Implementation of Decision Support System with Data Mining Methods in the Quality Control Process of the Automotive Sector

Today, the automotive sector is the "key" sector for developed and even developing countries. A strong automotive sector is striking as one of the common features of industrialized countries. Production in this sector consists of many processes. One of the most important of these processes is quality control. The measurement data in this area is very large and as the volume of data increases, the rate that people understand is reduced. Variations are the enemy of quality. There are many variations in the area of quality control. In this study, a decision support system is applied in the quality control process with classification algorithms which are data mining methods. C4.5, Naive Bayes, SMO and Random Forest algorithms are run on data set collected from production. These algorithms are used to measure the quality and accuracy of the product without completing the operations during production. Algorithms have been cost-reduced by determining that the product is faulty before operations are completed. The algorithm C4.5 has been the best performing algorithm. In addition, these algorithms make quality analysis very fast and easy. Thanks to this work, the cost of labor and materials has been reduced in the production company