A Data-based Approach for Quality Regulation

Abstract

In the customized production more complex processes are required. Companies are challenged by monitoring these complex processes which compared to mass production show a lower degree of standardization and are therefore characterized by higher instabilities. Quality management has developed various techniques to deal with instabilities such as error analysis and process monitoring, which are implemented successfully in mass production. These techniques are based on the principle of causality and are effective in identifying, monitoring and adjusting the main cause of error in isolated effect chains. Within the customized production the elimination of the main cause of error does not lead to a sustained improvement of production quality since causes of error differ due to varied products to be manufactured. Furthermore, processes in customized production increasingly imply immanent interdependencies. The emergence of quality along the value chain is thus getting more complex and can less be explained by an effect chain using the principle of causality. The data-based quality regulation is therefore developed in order to achieve high quality in complex production. This paper outlines the data-based quality regulation as well as its need for research. Afterwards, an approach based on a virtual production model to validate suitable data mining methods for the data-based quality regulation is provided.