Supply chain diagnostics with dynamic Bayesian networks

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.