Risk matrix driven supply chain risk management: Adapting risk matrix based tools to modelling interdependent risks and risk appetite

•A major research gap is identified in the literature on supply chain risk management.•A new risk management process is introduced to narrow the research gap.•The conventional risk matrix is adapted to modelling interdependent risks.•Algorithms are proposed for managing risks specific to the risk ap...

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Bibliographic Details
Published inComputers & industrial engineering Vol. 139; p. 105351
Main Authors Qazi, Abroon, Akhtar, Pervaiz
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.01.2020
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ISSN0360-8352
1879-0550
DOI10.1016/j.cie.2018.08.002

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Summary:•A major research gap is identified in the literature on supply chain risk management.•A new risk management process is introduced to narrow the research gap.•The conventional risk matrix is adapted to modelling interdependent risks.•Algorithms are proposed for managing risks specific to the risk appetite.•The proposed approach is demonstrated through a simulation study. There is a major research gap of developing a supply chain risk management process integrating the risk appetite of a decision maker and all stages of the risk management process within an interdependent network of systemic risks. We introduce an iterative process, namely risk matrix driven supply chain risk management, to bridge this gap. We make use of the recently introduced concept of utility indifference curves based risk matrix to capture the risk attitude of a decision maker. We also present algorithms for assessing and mitigating interdependent risks for risk-neutral and risk-averse/seeking decision makers and demonstrate the application of our proposed process through a simulation study. Utilising the method of cost-benefit analysis within an interdependent setting of interacting risks and risk mitigation strategies, we also propose a second approach that can help a decision maker to determine a set of Pareto-optimal risk mitigation strategies and select optimal solutions subject to the budget constraint and specific risk appetite.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2018.08.002