個人或企業都處在資料過多但資訊過少的情境中，如何在眾多的資料中擷取可供決策出有用的資訊顯得非常重要。將資料轉換成資訊的方式有很多，資料採礦（Data Mining）對於從大量資料中擷取出可供利用的資訊提供了有效率的解決方式。
真實世界裡的產業活動行為可經由模擬方式來進行研究。模擬為一種有效而可行之分析方法。本研究透過供應鏈模擬程式－啤酒賽局（Beer Game），對單一供應鏈體系進行模擬，取得決策行為資料。再以Data Mining方法中的決策樹（Decision Tree），對資料進行分析，產生有用之管理資訊，用以分析Beer Game供應鏈決策行為。本研究也探討不同資訊淬取方式所得到的決策樹進行分析比較其有效性。本研究可提供決策者結構化之決策資訊，可提高決策品質，藉以改善存在於供應鏈中的長鞭效應，降低整個供應鏈的成本。

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.

中文部份
Berry, M. J. and Linoff, G. S. 著/ 彭文正 譯，「Data Mining資料採礦：顧客關係管理暨電子行銷之應用」，維科出版社，民國90年。
王立志，「系統化運籌與供應鏈管理」，滄海書局，民國88年。
蘇木春，張孝德，「機器學習：類神經網路、模糊系統以及基因演算法則」，全華科技，民國88年。
英文部份
Chen, F., “Decentralized supply chains subject to information delays,” Management Science, 45, 1076-1090, 1999.
Chen, F. and Samroengraja, R., “The stationary beer game,” Production and Operations Management, 9, 19-30, 2000.
Ford, D. N., “System dynamics as a strategy for learning to learn,” Learning for the 21st Century Conference, Andersen Worldwide Center for Professional Education, St. Charles, Illinois, 1998.
Goodwin, J. S. and Franklin, S. G., “The beer distribution game,” Journal of Management Development, 13, 7-15, 1994.
Johnson, M. E. and Pyke, D. F., Supply Chain Management, Research Report, School of Business, Dartmouth College, 1999.
Kazaz, B. and Moskowitz, H., “An active learning exercise: the match distribution game,” Research Report, School of Business, Loyola University, Chicago, 1998.
Kimbrough, S. O., Wu, D. J. and Zhong, F., “Computers play the beer game: can artificial agents manage supply chains?,” IEEE Int Conf On System Sciences, 34, 1-10, 2001.
Lee, H. and Wang, S., “Decentralized multi echelon supply chains: incentives and information,” Management Science, 45, 633-639, 1999.
Lee, H. L., Padmanabhan, V. and Wang, S., “Information distortion in a supply chain: the bullwhip effect,” Management Science, 43, 546-558, 1997.
Machacha, L. L. and Bhattacharya, P., “A fuzzy logic based approach to project selection,” IEEE Transactions on Engineering Management, 47, 65-73, 2000.
Machuca, J. A. and Barajas, R. P., “A computerized network version of the beer game via the internet,” System Dynamics Review, 13, 323-340, 1997.
Metters, R., “Quantifying the bullwhip effect in supply chains,” Journal of Operations Management, 15, 89-100, 1997.
Sikora, R. and Shaw, M. J., “A multi agent framework for the coordination and integration of information systems,” Management Science, 44, S65-S78, 1998.
Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E., Designing And Managing The Supply Chain, McGraw-Hill International Edition., Singapore, 2000.
Sterman, J. D., “Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiment,” Management Sciences, 35, 321-339, 1989.
Witten, I. H. and Frank, E., Data Mining, Morgan Kaufmann Publishers., San Francisco, 2000.
XpertRule Miner, Attar Software Limited, Lancashire, UK, 1999.