| Summary: | 碩士 === 國立交通大學 === 管理學院資訊管理學程 === 106 === The quality control of products, processes, and methods in the production process is very important for the machine industry that requires extremely high stability. In today's highly automated and intelligent production environment, the high reliability and stability of products are even more important. Once a product has a bad appearance, it may not only increase the cost the company, but it also has an impact. In order to meet customer expectations and market demands, the company needs to provide more types of models to meet the requirements. Quality control engineering must be used to ensure that products do not fail in the future, although there are various analysis methods, such as the Fishbone Diagram, Pareto, etc., However, it has not been able to provide more effective solutions. Most solutions still rely on the experience of engineers to make decisions.
Data Mining is a model that can be built using big data databases. It can be used to find hidden special relationships for various factors recorded in product life. The analysis method provides information to decision makers as a basis for execution judgment, solves bottlenecks in the analysis of adverse problems by engineers and improves the timeliness of proposed solutions. This study uses data mining techniques to effectively discover the problem relationships that are difficult to analyze in the quality management system. The data mining result is integrated into the system to improve product availability and stability and enhance company competitiveness and customer satisfaction.
|
|---|
