Worst Expected Best method for assessment of probabilistic network expected value at risk: application in supply chain risk management
International Journal of Quality & Reliability Management
ISSN: 0265-671X
Article publication date: 11 March 2021
Issue publication date: 14 January 2022
Abstract
Purpose
The purpose of this paper is to develop and operationalize a process for prioritizing supply chain risks that is capable of capturing the value at risk (VaR), the maximum loss expected at a given confidence level for a specified timeframe associated with risks within a network setting.
Design/methodology/approach
The proposed “Worst Expected Best” method is theoretically grounded in the framework of Bayesian Belief Networks (BBNs), which is considered an effective technique for modeling interdependency across uncertain variables. An algorithm is developed to operationalize the proposed method, which is demonstrated using a simulation model.
Findings
Point estimate-based methods used for aggregating the network expected loss for a given supply chain risk network are unable to project the realistic risk exposure associated with a supply chain. The proposed method helps in establishing the expected network-wide loss for a given confidence level. The vulnerability and resilience-based risk prioritization schemes for the model considered in this paper have a very weak correlation.
Originality/value
This paper introduces a new “Worst Expected Best” method to the literature on supply chain risk management that helps in assessing the probabilistic network expected VaR for a given supply chain risk network. Further, new risk metrics are proposed to prioritize risks relative to a specific VaR that reflects the decision-maker's risk appetite.
Keywords
Citation
Qazi, A. and Simsekler, M.C.E. (2022), "Worst Expected Best method for assessment of probabilistic network expected value at risk: application in supply chain risk management", International Journal of Quality & Reliability Management, Vol. 39 No. 1, pp. 155-175. https://doi.org/10.1108/IJQRM-07-2020-0238
Publisher
:Emerald Publishing Limited
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