Requirements towards optimizing analytics in industrial processes

Abstract Modern production systems are composed of complex manufacturing processes with highly technology specific cause-effect relationships. Developments in sensor technology and computational science allow for data-driven decision making that facilitate effcient and objective production management. However, process data may only be beneficial if it is enriched with meta information and process expertise, reduced to relevant information and modelling results interpreted correctly. The importance of data integration in the heterogeneous industrial environment rises at the same momentum as new metrology techniques are deployed. In this paper, we focus on optimizing analytics, containing data-driven decision making for predictive quality and maintenance. We summarize key requirements for data analytics and machine learning application in industrial processes. With a use case from automotive component manufacturing we characterize industrial production, categorize process data and put requirements in context to a real-world example.