TY - JOUR
T1 - Supply chain diagnostics with dynamic Bayesian networks
AU - Kao, Han Ying
AU - Huang, Chia Hui
AU - Li, Han-Lin
PY - 2005/9
Y1 - 2005/9
N2 - 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.
AB - 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.
KW - Diagnostic reasoning
KW - Dynamic Bayesian networks
KW - Stochastic simulation
KW - Supply chain diagnostics
UR - http://www.scopus.com/inward/record.url?scp=24344489872&partnerID=8YFLogxK
U2 - 10.1016/j.cie.2005.06.002
DO - 10.1016/j.cie.2005.06.002
M3 - Article
AN - SCOPUS:24344489872
SN - 0360-8352
VL - 49
SP - 339
EP - 347
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
IS - 2
ER -