資料採礦分析法於解析客訴不良品之應用 - 以TFT-LCD製造廠某C公司為例

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題名:	資料採礦分析法於解析客訴不良品之應用 - 以TFT-LCD製造廠某C公司為例
作者:	宋堯正;Sung,Yao-cheng
貢獻者:	工業管理研究所在職專班
關鍵詞:	線缺陷解析;資料採礦;決策樹;Line Defect analytical;Data Mining;Decision Tree
日期:	2013-06-21
上傳時間:	2013-07-10 11:50:12 (UTC+8)
出版者:	國立中央大學
摘要:	面板製造商提供品質良好的產品給客戶是最基本的服務，因此當客戶發現品質不良產品時，客戶會要求製造商能在更短的時間內找出客退不良品品質異常的原因並要有解決的方案，而負責找出產品品質異常原因就是「解析」的工作，雖然各種品質分析手法，如魚骨圖、5Why分析…等的運用在製造業提升品質已相當普遍，但對於如何提升「解析作業」的效率來說並無特別的幫助，而解析的效率是靠經驗累積來增加的，但人的經驗傳承終究會讓原先累積的解析知識所遺漏，對提升解析效率是不利的。資料採礦（Data Mining）是利用電腦從一大堆的資料中尋找出特定的模式或有關聯的資訊，這一些資訊可以輔助決策者做出重要的判斷，因此對於品質不良產品解析的作業應可利用此方式來增加效率。本研究乃藉由個案公司客退車用面板線缺陷的（Line Defect）資料，利用資料採礦相關手法，探討此一手法是否能有效的幫助解析作業提高效率，此一新的模式可否運用於客退產品的解析作業，所得結論如下： 1.資料採礦所分析出來的線缺陷解析規則對發生異常責任單位判定是可信的，因而客退產品解析作業可利用此一模式增加解析效率。 2.資料採礦分析前需要先了解問題本質與運用方法，才能找出所需要之資訊幫助決策。 Panel manufacturers’ offering of high-quality products to customers is the most basic service. Thus, when customers obtain bad products, they will ask manufacturers to find the causes of abnormal products returned in shorter time and have the solutions. “Analysis” is adopted to find the causes of abnormal product quality. Although various quality analysis techniques, such as Fishbone Diagram and 5Why Analysis, are commonly applied to upgrading of quality in manufacturing industry, they do not significantly enhance the efficiency of “analysis” which relies on cumulative experiences. However, people’s inheritance of experience will be neglected by the original cumulative analytical knowledge and it will not enhance analytical efficiency. Data Mining means to find specific models or related information from great amount of data by computers. The information helps decision makers to have significant judgment. Thus, efficiency of analysis on bad products can be reinforced by this method. This study focuses on data of automobile panel “Line Defect” returned of case company and by Data Mining, it tries to find if the technique can effectively help enhance efficiency of analysis and if the new model can be applied to analysis of returned products. The conclusions are below: 1.Line Defect analytical rules by Data Mining are reliable to judge the units of abnormality. Thus, efficiency of analysis on returned products can be enhanced by this model. 2.Before Data Mining analysis, it must recognize the essence of the problems and the technique applied in order to find the information needed for decision making.
顯示於類別:	[工業管理研究所碩士在職專班 ] 博碩士論文

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