Bayesian Analysis of Supply Chain Diagnostics using Dynamic Networks

摘要：

The supply chain is the central organizing unit in today’s global industries and has gained significance as one of the 21st century manufacturing paradigms for improving organizational competitiveness. In this paper, we propose 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, 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.

关键词： Supply chain diagnostics Bayesian analysis Dynamic networks Posterior probability

作者: Hui-ming ZHU Li-ya HAO

作者单位: College of Statistics,Hunan University,Changsha,410079,China

会议类型: 国际会议

会议名称: 工业工程与系统管理2007年国际会议(International Conference on Industrial Engineering and Systems Management)(IESM 2007)

会议地点: 北京

会议语种:英文

在线出版日期: 2007-05-30（万方平台首次上网日期，不代表论文的发表时间）