Abstract
The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production, two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI. Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure. Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.
Philipp Brauner
Manuela Dalibor
Matthias Jarke
Ike Kunze, M.Sc.
Researcher
István Koren
Gerhard Lakemeyer
Martin Liebenberg
Judith Michael
Dr. rer. nat. Jan Pennekamp
Postdoctoral Researcher
Christoph Quix
Bernhard Rumpe
Wil Van Der Aalst
Klaus Wehrle
Head of Group
Andreas Wortmann