預測在供應鏈管理之中，屬於早期規劃的重要環節，預測結果的準確與否，會影響後端的物料規劃與排程計畫，甚至延伸至供應鏈末端零售商的補貨計畫。供應鏈中的預測，可概略分為屬於企業之間的協同預測與企業內部的需求預測。協同預測能同時結合供應端與需求端的預測優勢，其主要概念是由供應鏈夥伴協同分享資訊與風險，不再只是著重在供應鏈的其中一端，而是涵蓋所有成員之間的協調規劃、執行、監控，協同雙方的共識產生最後的預測能使得後續的排程與補貨能順利進行；需求預測則是著重在提昇產品預測的準確性，產品預測效果越好，越能針對此產品進行適當的需求規劃，對於協同預測更能有顯著的助益。因此若能提昇此兩種預測模式的準確性並結合成一套運作模式，不但能確實的滿足顧客需求，同時能避免整個供應鏈發生物料短缺或是存貨過多的情況。本研究分為三個部份進行預測效果改善，包括協同預測的改善，需求預測底下的多產品需求預測問題以及組合預測問題。
首先在協同供應鏈中，本研究建立一套以應用六標準差概念之協同預測的運作模式，運作結果可以讓供應鏈雙方能在數量化的基礎下得到最適當的結果，本模式並以實際流程與資料作為驗證。
接著在企業部分，為了提升需求預測的效益，研究首先針對多產品架構下，建立考量產品相關性之多產品擴散模式，由於此模式無法採用傳統的參數估計方式得到良好的求解品質，因此本研究提出了一種有效的混合式基因演算法應用於此模型的參數估計。
最後本研究運用協同預測的觀念，建立一套同時考量出貨端與全球需求端的協同需求預測模型，並以台灣TFT-LCD大廠出貨量與全球需求量的實際資料驗證，模型前端架構以需求預測模型為主,後端架構以組合預測為主，目前組合預測的方式仍然常以線性方式為主，但效果仍然具有加強的空間存在，模糊類神經網路屬於一種非線性的模型，放眼諸文獻，模糊類神經網路雖然已被廣泛的應用於各類組合最佳化問題且獲得十分良好的求解效率與品質，因此本研究將提出融合線性組合預測優點之整合模糊類神經組合預測模式套入所提出的協同需求預測模型之中，並與傳統的單一預測模式進行比較，結果顯示本協同預測模型無論在歷史資料的配適程度或是預測效果都比單一來源的需求預測模型優秀。
本研究以實際資料進行驗證所提出的三種預測模型，從求解的品質與連續的穩定性而言，融合六標準差的協同預測模型能持續且穩定的改善預測績效。從解題之效率與效果的觀點而言，本研究所提出的改良式多產品擴散模型搭配混合式基因演算法需求預測模型與融合模糊類神經網路概念之協同需求預測模型，不只在求解的速率十分迅速，而且得到最佳的配適度與預測準確性，同時亦可作為不同績效準則下相關預測問題研究之參考。

Forecasting is an important part of supply chain management. The forecasting accuracy will influence the material planning and the scheduling; even affect the replenishment plan of the retailer of the supply chain downstream. In this study, we focus on two issues which are the collaborative forecasting between enterprises and the demand forecasting. The collaborative forecasting concept, shares the information and risk between the supply chain partners, combines both advantage the seller and buyer. It no longer only focuses on one side of supply chain, but contains the coordination, planning, execution and monitoring among all members until collaboratively producing the final forecasting with of both agreement. The final forecasting could drive the replenishment plan successfully.
Demand forecasting emphasized on how to promote forecasting accuracy of the products. We could obtain a more suitable demand plan and a better collaborative forecasting due to the higher degree of accuracy. If we promote the accuracy of collaborative forecasting and demand forecasting, we can monitor the forecasting accuracy and avoid the shortage or too many stocks in the whole supply chain. To improve the accuracy of the forecasting, we consider three forecasting problems which include collaborative forecasting, multi-product forecasting and combining forecasting under the demand forecasting.
First, we applied Six Sigma methodology and proposed a continuous improvement model on different phases of collaborative planning, forecasting and replenishment (CPFR). A real case is used to demonstrate how to improve the performance of collaborative forecasts.
Second, in a multi-product framework, the traditional estimation methods could not get the satisfied results. We have conducted research using the hybrid genetic algorithm (GA) for an efficient parameter estimation method for multi-product forecasting.
Finally, we developed a demand forecasting methodology that combines market and shipment forecasts. We used the LCD monitor sales data to test and verify our methods. In the past studies, the linear ways were usually used to estimate the parameters of combining forecasts. Fuzzy neural network (FNN) is a nonlinear model and often used to find the best combination in past references. The applications of FNN to combining forecasting problems are extremely few. We developed an integrated fuzzy neural network model to find the best combining forecasting and compare with other traditional methods such as k method, adaptive set of weights and linear composite. Results show that the proposed model using integrated FNN can gain the superior forecasting efficiency and performance in the whole.

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