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Due to their growing amount and heterogeneity, we need a precise and standardized understanding about the foundation, structure, and forms of aggregation and especially the use of data and models within the production domain. Our aim is to investigate how to model data elements and static and dynamic relationships as well as their physical resources in the IoP, in a cross-disciplinary life cycle spanning cooperation as a basis for information management, meeting all technical, scientific-ethical, and legal framework conditions. The core solution for this challenge is the use of an adequate set of modeling techniques, transformations, and their integration with digital shadows. This chapter provides a deep insight into relevant concepts that constitute a digital shadow, link it to their semantics defined by appropriate metamodels, and discuss the data and models a digital shadow consists of in four use cases. We show a method to derive digital shadows and introduce their life cycle in relation to the product life cycle. These concepts are the foundation for data and model sharing within digital shadows applicable for worldwide production labs.

Smart manufacturing demands to process data in domain-specific real-time. Engineering models created for constructing, commissioning, planning, or simulating manufacturing systems can facilitate aggregating and abstracting the wealth of manufacturing data to faster processable data structures for more timely decision making. Current research lacks conceptual foundations for how data and engineering models can be exploited in an integrated way to achieve this. Such research demands expertise from different smart manufacturing domains to harmonize the notion space. We propose a conceptual model to describe digital shadows, data structures tailored to exploit models and data in smart manufacturing, through a metamodel and its notion space. This conceptual model was established through interdisciplinary research in the German excellence cluster “Internet of Production” and evaluated in various real-world manufacturing scenarios. This foundation for an understanding helps to manage complexity, automated analyses, and syntheses, and, ultimately, facilitates cross-domain collaboration.