In the production industry, the volume, variety, and velocity of data as well as the number of deployed protocols increase exponentially due to the influences of the Internet-of-Things (IoT) advances. While hundreds of isolated solutions exist to utilize these data, e.g., optimizing processes or monitoring machine conditions, the lack of a unified data handling and exchange mechanism hinders the implementation of approaches to improve the quality of decisions and processes in such an interconnected environment. The vision of anInternet of Productionpromises the establishment of aWorldwide Lab, where data from every process in the network can be utilized, even interorganizational and across domains. While numerous existing approaches consider interoperability from an interface and communication system perspective, fundamental questions of data and information interoperability remain insufficiently addressed. In this article, we identifytenkey issues, derived from three distinctive real-world use cases that hinder large-scale data interoperability for industrial processes. Based on these issues, we derive a set offivekey requirements for future (IoT) data layers, building upon the FAIR data principles. We propose to address them by creatingFactDAG, a conceptual data layer model for maintaining a provenance-based, directed acyclic graph of facts, inspired by successful distributed version-control and collaboration systems. Eventually, such a standardization should greatly shape the future of interoperability in an interconnected production industry.

In the context of the Industrial Internet of Things safety and robustness play a crucial role as autonomous and emergent behavior increase the complexity of Cyber-Physical Production Systems. Given the intractability of exhaustively verifying distributed production systems, testing and runtime monitoring seem to be two promising methods used to verify correctness in the digitally networked factory. External runtime monitoring is an efficient and lightweight technique that bridges the gap between testing and verification.This paper describes a framework for on-the-fly monitor generation using a state-of-the-art monitoring algorithm for proving properties of a distributed production system relying on the Amazon Web Services Internet of Things architecture and the use of the digital shadow. The feasibility of the proposed architecture is evaluated using an industrial case study.

Due to agile product development, production systems have to be flexible and adaptable to meet high quality standards and a high productivity. As a result, set-up processes has to be shorter and more resilient because of the increasing number of variants. To meet future requirements, the processes need to be self-adaptive and reconfigurable at any time. Nowadays, a shift of the automation pyramid to interconnected cyber physical systems can be observed as well as emerging technologies as cloud and edge computing are introduced to production systems. These technologies in combination with the Digital Shadow, which provides information about all production assets, open up potential for an adaptive process control and an overall life cycle data management. For this, the Digital Shadow has to be used not only for the aggregation of data, but also for pushing data back into the system and to control the process. As a result, services in regard to an architecture based on edge computing as an enabling technology for an adaptive production together with the Digital Shadow are presented, implemented and discussed on the basis of an industrial use case.