在半導體代工廠，景氣程度以及每個客戶生產策略的變動經常直接影響客戶訂單的數量，也造成半導體廠需求的變動。因此利用系統化的方法對於客戶行為模式建立一個的預測系統，並且幫助決策者在不確定的情況下計畫生產策略十分重要。當每一批貨完成的時候，有很多原因會造成製成品的延遲交貨。當應該交出去的製成品被存放在廠內的成品庫時，存貨上升，儲存成本提高，存貨週轉率也會相對的降低，這類的的存貨在本研究稱為半導體供應商管理庫存。供應鏈的運作效率是公司的經營績效中很重要的一項關鍵因素，在供應鏈管理中，常以績效指標的方式來做定時的評估以評估內部的供應鏈運作效率，而存貨週轉率是用來了解供應鏈的運作效率重要指標。
本論文的研究目的是發展一個資料挖礦的研究架構，其中包含類神經網路演算法以及利用分佈時滯方程的模式結構來分析客戶所需的製成品、訂單以及其他可能影響的要素之間的關係。從這個方法架構所萃取的規則能有效幫助決策者在不確定的訂單情形下作及時的生產決策，藉此可以維持良好的產能利用率以及控制生產週期。並以某半導體廠的實際資料作為實證 。
本文除了回應相關研究，並請教領域專家選出可能影響製成品進成品庫的關鍵性指標做為預測因子。在初步的研究，我們過濾不重要的指標並且將焦點放在影響較為顯著的預測因子中。
利用這個研究架構，我們能找出是哪些因子造成客戶比較容易將製成品存放在成品庫中而不將貨品拿走。利用取得的客戶行為訊息，決策者能透過這個決策支援系統獲得更多的資訊，並且能在不確定的客戶行為下，有效地控制產能並且最佳化產量分配，因此能降低材料費用和存貨成本。這些萃取出的規則也能幫助決策者追蹤出是哪些關鍵性指標發生了問題，因而造成成本的浪費。本論文的實證研究結果顯示此架構的實用性以及有效性。

In semiconductor foundry, the variation of prosperity and the production strategy of various customers often affect the total quantity of customer order directly, thus create variability on fab input on production. It is critical to develop a systematic methodology to predict customer behaviors and help the decision maker to plan production strategy for the fabrication in the light of uncertainty. When the lots were finished in fab, there are many causes that would let the finished goods delay for customers. Thus the finished goods are stored in fab’s ware house. This increases the fab’s inventory and decrease the inventory turnover ratio. This kind of inventory is called the semiconductor vendor managed inventory in this thesis. The operation efficiency in supply chain is a key factor of enterprise. In supply chain management, it is very common to evaluate the internal efficiency of supply chain operation with key performance index. The inventory turnover ratio is one of the key performance indexes to understand the efficiency of supply chain operation.
This research aims to develop a data mining framework and use neural network algorithms and distributed lags structure to analyze the relationships among customer finished goods, order, and other factors. We also conduct an empirical study for validation. The derived empirical rules from this approach can effectively help the decision maker in fab to make timely production decisions given different order situations while maintaining good fab utilization and controlling cycle time.
Studying addition to literature review, this thesis consults the domain experts of one company to extract key indexes as the predictors of vendor managed inventory. Based on preliminary study, this study filters the unimportant indexes and focuses the critical ones to adopt the framework including utilization, customers hold stocks, SEMI B/B, etc.
By applying this framework, the rules can be extracted to explain why and how customers queue the lots in the fabrication or find other causes that make the finished lots keep in fab. With knowing the information of customer behavior, the decision maker can have more advice by this decision support system. Based on the derived rules, the planning manager can effectively control the quantity of productions and optimize production allocation decisions in the light of involved uncertainty of customer behaviors and thus be able to reduce the waste of material and cost of inventory. These rules can also help the manager to track what goes wrong with these predict variables (i.e. key indices). The empirical study showed the practical viability of this approach and thus also would be able to help this company to better serve her customers.

