CPFR流程下之銷售預測方法~混合預測模型

Abstract

協同規劃、預測與補貨(Collaborative Planning, Forecasting and Replenishment,CPFR)，在歐美經過一些企業的採用後已經有顯著的成效，目前國內已經有一些企業相繼採用或即將採用CPFR，期望能因此降低供應鏈作業成本及提升供應鏈作業績效，以提升企業競爭力。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的重要依據；但是多數的企業並沒有一個結構化、系統化的預測流程及方法，而是各部門透過簡單時間序列方法、天真預測法或人為經驗法則估算需求，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。本研究結合時間序列、多元回歸模型與基因演算法發展出一個CPFR流程下之三階段混合預測方法，以買賣方直接之銷售資料、銷售計畫等資訊進行以「週」為單位之個別商品銷售預測。同時本研究中，亦以國內某製造業公司與其顧客（一國際大型零售連鎖店通路商）之產品銷售資料進行方法的驗證；實驗顯示，本研究所提出之預測方法之預測結果較Jeong等人(2002)所提結合多元回歸模型與基因演算法之二階段預測系統之預測結果佳；亦較傳統使用普通最小平方法求解之一般統計回歸方法預測結果佳。

It has been verified in pilot projects by many European and American Corporations that Collaborative Planning, Forecasting and Replenishment (CPFR) can improve supply chain performance. Enterprises nowadays in Taiwan are implementing or going to implement CPFR, with hopes to reduce their supply chain operation cost, enhance logistic performance and increase their competition capability consequently. Under CPFR process and supply chain collaboration environment, a supply and demand both sides promised identical sales forecast with well forecasting performance for order decision making and cooperation is very important. Due to the dynamic complexities of both internal and external co-operate environment, many firms resort to qualitative, navie forecasting or other simple quantitative forecasting techniques and have many forecasts in their organization. However, these forecasting techniques lack the structure and extrapolation capability of quantitative forecasting models or without stable performance, while multi-forecasts providing different views of demand. Forecasting inaccuracies exist and typically lead to dramatic disturbances in sales order and production planning.\nThis paper presents a hybrid forecasting model for sales forecasting requirements in CPFR. A three stage model is proposed that integrate the time series model, regression model and use genetic algorithm to determine its coefficients efficiently. Direct sales information and related planned events in both collaborated sides is used for individual product’s “week” sales forecasting. To verify this model, we experiment on two different products and produce forecasts with datum from one manufacturer in Taiwan and its international retailer. The results shows that the hybrid sales forecasting model has better forecasting performance than not only the causal-genetic forecasting model proposed by Jeong et al. (2002), but also ordinary regression model with no genetic training process.