Using Data Mining to Identify Critical Factors of Product Quality: An Empirical Study on LED Packaging

• Main Authors: Peng, Hsin Chieh, 彭新傑
• Other Authors: Chen, James C.
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/932skc

碩士 === 國立清華大學 === 工業工程與工程管理學系碩士在職專班 === 104 === Since stiff global competition, the price of light-emitting diode (LED) package is going down. How to keep the gross profit of product had been a critical issue for the industry. Although we can shift the losses to the vendor for asking more cost down, we also have to promote productive yield with data mining. The better our product is, the higher gross profit is, so we can use data mining to identify critical factors and make our company more profitable and competitive. Data mining is a very powerful and useful tool to find out the root cause and decrease the moment of detecting problems among the flow of data analysis. Knowledge discovery from database (KDD) is the procedure that we can define our problem and find out critical factors by using data mining skills. Owing to the development of information system, all company has their own database to store transaction data. From data collecting to data analyzing, KDD just set up a framework to make manufacturing much better intelligent. In this research, we used two common data mining skills, decision trees and association rules, to identify critical factors of product quality. In the empirical study of LED packaging, we know critical factors in bill of materials (BOMs) base on decision tree, and understand how to choose the operation machine in manufacturing. Using data mining is very different from the rule of thumb that’s used in the past, and it’s more efficient to make decision. Last, we hope to enhance our manufacture by mining historical data, and make intelligent manufacture with rolling discover knowledge from database. Keyword: Data mining, Knowledge discovery from database, Decision tree, Association rules, Decision support.