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The importance of cross-process multivariate data analysis for improving products and processes is continuously increasing. Artificial intelligence and machine learning offer new possibilities to represent complex cause-effect relationships in models and to use them for optimisation. For consistent and scalable usage, unified data structures and representations of products, processes and resources are required in order to be able to use larger data populations as well as deploy these models in different application contexts. The paper presents an approach of shared prediction models for recipe optimisation based on unified digital twins in the beverage industry. For this purpose, a central generic data model was created, which is the basis for unified digital twins and thus the integration of physical and digital entities, as well as the foundation for cross-process data analysis.

© 2020 The Authors The supply of defect-free, high-quality products is an important success factor for the long-term competitiveness of manufacturing companies. Despite the increasing challenges of rising product variety and complexity and the necessity of economic manufacturing, a comprehensive and reliable quality inspection is often indispensable. In consequence, high inspection volumes turn inspection processes into manufacturing bottlenecks. In this contribution, we investigate a new integrated solution of predictive model-based quality inspection in industrial manufacturing by utilizing Machine Learning techniques and Edge Cloud Computing technology. In contrast to state-of-the-art contributions, we propose a holistic approach comprising the target-oriented data acquisition and processing, modelling and model deployment as well as the technological implementation in the existing IT plant infrastructure. A real industrial use case in SMT manufacturing is presented to underline the procedure and benefits of the proposed method. The results show that by employing the proposed method, inspection volumes can be reduced significantly and thus economic advantages can be generated.