Optimizing reaction and processing times in automotive industry's quality management: A data mining approach

Abstract

Manufacturing industry has come to recognize the potential of the data it generates as an information source for quality management departments to detect potential problems in the production as early and as accurately as possible. This is essential for reducing warranty costs and ensuring customer satisfaction. One of the greatest challenges in quality management is that the amount of data produced during the development and manufacturing process and in the after sales market grows rapidly. Thus, the need for automated detection of meaningful information arises. This work focuses on enhancing quality management by applying data mining approaches and introduces: (i) a meta model for data integration; (ii) a novel company internal analysis method which uses statistics and data mining to process the data in its entirety to find interesting, concealed information; and (iii) the application Q-AURA (quality - abnormality and cause analysis), an implementation of the concepts for an industrial partner in the automotive industry. © 2014 Springer International Publishing.