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Yang L, Wang P, Zhao W, Wang C, Wu X, Faes MGR. On investigation of the Bayesian anomaly in multiple imprecise dependent information aggregation for system reliability evaluation. INT J INTELL SYST 2021. [DOI: 10.1002/int.22405] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Lechang Yang
- School of Mechanical Engineering, University of Science and Technology Beijing Beijing China
- Institute for Risk and Reliability, Leibniz Universität Hannover Hannover Germany
| | - Pidong Wang
- School of Mechanical Engineering, University of Science and Technology Beijing Beijing China
| | - Wenhua Zhao
- School of Mechanical Engineering, University of Science and Technology Beijing Beijing China
| | - Chenxing Wang
- School of Mechanical Engineering, University of Science and Technology Beijing Beijing China
| | - Xiuli Wu
- School of Mechanical Engineering, University of Science and Technology Beijing Beijing China
| | - Matthias G. R. Faes
- Institute for Risk and Reliability, Leibniz Universität Hannover Hannover Germany
- Department of Mechanical Engineering KU Leuven Leuven Belgium
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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 2021. [DOI: 10.1108/ijqrm-07-2020-0238] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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.
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Cobb BR, Li L. Bayesian network model for quality control with categorical attribute data. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105746] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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