Supply chain diagnostics with dynamic Bayesian networks

Han Ying Kao*, Chia Hui Huang, Han-Lin Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


This paper proposes a dynamic Bayesian network to represent the cause-and-effect relationships in an industrial supply chain. Based on the Quick Scan, a systematic data analysis and synthesis methodology developed by Naim, Childerhouse, Disney, and Towill (2002). [A supply chain diagnostic methodlogy: Determing the vector of change. Computers and Industrial Engineering, 43, 135-157], a dynamic Bayesian network is employed as a more descriptive mechanism to model the causal relationships in the supply chain. Dynamic Bayesian networks can be utilized as a knowledge base of the reasoning systems where the diagnostic tasks are conducted. We finally solve this reasoning problem with stochastic simulation.

Original languageEnglish
Pages (from-to)339-347
Number of pages9
JournalComputers and Industrial Engineering
Issue number2
StatePublished - 1 Sep 2005


  • Diagnostic reasoning
  • Dynamic Bayesian networks
  • Stochastic simulation
  • Supply chain diagnostics


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