Supply chain risk network value at risk assessment using Bayesian belief networks and Monte Carlo simulation

Abroon Qazi (), Mecit Can Emre Simsekler and Steven Formaneck
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Abroon Qazi: American University of Sharjah
Mecit Can Emre Simsekler: Khalifa University of Science and Technology
Steven Formaneck: Canadian University Dubai

Annals of Operations Research, 2023, vol. 322, issue 1, No 11, 272 pages

Abstract: Abstract Several techniques have been proposed in supply chain risk management to capture causality among risks in a network setting and prioritize risks regarding their network-wide propagation impact. However, these techniques might be unable to capture the Risk Network Value at Risk (RNVaR), the maximum risk exposure expected at a given confidence level for a given timeframe, associated with individual supply chain performance measures within a network setting. With an exclusive focus on using point estimates in existing techniques, there is a risk of overlooking tail distributions and ignoring critical risks. In this paper, we aim to address this research gap by introducing new risk metrics and a new process theoretically grounded in Bayesian Belief Network and Monte Carlo Simulation frameworks. Integrating these two techniques helps establish the RNVaR that is associated with different performance measures and the relative importance of individual risks for resource allocation. We demonstrate the application of the proposed process through a real case study in the telecommunications industry and compare the results of this study with existing approaches.

Keywords: Supply chain risk management; Bayesian belief network; Monte Carlo simulation; Risk network value at risk; Risk metrics; Performance measures (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-022-04598-3

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