Applying digital product memories in industrial production


Autoria(s): Stephan, Peter; Eich, Markus; Neidig, Jörg; Rosjat, Martin; Hengst, Roberto
Contribuinte(s)

Wahlster, Wolfgang

Data(s)

2013

Resumo

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.

Identificador

http://eprints.qut.edu.au/83987/

Publicador

Springer

Relação

http://download-v2.springer.com/static/pdf/986/chp%253A10.1007%252F978-3-642-37377-0_17.pdf?token2=exp=1431046020~acl=%2Fstatic%2Fpdf%2F986%2Fchp%25253A10.1007%25252F978-3-642-37377-0_17.pdf*~hmac=1b8ca443e7ac988042939052369ae8de9b129fc59177fab57b8b30925edfbde6

DOI:10.1007/978-3-642-37377-0_17

Stephan, Peter, Eich, Markus, Neidig, Jörg, Rosjat, Martin, & Hengst, Roberto (2013) Applying digital product memories in industrial production. In Wahlster, Wolfgang (Ed.) SemProM: Foundations of Semantic Product Memories for the Internet of Things. Springer, Berlin / Heidelberg, pp. 283-304.

Fonte

Science & Engineering Faculty

Tipo

Book Chapter