Bayesian network modelling for supply chain risk propagation

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

Research output: Contribution to journalArticle

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.
LanguageEnglish
JournalInternational Journal of Production Research
Early online date4 May 2018
DOIs
Publication statusE-pub ahead of print - 4 May 2018

Fingerprint

Modeling
Propagation
Supply chain risk
Bayesian networks
Disruption
Supply chain network
Node
Lost sales
Service levels
Inventory cost
Adaptability
Global supply chain
Risk exposure
Network theory
Supply risk management
Ripple effect
Resilience
Vulnerability
Multi-echelon
Fragility

Keywords

  • risk propagation
  • Risk analysis
  • Bayesian inference
  • Supply Chain Risk Management

Cite this

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title = "Bayesian network modelling for supply chain risk propagation",
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.",
keywords = "risk propagation , Risk analysis, Bayesian inference, Supply Chain Risk Management",
author = "Ritesh Ojha and Abhijeet Ghadge and Tiwari, {Manoj Kumar} and Bititci, {Umit Sezer}",
year = "2018",
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day = "4",
doi = "10.1080/00207543.2018.1467059",
language = "English",
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publisher = "Taylor & Francis",

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Bayesian network modelling for supply chain risk propagation. / Ojha, Ritesh; Ghadge, Abhijeet; Tiwari, Manoj Kumar; Bititci, Umit Sezer.

In: International Journal of Production Research, 04.05.2018.

Research output: Contribution to journalArticle

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AU - Ghadge, Abhijeet

AU - Tiwari, Manoj Kumar

AU - Bititci, Umit Sezer

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KW - Bayesian inference

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