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Title: 資料探勘於汽車零組件業銷售預測之研究
A Study of Data Mining in Sales Forecasting for Automobile Parts Industry
Authors: 陳登貴
Teng-Kuei Chen
Contributors: 董信煌
Shing-Hwang Doong
Keywords: 資料探勘;銷售預測;類神經網路;線性迴歸;時間序列分析(ARIMA模式)
Data Mining;Sales Forecasting;Artificial Neural Network;Linear Regression;Time Series Analysis (ARIMA model)
Date: 2005
Issue Date: 2011-05-26 11:08:17 (UTC+8)
Publisher: 高雄市:[樹德科技大學資訊管理研究所]
Abstract: 銷售活動向來是企業經營賴以生存的重要功能,而銷售預測是銷售循環的重要工作之一,由於預測可以減少未來的不確定性風險,並提供計劃和決策的依據,使得預測扮演著相當重要的角色。而在汽車零組件供應售後維修市場(AM)的行銷中,由於受到市場競爭等不確定因素影響,難以掌握需求,往往只能根據過去銷售資料以經驗法則作為主要的預測基礎。
Sales activity has been considered as an important function to sustain business operations, and sales forecasting is one of essential duties of the sales circle. Because predictions can reduce the risks of the future uncertainty, and provides the basis for making plans and decisions, they play an important role. As regards marketing in automobile parts after-sale service market, due to the influence of uncertainty such as market competition, the prediction can only be made, based on the previous sales data, by heuristic rules.
Prediction usually employs various statistical methods or regression analyses to find out useful trends or models from the historical data, and then obtains forecast of the next period or circle from these trends or models. Prediction is a related technique of data mining. This study attempts to apply the data mining technique to sales forecasting, in order to offer a better sales forecasting model, provide more accurate information to the enterprises, so that the inventory cost will be deducted, customers' needs are met, and enterprises earn the maximum profit.
This study adopts four prediction methods: the artificial neural network of data mining technique, the simple statistics, the linear regression, and the time series analysis. The results show that the artificial neural network obtains the most accurate prediction values.
Appears in Collections:[資訊管理系(所)] 博碩士論文

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