Publication

Applying Digital Product Memories in Industrial Production

Peter Stephan, Peter Stephan, Markus Eich, Jörg Neidig, Martin Rosjat, Roberto Hengst

In: Peter Stephan. SemProM: Foundations of Semantic Product Memories for the Internet of Things. Chapter oA Pages 283-304 ISBN 978-3-642-37376-3 Springer Berlin / Heidelberg 6/2013.

Abstract

Industrial production and supply chains face increased demands for mass customization and tightening regulations on the traceability of goods, leading to higher requirements concerning flexibility, adaptability, and transparency of processes. Technologies for the “Internet of Things” such as smart products and semantic representations pave the way for future factories and supply chains to fulfill these challenging market demands. In this chapter a backend-independent approach for information exchange in open-loop production processes based on Digital Product Memories (DPMs) is presented. By storing order-related data directly on the item, relevant lifecycle information is attached to the product itself. In this way, information handover between several stages of the value chain with focus on the manufacturing phase of a product has been realized. In order to report best practices regarding the application of DPM in the domain of industrial production, system prototype implementations focusing on the use case of producing and handling a smart drug case are illustrated.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz