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Title: CPFR銷售預測模式之探討
Authors: 曾永勝
Contributors: 林我聰
Keywords: 協同規劃、預測與再補貨
Collaborative Planning, Forecasting and Replenishment
Sales Forecasting
Mixed Forecasting Structure
Artificial Neural Network
Evolution Strategy
Date: 2005
Issue Date: 2009-09-14 09:18:30 (UTC+8)
Abstract: 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。
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.
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