Bayesian network modelling for supply chain risk propagation

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

Research output: Contribution to journalArticlepeer-review

188 Citations (Scopus)
385 Downloads (Pure)


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 languageEnglish
Pages (from-to)5795-5819
Number of pages25
JournalInternational Journal of Production Research
Issue number17
Early online date4 May 2018
Publication statusPublished - 2 Sept 2018


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


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