A stochastic program to evaluate disruption mitigation investments in the supply chain

André Snoeck, Maximiliano Udenio and Jan C. Fransoo

European Journal of Operational Research, 2019, vol. 274, issue 2, 516-530

Abstract: Supply chain risk management is becoming increasingly important due to a variety of natural and man-made uncertainties. We develop a methodology to evaluate the costs of disruptions and the value of supply chain network mitigation options based on a two-stage stochastic program. To solve the model, we rely on a solution scheme based on sample average approximation. We explicitly differentiate between disruption periods and business as usual periods to decrease the model size and computational requirements by approximately 85% and 95%, respectively. Furthermore, the decrease in model complexity allows us to include the conditional value at risk in the objective function to incorporate the risk aversion of decisions makers. Based on a case study of a chemical supply chain, this study shows the trade-off between long-term expected costs minimization and short term risk minimization, where the latter leads to a more aggressive investment policy.

Keywords: Stochastic programming; Supply chain network design; Supply chain risk management (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:274:y:2019:i:2:p:516-530

DOI: 10.1016/j.ejor.2018.10.005

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