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

Qazi, Abroon; Akhtar, Pervaiz

Abstract

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.

Citation

Qazi, A., & Akhtar, P. (2018). Risk matrix driven supply chain risk management: Adapting risk matrix based tools to modelling interdependent risks and risk appetite. Computers and Industrial Engineering, https://doi.org/10.1016/j.cie.2018.08.002

• Journal Article Type: Article
• Acceptance Date: Aug 1, 2018
• Online Publication Date: Aug 4, 2018
• Publication Date: 2018-08
• Deposit Date: Sep 8, 2018
• Publicly Available Date: Sep 12, 2018
• Print ISSN: 0360-8352
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1016/j.cie.2018.08.002
• Keywords: Supply chain risk management; Systemic risks; Risk appetite; Utility indifference curves; Risk matrix; Algorithms
• Public URL: https://hull-repository.worktribe.com/output/1029572
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0360835218303747