Data Mining Methods Used for Quality Management - a Bibliometric Analysis

Nowadays, the most significant trend regarding Industry 4.0 in manufacturing appears to be smart factories. In Smart factories, manufacturing is mostly lead by robots with the use of a wide range of sensors, QR codes, etc. Factories can monitor all the manufacturing processes and store huge amounts of data. From this data, they can mine information that can be beneficial for a company's revenues, costs, or product quality, which is mainly in our interest. In this state-of-the-art paper, we have performed bibliometric analysis and an extensive survey on recent developments in the field of Data mining techniques application in quality management in manufacturing. This study collected research papers, journal articles, and conference proceedings from Web of Science (WoS) for all history until 2019. A total of 372 papers from WoS were found. We also analyzed papers from the Scopus database from which we selected 660 papers. This paper summarizes the increasing structure of Data mining applications for quality management and provides a concise background overview of various Data mining techniques frequently used in the last 20 years. In the bibliometric analysis, different performance metrics are extracted, such as total papers, total citations, and citations per year. These metrics are analyzed within three main areas: Productivity, Sustainability and Index. Further, top of the most productive and highly cited authors, major subject areas, sources or journals, and countries are evaluated. A list of highly influential papers is also assessed.