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題名：決策支援為基底之資訊產品通路業銷售預測
作者：連甲玫
作者(外文)：Claire Lian
校院名稱：輔仁大學
系所名稱：商學研究所
指導教授：李天行 博士
呂奇傑 博士
學位類別：博士
出版日期：2012
主題關鍵詞：資訊產品通路；銷售預測；多元適應性雲形迴歸；支援向量迴歸；極限學習機；information technology product channel；sales forecasting；support vector machine；multivariate adaptive regression splines；extreme learning machine
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銷售預測對資訊產品通路業(Information Technology Product Channel)而言是一個重要且困難的議題。重要是因為銷售預測可以在銷售前預估消費者的需求，並藉以決定符合消費者需求量的存貨，來避免存貨過多或缺貨的情況。困難則是因資訊產品通路業具有產業環境變遷快速、技術進步迅速、決策時間短、產品規格變動快、資訊流通快、產業競爭大、產品跌價快等特質，造成要建構有效的銷售預測模式變成一個挑戰。
　　有鑑於銷售預測對於資訊產品通路業的重要性，本研究將討論如何利用無母數統計方法(nonparametric statistical approach)與類神經網路(neural network)分別針對資訊通路業的代理商與經銷商建構銷售預測模式。本研究以台灣某資訊產品代理商與零售商的銷售資料進行實證研究，希望能達成以下四個目標(1) 應用無母數統計方法與類神經網路技術建構資訊產品代理商銷售預測模式並分析其可行性。(2) 找出影響代理商銷售預測較重要的關鍵變數與討論變數之實務意義。(3) 結合無母數統計方法與類神經網路技術建構資訊產品零售商銷售預測模式並討論其有效性。(4) 找出影響零售商銷售之各電腦產品的重要預測變數，並討論變數的實務意義。
Sales forecasting is one of the most crucial challenges for the information technology product channel. By predicting consumer demand before selling, sales forecasting helps determine the appropriate number of products to keep in inventory, thereby preventing over- or under-stocking. Because of the information technology product channel’s volatile environment, with rapidly advancing technologies, short decision times, rapid changes to product specifications, fast flowing information, intense competition, and rapidly eroding prices, constructing an effective sales forecasting model is a challenging task.
Because sales forecasting is vital to the information technology product channel, this study presents a discussion on how to use neural networks and a nonparametric statistical approach to construct sales forecasting models for information technology retailers and agents, respectively. Empirical studies were conducted using sales data collected from information technology agents and retailers in Taiwan to achieve the following four objectives: (1) to construct a sales forecasting model for information technology agents by applying a nonparametric statistical approach and to analyze its feasibility; (2) to determine the key variables affecting sales forecasting for agents and to discuss the practical significance of the variables; (3) to construct a sales forecasting model for information technology retailers by combining the nonparametric statistical approach with neural network technology and to discuss its effectiveness; and (4) to determine the key forecasting variables affecting retailer sales of various computer products and to discuss the practical significance of the variables.
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