An approach to analyzing and modeling decision behavior in supply chain

• Main Authors: Tsung-Sheng Chen, 陳宗聲
• Other Authors: Chih-Sen Wu
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/06317592720735000745

碩士 === 國立成功大學 === 資訊管理研究所 === 90 === Both individuals and enterprises are all in a data rich and information explosion environment. There are many methods can transform data into information. Data mining is one of the most effective ways in capturing useful information from a huge amount of data. The behavior of business activities can be analyzed through simulation. Simulation has been proven an effective analysis tool. In this study, we capture data of decision behavior in a supply chain by using a famous simulation program, beer game, which is developed in MIT. The gated data was transformed into decision knowledge. Decision knowledge was extracted and represented in decision trees through data mining techniques. Several trees were induced by different schemes of data arrangement. The performs of decision based on distinct decision trees are compared in terms of error rates. The study chows that knowledge generated directly from transaction data can be used to import quality of decision and hence reduce bullwhip effect in supply chain.