隨著網路與資訊科技的進步，促使供應鏈及其管理技術的持續演進，許多資訊共享策略也因應而生，如電子銷售點、供應商管理存貨、電子購物和緊急運送等。但環境的變化會產生不確定性，不同的績效指標在不確定的環境下受到的影響程度也往往不同，因此當企業在選擇供應鏈資訊共享策略時增加了決策的困難。而一個有效率的供應鏈策略，不僅要能夠減少成本、提高顧客服務水準，更需在不確定的環境中仍能維持穩健的特性，以確保企業的營運績效與競爭力。本研究利用啤酒遊戲，以田口複合雜音因子（Compounding noise factors）方法建構出不確定情境，藉由模擬方式探討不同的供應鏈資訊共享策略在不確定環境中的績效表現，再以訊號雜音比（Signal-to-Noise ratio，簡稱SN比）作為各個準則穩健特性的衡量指標，並結合多準則決策方法在多個準則間做一整體評估。如此希望提供決策者一套有系統且有效率的穩健供應鏈資訊共享策略評估流程，以期改善決策品質，降低供應鏈成本與決策風險。而研究的結果指出，以電子銷售點同時結合緊急運送和移除配銷商的供應鏈資訊共享策略在不確定環境中有較佳的穩健特性。

The advance of internet and information technology has prompted the development of supply chain and related management techniques. Many information sharing strategies have been created, such as electronic point of sales, vendor manage inventory, e-shopping, emergency transportation and so on. The variation of environment will produce uncertainty and the levels of different performance criteria which are affected by different environments are distinct. As a result, it will increase decision difficulties when enterprises choose supply chain information sharing strategies. An effective and efficient supply chain strategy should not only have the ability to reduce cost, raise customer service level, but also maintain the robustness characteristic under uncertain environments to make sure the business operation efficiency and competitivity. Our research constructs uncertain scenarios by compounding noise factors which was proposed by Taguchi. And we observe the performance of different supply chain strategies under different uncertain environments by simulation of the Beer Game. Then we calculate Signal-to-Noise ratio of each criteria as the robustness performance index and make an overall evaluation among each criteria by multiple criteria decision making methods. To improve decision quality, reduce supply chain costs, and decision risks, we provide a systematic and efficient evaluation process of robust supply chain information strategies for decision makers. And the result of this research shows that combines electronic point of sales with emergency transportation and reduced supply chain strategy to be an integrated strategy will have better robustness characteristic under uncertain environments.

參考文獻
李輝煌，民89，田口方法：品質設計的原理與實務，高立圖書有限公司。
溫坤禮，黃宜農，張偉哲，張廷政，尤美利，賴嘉瑞，民92，灰關聯模型方法與應用，高立圖書有限公司。
鍾清章，民83，品質工程，中華民國品質學會。
簡禎富，民94，決策分析與管理：全面決策品質提升之架構與方法，雙葉書廊。
蘇朝墩，民92，品質工程，中華民國品質學會。
Ahn, H.J., Lee, H., and Park, S.J., 2003, A flexible agent system for change adaptation in supply chains, Expert Systems with Applications, 25, 603-618.
Belavendram, N., 1995, Quality by Design: Taguchi Techniques for Industrial Experimentation, Prentice Hall, Englewood Cliffs, New Jersey.
Bendell, A., Disney, J., and Pridmore, W.A., 1989, Taguchi Methods: Applications in World Industry, IFS Publications, UK.
Deng, J.L., 1982, Control problems of grey systems, System and Control Letters, 1, 288-294.
Disney, S.M. and Towill, D.R., 2002, A robust and stable analytical solution to the production and inventory control problem via a z-transfrom approach, Proceedings of the 12th International Working Conference on Production Economics, 18-22 February, Igls, Austria.
Disney, S.M., 2001, The production and inventory control problem in vendor managed inventory supply chains, PhD thesis, Cardiff Business School, Cardiff University, Cardiff.
Disney, S.M., Naim, M.M., and Potter, A., 2004, Assessing the impact of e-business on supply chain dynamics, International Journal of Production Economics, 89, 109-118.
