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

Date
2022
Authors
Qazi, Abroon
Simsekler, Mecit Can Emre
Formaneck, Steven
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
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. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Description
This article is not available at CUD collection. The version of scholarly record of this paper is published in Annals of Operations Research (2022), available online at: https://doi.org/10.1007/s10479-022-04598-3
Keywords
Bayesian belief network, Monte Carlo simulation, Performance measures, Risk metrics, Risk network value at risk, Supply chain risk management
Citation
Qazi, A., Simsekler, M. C. E., & Formaneck, S. (2022). Supply chain risk network value at risk assessment using bayesian belief networks and monte carlo simulation. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04598-3