Achabal, D. D., S. H. Mcintyre, S. A. Smith and K. Kalyyanam (2000), "A Decision Support System for Vendor Managed Inventory", Journal of Retailing, 76(4), 430-454.
Ben-Bassat, M. (1982), "Pattern recognition and reduction of dimensionality", In Krishnaiah, P. R. and L. N. Kanal, editors, Handbook of statistics-II, 773-791, North Holland.
Berry, M. and G. Linoff (1997), Data Mining Techniques for Marketing, Sales and Customer Support, John Wiley and Sons, New York.
Bezdek, J. (1983), "Some recent application of fuzzy c-means in pattern recognition and image processing", IEEE 1983 workshop on Languages for Automation, IEEE computer society, 247-252.
Box, G. E. P., W. G. Hunter and J. S. Hunter (1978), Statistics for Experimenters, John Wiley and Sons, New York.
Breiman, L. (1996), "Bagging Predictors", Machine Learning, 26, 123-140.
Breiman, L. (2001), "Random Forests", Technical Report, University of California, Berkeley.
Brown, E. C., et al. (1963), Stabilization Policies (prepared for the Commission of Money and Credit), Prentice-Hall, Inc., Englewood Cliffs, N. J.
Chen, M. C., A. L. Chiu, and H. H. Chang (2005), "Mining changes in customer behavior in retail marketing", Expert Systems with Applications, 28, 773-781.
Chien, C. F., T. H. Lin, C. Y. Peng, and S. C. Hsu (2001), "Developing data mining framework and mehtods for diagnosing semiconductor manufacturing defects and an empirical study of wafer acceptance test data in a wafer fab", Journal of the Chinese Institute of Industrial Engineering, 18(4), 37-48.
Chien, C. F., W. C. Wang, and J. C. Cheng (2007), “Data mining for yield enhancement in semiconductor manufacturing and an empirical study,” Expert Systems With Applications, 33(1), 1-7.
Cox Jr., L. A. and D. A. Popken (2002), “A hybrid system-identification method for forecasting telecommunications product demands”, International Journal of Forecasting, 18(4), 647-671.
Daugherty, P.J., M.B. Myers, and C.W. Autry (1999), "Automatic replenishment programs: an empirical examination", Journal of Business Logistics, 20(2), 63-82.
Dayhoff, J. E. (1990), Neural networks architectures: an introduction, Van Nostrand Reinhold, New
York.
Dhrymes, P. J. (1981), Distributed lags: problems of estimation and formulation, North Holland.
Disney, S. M. and D. R. Towill (2002), "A procedure for the optimization of the dynamic response of a Vendor Managed Inventory system", Computer and Industrial Engineering, 43, 27-58.
Disney, S. M. and D. R. Towill (2003), "The effect of vendor managed inventory (VMI) dynamics on the Bullwhip Effect in supply chains", International Journal of Production Economics, 85, 199-215.
Fayyad, U. (1997), "Data mining and knowledge discovery in database: implication for scientific database", Proceedings of Ninth International Workshop on Scientific and Statistical Database Management, 2-11.
Fayyad, U., G. Piatetsky-Shapiro, and P. Smyth (1996), "From data mining to knowledge discovery: An overview", in Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, S. P. Amith, and R. Uthurusamy (Eds.), Cambridge, MA, MIT Press, 1-36
Fayyad, U., G. Piatetsky-Shapiro, and P. Smyth (1996), "The KDD process for extracting useful knowledge from volumes of Data", Communication of ACM, 39(11), 27-34
Friedman, J.H. (1999), "Stochastic Gradient Boosting", Technical Report, Stanford University.
Fu, Y. (1997), "Data mining", IEEE Potentials, 164, 18-20.
Han, J. and M. Kamber (2001), Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers.
