Bayesian network modelling for supply chain risk propagation

Ritesh Ojha; Abhijeet Ghadge; Manoj Kumar Tiwari; Umit Sezer Bititci

doi:10.1080/00207543.2018.1467059

Bayesian network modelling for supply chain risk propagation

Ritesh Ojha, Abhijeet Ghadge, Manoj Kumar Tiwari, Umit Sezer Bititci

School of Social Sciences

Research output: Contribution to journal › Article › peer-review

193 Citations (Scopus)

461 Downloads (Pure)

Abstract

Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk propagation. Bayesian network theory is used to analyse the multi-echelon network faced with simultaneous disruptions. The ripple effect of node disruption is evaluated using metrics like fragility, service level, inventory cost and lost sales. Developed risk exposure and resilience indices support in assessing the vulnerability and adaptability of each node in the supply chain network. The research provides a holistic measurement approach for predicting the complex behaviour of risk propagation for improved supply chain risk management.

Original language	English
Pages (from-to)	5795-5819
Number of pages	25
Journal	International Journal of Production Research
Volume	56
Issue number	17
Early online date	4 May 2018
DOIs	https://doi.org/10.1080/00207543.2018.1467059
Publication status	Published - 2 Sept 2018

Keywords

risk propagation
Risk analysis
Bayesian inference
Supply Chain Risk Management

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This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Researchon 10/04/2018 available online: https://doi.org/10.1080/00207543.2018.1467059
Accepted author manuscript, 793 KBLicence: All Rights Reserved

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abstract = "Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk propagation. Bayesian network theory is used to analyse the multi-echelon network faced with simultaneous disruptions. The ripple effect of node disruption is evaluated using metrics like fragility, service level, inventory cost and lost sales. Developed risk exposure and resilience indices support in assessing the vulnerability and adaptability of each node in the supply chain network. The research provides a holistic measurement approach for predicting the complex behaviour of risk propagation for improved supply chain risk management. ",

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AU - Bititci, Umit Sezer

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Bayesian network modelling for supply chain risk propagation

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Keywords

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