Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Decision Support Systems and Intelligent Systems
Decision Support Systems and Intelligent Systems
A supply chain diagnostic methodology: determining the vector of change
Computers and Industrial Engineering - Supply chain management
Expert Systems with Applications: An International Journal
International Journal of Computer Applications in Technology
Combining Bayesian Networks and Total Cost of Ownership method for supplier selection analysis
Computers and Industrial Engineering
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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.