Quality Intelligence (QI)

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The initiative Industry 4.0 from the german government focusses on flexible manufacturing systems with enhanced productivity. Cyber-Physical Systems (CPS) are the core of Industry 4.0 as well as for its technical integration in production and logistics processes. CPS are complex systems and a huge amount of available data. Data can be obtained by observations, measurements and statistical investigations and describes the first step of generating knowledge. If data get linked with a certain context, information is generated. Finally, knowledge originates by linking information with experiences, concepts and expertise. Knowledge Management provides the basics for the generation of knowledge form data and information. Business Intelligence (BI) has become a novel approach arising from the demand for informational systems. BI combines all activities of integrating, improving, transforming and analyzing data. Supply Chains are one form of CPS. Across a supply chain numerous data concerning products, orders, processes and quality are incurred. Supply chain management has realized the importance of these data. Several approaches focusing the importance of data exchange within supply chains exist. Beyond that, the high impact of product, process and system quality is common in the target system of supply chain management. A link between both aspects, that is an analysis of quality-related data across a supply chain for decision support of quality management, is still missing. The academic void should be picked up within the research project Quality Intelligence (QI). Research objective is the development of a prediction model for quality-related instabilities across a supply chain reference model. Therefore, in a first step a quality-related description model of a supply chain should be developed. The quality-related data will be identified, allocated and the interdependencies analyzed. By means of ontology, the gained results should be integrated into the prediction model. The research results should enable conclusions about quality-related system statuses in the future. This research contributes to efficient usage of resources such as existing data. The ex-ante avoidance of quality-related instabilities could cut quality costs due to ex-post reactions on quality problems.