Hand, D., H. Manila and P. Smyth (2001), Principles of Data Mining, MIT Press.
Hartigan, J. A. and M. A. Wong (1979), "Algorithm AS136: a k-means clustering algorithm", Applied Statistics, 28, 100-108.
Hastie, T., R. Tibshirani and J. Friedman (2001), The Elements of Statistical Learning, Springer, New York.
Jorgenson, D. W. (1965), "Anticipations and Investment Behavior", in The Bookings Quarterly Econometric Model of the United States, J. S. Duesenberry, G. Fromm, L. R. Klein and E. Kuh (eds), Rand McNally & Company, Chicago, 35-94.
Kleissner, C. (1998), "Data Mining for the enterprise", IEEE Proceedings of 31st Annual Hawaii International Conference on System Science, 7, 295-304.
Kohonen, T. (1981), "Automatic formation of topological maps of patterns in a self-organizing system", Proceedings of the 2nd Scandinavian Conference on Image Analysis, 214-220, Espoo, Finland
Kohonen, T. (1990), "The self-organizing map", Proc. IEEE, 78(9), 1464-1480.
Koyck, L. M. (1954), Distributed Lags and Investment Analysis, North-Holland Publishing Company, Amsterdam.
Kuk, G. (2004), "Effectiveness of vendor-managed inventory in the electronics
industry: determinants and outcomes", Information and Management, 41, 645-654.
Lee, C. C. and W. H. J. Chu (2005), "Who should control inventory in a supply chain?", European Journal of Operational Research, 164, 158-172.
Liu, H. and H. Motoda (1998), Feature Extraction, Construction and Selection: A Data Mining Perspective, Kluwer Academic Publishers.
Luxhoj, T. T., J. O. Riis, and B. Stensballe (1996), "A hybrid econometric-neural network modeling approach for sales forecasting", International Journal of Production Economics, 43, 175-192.
Mitchell, T. (1997), Machine Learning, McGraw-Hill.
Myers, M.B., P.J. Daugherty, C.W. Autry (2000), "The Effectiveness of automatic inventory replenishment in supply chain operations: antecedents and outcomes", Journal of Retailing, 76(4), 455-481.
Nerlove, M. (1958), Distributed Lags and Demand Analysis for Agricultural and Other Commodities, U. S. Department of Agriculture, Agricultural Marketing Service, Agricultural Handbook No. 141, Washinton.
Packianather, M. S. , P. R. Drake and H. Rowland (2000), "Optimizing the parameters of multilayered feedforward neural networks through Taguchi design of experiments", Qual. Reliab. Engng. Int., 16, 461-473.
Rosenblatt, F (1957), The Perceptron: a Perceiving and Recognizing Automation, Cornell Aeronautical Lab Rep 85-460-1.
Rumelhart, D. E., G. E. Hinton and R. J. Williams (1985), "Learning internal representation by error propagation", Parallel Distributed Proceeding, 1, 318-362.
Rygielski, C., J. C. Wang and D. C. Yen (2002), “Data mining techniques for customer relationship management”, Technology in Society, 24(4), 483-502.
Sethi, I. K. and J. H. Yoo (1997), "Structure-driven induction of decision tree classifiers through neural learning", Pattern Recognition, 30(11), 1893-1904.
Subhash, S. (1996), Applied Multivariate Techniques, John Wiley, New York.
Taguchi, G., E. A. Elsayed and T. C. Hsiang (1989), Quality Engineering in Production Systems, McGraw-Hill Book company.
Thiesing, F. M. and O. Vornberger (1997), "Sales Forecasting Using Neural Networks", Thiesing, F.M.; Vornberger, O., IEEE International Conference on Neural Networks, 4, 9-12.
Ward, J. H. (1963), "Hierachical Grouping to Optimize an Objective Function", Journal of the American Statistics Association, 58, 236-244.
Wasserman, L. (2004), All of Statistics, Springer-Verlag, New York.