Paradigm change towards Visual Analytics for data-driven quality improvement in highly flexible production systems

Especially in highly flexible production systems, quality management is confronted with immense challenges. Data Analytics is regarded as a technology for coping with increasing complexity. Currently a lack of value-added orientation impairs the effectiveness of such initiatives. This is because the workers do not have the capabilities to process Data Analytics. One approach is to train these workers to enable them for this new technology. These experts are highly busy with their core tasks, so that the other approach is to reduce the complexity for the user of Analytics by providing an interface with a high and interactive usability to have a minimum of training efforts. Today the usage of Visual Management within the lean production paradigms is used to provide Key Performance Indicators and other static information to the workers in an analogue or even digital way. The main problem is that there is no possibility for the worker to analyze why Key Performance Indicators changed and to find the root causes for the development so that they can improve their processes based on the analysis they have done. This paper shows a concept how Visual Analytics can be applied to enable production and quality experts to perform their analysis directly at the value-added process and thus continuously improve process and product quality.