促銷檔期下之銷售預測流程設計與模式構建

Abstract

在當今世界激烈的市場競爭和快速多變的市場需求下，企業面臨的經營壓力已非本身單一方面就可以解決，必須藉由整體供應鏈成員彼此的溝通、合作，以供應鏈管理之手法提升競爭優勢。但目前發展出來的供應鏈管理手法中常忽視供應鏈資訊流的最佳化，為供應鏈帶來「長鞭效應」(Long-Whip Effect) 的問題，需求訊息的不真實性造成供應鏈所有成員受高庫存、高缺貨率之影響，進而帶來營業額下降。而此問題尤其容易發生在促銷檔期期間。由於商品銷售量在促銷檔期中會突然的升高，若供應鏈成員之間沒有良好的溝通管道，則更為容易產生長鞭效應。在許多供應鏈管理方法中，協同計畫、預測與補貨(Collaborative Planning, Forecasting and Replenishment, CPFR)被認為是最能夠解決長鞭效應的手法。 本研究藉由台灣某大型供應商與通路商之合作為實例，企圖達成以下三點目標，給予雙方建議並為雙方帶來更大利益：第一，以CPFR之手法作為基礎，重新設計原先該供應商與通路商於促銷檔期間預測與補貨模式，使得雙方能夠更快速應對促銷檔期間的需求。第二，本研究將該供應商以及通路商所蒐集之銷售及檔期資訊，以複迴歸模型探討該供應商提供給該通路商之產品中其中57隻產品的關鍵銷售因素；而研究也證實即使為相同品牌，不同的產品的銷售量也會被不盡相同的促銷方法影響。第三，本研究採用複迴歸模型以及以人工智慧為基礎之自組性演算法模型進行銷售量預測，並藉由預測誤差了解在何種促銷方案下會有較好的準確率，而研究也證明自組性演算法之預測準確率較高。而採用本研究方案之通路商分店，在缺貨率、存貨減少及訂單滿足率等指標上皆優於其餘未採用此方案之分店，也證實本研究之銷售預測流程與預測模型具有其實務上的效果。

In recent year, due to the strong competition and volatility of the market, firms must engage and cooperate with all the members in supply chain to sustain their competitive advantage by using supply chain management method. However, most of the methods have ignored the importance in optimization of information flow, which caused a serious impact to the whole supply chain, the “Long-Whip Effect”. The Long-whip effect indicates that the uncertainty and variation of demand information in supply chain will cause high inventory and high out-of-stock. This situation happens in promotion period often especially. For the demand will drive up in a very short period of time, long-whip effect happened if the supply chain cannot communicate to each other easily. Among all the supply chain management methods, collaborative planning, forecasting and replenishment (CPFR) is considered as the most effective way to deal with long-whip effect problem in supply chain. This study takes the engagement of a large supplier and a retailer in Taiwan as example, and aims to handle below three issues. First, this study re-designed the process in promotion period base on the basis of CPFR. Second, this study used multiple regression to discuss the key promotion factors that will affect sales quantity for 57 SKUs (stock keeping unit) that the supplier provided to the channel. Third, this study uses both multiple regression and Group Method Data Handling (GMDH) to predict sales quantity for each promotion period. With the total solution designed, the result has shown that the retailer has a better performance on out-of-stock rate, inventory reduction rate and order fulfillment rate that both side are eager to reach.