某些專門針對企業進行銷售之批發零售廠商，如金屬材料、五金、包材等廠商的銷售行為觀察發現，由於該廠商位處供應鏈中階，且下游顧客群大多分屬不同的終端產品供應鏈體系，往往會發生上、下游需求資訊不對稱的現象。在此種情況下，批發商無法事先針對產品進行統整性的需求預測，僅能利用過往的銷售資訊進行例行的備料作業。如何在缺貨、存貨成本下取得最適平衡，本研究採用混沌理論為基礎來整合多供應鏈系統之非線性需求變化，並結合相空間局部近似法與倒傳遞類神經網路來建構短期需求之預測模式。最後以個案公司實例資料進行效果驗證，其結果顯示本研究所提出之方法確較灰預測GM(1,1)與傳統類神經網路有更穩定與準確之預測效果。

Some wholesalers who focus on business-to-business selling are playing a role as the intermediators between different supply chains. Under such situation, however, the demand information the wholesalers can obtain is always asymmetrical. How to integrate the demands from different supply chains for the wholesalers to accurately prepare their stocks in advanced has become an important issue. This paper employs the Chaos theory to integrate the non-linear changes of theses demands; in addition, the phase space of local approximation and the back-propagation neural networks (BPN) are taken to build the forecasting model. In the experiment, this research took a real case as an example for validation. The results show that the proposed procedure outperforms the GM (1,1) of grey theorem and traditional BPN.

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