協同規劃、預測與補貨﹙Collaborative Planning, Forecasting and Replenishment; CPFR﹚是協同商務中的一個應用實務，主要強調供應鏈上買賣雙方協同合作流程的概念，以提升供應鏈上流程的處理效率。未來企業的競爭將是產品背後整體供應鏈的激烈競爭，能對於不斷變化的市場需求作出有效預測，進而快速反應的企業將脫穎而出。對於庫存與補貨的掌控能力更將是企業決勝的關鍵因素之一。 CPFR 中的補貨模型是根據銷售預測、訂單預測、存貨策略與供給面資訊來做實際訂單，以作為補貨之用。補貨模式的準確性可以使賣方針對不同的需求來有效分配未來訂單預測的需求量，並降低安全庫存；買方則可根據訂單預測來調整庫存策略與採購數量。 現今廣用的供應商管理存貨（Vendor Managed Inventory, VMI）並沒有像CPFR加入更多的協同項目與精神，因此比較VMI與CPFR的補貨流程的差異性與優劣性，進而提供企業導入CPFR的補貨流程是相當重要的。 本研究以補貨階段為主題，除了探討協同補貨模式所需具備的屬性與輸入變數外，更將建構一個整合供應鏈上、下游協同資訊與符合協同訂單預測特性之預測模型，以提升補貨準確度，進而堆砌出整個CPFR 協同補貨模式，並加以與現今企業廣為採用的供應商管理存貨（Vendor Managed Inventory, VMI）的補貨模式進行比較，證明CPFR優於VMI，進而可供欲導入CPFR 流程下協同補貨模式或一般補貨模式的相關人員之參考。 / CPFR (Collaborative Planning, Forecasting, and Replenishment) is one of the applications of collaborative business. The stressed concept is the cooperation process of sellers and buyers on the supply chain in order to increase the handling efficiency. In the future, the industries would compete on the whole supply chains behind products—only the industry that is capable of making accurate predictions according to the constantly changing market and reacts immediately has the chance of winning. Being able to control the inventory and supply effectively would be one of the key factors leading to an industry’s success. The replenishment model of CPFR is to fill out the order according to the sales prediction, order prediction, inventory strategy, and supply information. The precision of the replenishment model could affect both suppliers and customers. The former can distribute products properly and meet the different demands from the upcoming orders so as to reduce inventory; the latter are able to revise the inventory strategy and amount of order according to the order prediction. A few research papers aimed at the replenishment model, though, most still focus on the management issues like the process framework of CPFR and the implementation benefit. Hence, establishing both an information system that coordinates customer demand with suppliers and a collaborative replenishment model that increases the accuracy of predictions is fairly important. The phase of replenishment, as the subject of this study, will approach on parameters the collaborative replenishment model needs to input and combine evolution strategies with tabu search to establish a replenishment model under the process of CPFR.

迴歸分析與類神經網路此兩種方法皆是預測領域上的主要工具。本論文嘗試在線性迴歸模式及非線性迴歸模式的條件下，隨機產生不同特性的資料以完整探討資料特性對迴歸分析與類神經網路之預測效果的影響。這些特性包括常態分配、偏態分配、不等變異、Michaelis-Menten關係模式及指數迴歸模式。 再者，我們使用區域搜尋法(local search methods)中的演化策略法(evolution strategies，ES)作為類神經網路的學習(learning)方法以提高其預測功能。我們稱這種類型的類神經網路為ESNN。 模擬結果顯示，ESNN確實可以取代常用來與迴歸分析做比較的倒傳遞類神經網路(back-propagation neural network，BPNN)，成為類神經網路的新選擇。針對不同特性的資料，我們建議：如果原始的資料適合以常態線性迴歸模式配適，則使用者可考慮使用迴歸方法做預測。如果原始的資料經由圖形分析或由檢定方法得知違反誤差項為均等變異之假設時，若能找到合適的權數，可使用加權最小平方法，但若權數難以決定時，則使用ESNN做預測。如果資料呈現韋伯偏態分佈時，可考慮使用ESNN或韋伯迴歸方法。資料適合以非線性迴歸模式做配適時，則選擇以ESNN做預測。 關鍵詞：迴歸分析，類神經網路，區域搜尋法，演化策略法類神經網路，倒傳遞類神經網路 / Both regression analysis and artificial neural networks are the main techniques for prediction. In this research, we tried to randomly generate different types of data, so as to completely explore the effect of data characteristics on the predictive performance of regression analysis and artificial neural networks. The data characteristics include normal distribution, skew distribution, unequal variances, Michaelis-Menten relationship model and exponential regression model. In addition, we used the evolution strategies, which is one of the local search methods for training artificial neural networks, to further improve its predictive performance. We name this type of artificial neural networks ESNN. Simulation studies indicate that ESNN could indeed replace BPNN to be the new choice of artificial neural networks. For different types of data, we commend that users can use regression analysis for their prediction if the original data is fit for linear regression model. When the residuals of the data are unequal variances, users can use weighted least squares if the optimal weights could be found. Otherwise, users can use ESNN. If the data is fit for weibull distribution, users can use ESNN or weibull regression. If the data is fit for nonlinear regression model, users can choose ESNN for the prediction. Keywords: Regression Analysis, Artificial Neural Networks, Local Search Methods, Evolution Strategies Neural Network (ESNN), Back-propagation Neural Network (BPNN)

協同規劃、預測與再補貨(Collaborative Planning, Forecasting and Replenishment; CPFR)，是目前供應鏈管理下重要的討論議題；台灣近年來由於加入WTO與製造業外移使競爭壓力加劇，全球運籌需求提升，使廠商間的合作更加密切，且近年來企業資訊環境與基礎建設逐漸成熟，有助於協同商務之發展。在CPFR流程與供應鏈協同作業環境下，一個供需雙方協同且績效良好的銷售預測具有關鍵的重要性，是管理決策與協同合作時的重要依據；但是多數的企業並沒有一個結構化、有系統化的預測流程及方法，進行多點且不同方法之預測，這樣的銷售預測較無穩定的品質，亦較難提供管理者合理的數據解釋。 在CPFR流程下，強調買賣雙方透過完整、即時資訊的交流，進行短期、單一銷售預測，以提供雙方後續訂單預測、訂單補貨等決策的依據。本研究利用演算法（類神經網路和演化策略法）找出更適合混合性預測架構的解釋變數，再以較適合於實數解之演化策略法於修改黃蘭禎（2004）的三階段之預測模型架構，最後採用實驗方法，進行模型績效驗證。 / Collaborative Planning, forecasting and replenishment (CPFR) is an important issue of supply chain management currently. Because of the severer competition resulted from entrance into WTO and industry integration, cooperation between Taiwanese companies becomes more intensely; enterprises’ information environment and foundation construction attain to maturity also boost the development of collaboration business. In CPRF process and supply chain operation environment, it is critical that a good performance sale forecasting collaborated by both supplier and buyer sides, and it is also the basis of policy decision and collaboration. However, the majority of the companies lack for a structural and systematical forecasting process to proceed with a multi-points forecasting with different methods. This kind of sale forecasting is less of stable quality and is harder to provide the managers a reasonable statistics explanation. Under the CPRF process, both buyers and sellers are able to obtain the short-term and single sale forecasting by real time information communication. Furthermore, the follow-up order forecasting and replenishment strategy decision can be also established through this process. This research finds the variables that are more suitable to the mixed structure by usage of the algorithms, ANN and Evolution Strategy. And this research uses Evolution Strategy that is more suitable to real question to improve the mixed structure of Huang (2004). In the end, experimentation is adopted in order to verify the performance of the model.