Disney, S.M., Naim, M.M., and Towill, D.R., 1997, Dynamic simulation modeling for lean logistics, International Journal of Physical Distribution and Logistics Management, 27, 174-196.
Ealey, L.A., 1988, Quality by Design: Taguchi Methods and U.S. Industry, American Supplier Institute, Dearborn, Michigan.
Edwards, W., 1977, How to use multi-attribute utility measurement for social decision making, IEEE Transactions on Systems, Man, and Cybernetics, 326-340.
Fiala, P., 2005, Information sharing in supply chains, Omega, 33, 419-423.
Fowlkes, W.Y. and Creveling, C.M., 1995, Engineering Methods for Robust Product Design, Addison-Wesley Publishing Company, Reading.
Greco, S., Matarazzo, B., and Slowinski, R., 2002, Rough set methodology for sorting problems in presence of multiple attributes and criteria, European Journal of Operational Research, 138, 247-259.
Hong-Minh, S.M., Disney, S.M., and Naim, M.M, 2000, The dynamics of emergency transhipment supply chains, International Journal of Physical Distribution and Logistics Management, 30, 788-815.
Hwang, C.L. and Yoon, K.P., 1981, Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, New York.
John, S., Naim, M.M., and Towill, D.R., 1994, Dynamic analysis of a WIP compensated decision support system, International Journal of Manufacturing Systems Design, 1, 283-297.
Kelton, W.D., Sadowki, R.P., and Sadowki, D.A., 2007, Simulation with Arena, 4th edition, McGraw-Hill, New York.
Kimbrough, S.O., Wu, D.J., and Zhong F., 2002, Computers play the beer game: can artificial agents manage supply chain?, Decision Support System, 33, 323-333.
Kouvelis, P. and Yu, G., 1996, Robust Discrete Optimization and Its Applications, Kluwer Academic Publishers, Netherlands.
Liao, H.C., 2003, Using PCR-TOPSIS to optimize Taguchi’s multi-response problem, International Journal of Advanced Manufacturing Technology, 22, 649-655.
Machuca, A.D. and Barajas, R.P., 2004, The impact of electronic data interchange on reducing bullwhip effect and supply chain inventory costs, Transportation Research Part E, 40, 209-228.
Naim, M.M. and Towill, D.R., 1995, What’s in the pipeline, 2nd International Symposium on Logistics, July 11-12, 135-142.
Peace, G.S., 1993, Taguchi Methods: a Hands-on Approach to Quality Engineering, Addison-Wesley Publishing Company, Massachusetts.
Simchi-Levi, D., Kaminsky, P., and Simchi-Levi, E., 2003, Designing and Managing the Supply Chain, 2nd edition, McGraw-Hill, New York.
Sterman, J., 1989, Modelling managerial behaviour: misperceptions of feedback in a dynamic decision making experiment, Management Science, 35, 321-339.
Stevenson, W.J., 2002, Operations Management, 7th edition, McGraw-Hill, New York.
Taguchi, G., 1992, Taguchi Methods: Research and Development, American Supplier Institute, Dearborn, Michigan.
Tang, C.S., 2006, Perspectives in supply chain risk management, International Journal of Production Economics, 103, 451-488.
Tong, L.I., Wang, C.H., Chen, C.C., and Chen, C.T., 2004, Dynamic multiple responses by ideal solution analysis, European Journal of Operational Research, 156, 433-444.
Wang, C.H. and Tong, L.I., 2005, Optimization of dynamic multi-response using grey multilpe attribute decision making, Quality Engineering, 17, 1-9.
Wikner, J., Towill, D.R., and Naim, M.M., 1991, Smoothing supply chain dynamics, International Journal of Production Economics, 22, 231-248.
Wu, Y. and Wu, A., 1995, Taguchi Methods for Robust Design, The American Society of Mechanical Engineers, New York.
Yang, T. and Chou, P., 2005, Solving a multi-response simulation-optimization problem with discrete variables using a multiple-attribute decision-making method, Mathematics and Computers in Simulation, 68, 9-21.