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

Abroon Qazi, Mecit Can Emre Simsekler, Steven Formaneck

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    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.

    Original languageBritish English
    Pages (from-to)241-272
    Number of pages32
    JournalAnnals of Operations Research
    Volume322
    Issue number1
    DOIs
    StatePublished - Mar 2023

    Keywords

    • Bayesian belief network
    • Monte Carlo simulation
    • Performance measures
    • Risk metrics
    • Risk network value at risk
    • Supply chain risk management

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