Worst Expected Best method for assessment of probabilistic network expected value at risk: application in supply chain risk management

PurposeThe 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/...

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Published in:The International journal of quality & reliability management Vol. 39; no. 1; pp. 155 - 175
Main Authors: Qazi, Abroon, Simsekler, Mecit Can Emre
Format: Journal Article
Language:English
Published: Bradford Emerald Publishing Limited 14.01.2022
Emerald Group Publishing Limited
Subjects:
Algorithms
Belief networks
Confidence intervals
Decision making
Expected utility
Expected values
Risk assessment
Risk exposure
Risk management
Simulation
Statistical analysis
Supply chains
Risk metrics
Value at risk
Risk appetite
Supply chain risks
Worst Expected Best method
Bayesian belief networks
ISSN:0265-671X, 1758-6682
Online Access:Get full text
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Abstract PurposeThe 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/approachThe 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.FindingsPoint 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/valueThis 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.
AbstractList PurposeThe 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/approachThe 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.FindingsPoint 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/valueThis 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.
Author Qazi, Abroon
Simsekler, Mecit Can Emre
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  givenname: Mecit Can Emre
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  surname: Simsekler
  fullname: Simsekler, Mecit Can Emre
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CitedBy_id crossref_primary_10_1080_16258312_2024_2399493
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Issue 1
Keywords Risk metrics
Value at risk
Risk appetite
Supply chain risks
Worst Expected Best method
Bayesian belief networks
Language English
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Snippet PurposeThe 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...
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StartPage 155
SubjectTerms Algorithms
Belief networks
Confidence intervals
Decision making
Expected utility
Expected values
Risk assessment
Risk exposure
Risk management
Simulation
Statistical analysis
Supply chains
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Title Worst Expected Best method for assessment of probabilistic network expected value at risk: application in supply chain risk management
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