-57927-1_7

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