Data Mining Approach for Quality Control Process Improvement
MetadataShow full item record
While striving for serving high quality products, companies are also struggling for cost efficiency. In other words, today’s competitive business environment entails the fact that the economic benefits of the company must be considered together with the product requirements to be met for customer satisfaction. Hence, companies must focus both on improving their ongoing process and establishing cost efficiency. To accomplish this success, they are trying various ways, and data science and data mining tools are the latest solutions that the companies are using in this digital transformation era. With this motivation, this research is conducted in one of the leading bus manufacturers in the automotive industry for improving the cost efficiency of quality control processes with the application of data mining methods. The company has decided to make optimization in the current routes of test drives. Currently, the busses are sent to various routes for validation after the quality control processes. The company supports this project for identifying optimum route assignments in order to minimize test-drives distances, and consequently decrease the related costs. The study starts with the collection of data from the quality control records. Then, it continues with pre-processing and analysis of data to understanding the quality control (QC) process and failure modes. After that, data are processed on WEKA data mining software, for understanding and inspecting the patterns and relationships between route requirements and specific error codes. Finally, the appropriate classification algorithm is selected. After the application of appropriate data mining algorithm, rules are set for route characteristics, and assignments are done between routes and QC error codes. Optimization of routes is done by considering the minimization of distance that is completed on test drives. The comparative results display 18.11% decrease in fuel consumption after the optimization of the routes with the rules set. A decision support app is also developed on android studio, which can be used by the company for faster route assignment decisions.
The following license files are associated with this item: