A new modelling approach of evaluating preventive and reactive strategies for mitigating supply chain risks

Qazi, Abroon and Quigley, John and Dickson, Alex and Gaudenzi, Barbara; Corman, Francesco and Voß, Stefan and Negenborn, Rudy R., eds. (2015) A new modelling approach of evaluating preventive and reactive strategies for mitigating supply chain risks. In: Computational Logistics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer-Verlag, NLD, pp. 569-585. ISBN 9783319242637 (https://doi.org/10.1007/978-3-319-24264-4_39)

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Abstract

Supply chains are becoming more complex and vulnerable due to globalization and interdependency between different risks. Existing studies have focused on identifying different preventive and reactive strategies for mitigating supply chain risks and advocating the need for adopting specific strategy under a particular situation. However, current research has not addressed the issue of evaluating an optimal mix of preventive and reactive strategies taking into account their relative costs and benefits within the supply network setting of interconnected firms and organizations. We propose a new modelling approach of evaluating different combinations of such strategies using Bayesian belief networks. This technique helps in determining an optimal solution on the basis of maximum improvement in the network expected loss. We have demonstrated our approach through a simulation study and discussed practical and managerial implications.

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