English  |  正體中文  |  简体中文  |  Items with full text/Total items : 2737/2828
Visitors : 345756      Online Users : 89
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ir.lib.stu.edu.tw:80/ir/handle/310903100/2001

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
資訊管理研究所
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。
預測(Prediction)通常都是利用各種統計或迴歸的方法,從過去的歷史資料中找出有用的趨勢或是模式,然後利用這些趨勢或模式來求得下一期間或週期的預測值。而預測是資料探勘的一種相關技術,本研究擬藉由資料探勘技術,應用在銷售預測方面,提供較佳的銷售預測模式,帶給企業更準確的資訊,進而減少庫存成本,滿足顧客需求,促使企業利潤最大化。
本研究實驗資料探勘技術之類神經網路,與簡易統計、線性迴歸、時間序列分析四種預測方法,結果發現類神經網路可以得到較精確的預測值。
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

Files in This Item:

File Description SizeFormat
資料探勘於汽車零組件業銷售預測之研究__臺灣博碩士論文知識加值系統.htm國圖111KbHTML670View/Open


All items in STUAIR are protected by copyright, with all rights reserved.

 


無標題文件

著作權政策宣告:

1.

本網站之數位內容為樹德科技大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
 
2. 本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本校護人員(clairhsu@stu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
 
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback