Supply chain diagnostics with dynamic Bayesian networks

doi:10.1016/j.cie.2005.06.002

Supply chain diagnostics with dynamic Bayesian networks

2005-09-01

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DOI：10.1016/j.cie.2005.06.002

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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. (c) 2005 Elsevier Ltd. All rights reserved.

Keyword:

dynamic Bayesian networks

diagnostic reasoning

supply chain diagnostics

stochastic simulation

Supply chain diagnostics with dynamic Bayesian networks

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