Applying Digital Product Memories in Industrial Production
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
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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.
P. Stephan (B) DFKI GmbH, German Research Center for Artificial Intelligence, Kaiserslautern, Germany Current e-mail: [email protected] M. Eich DFKI GmbH, German Research Center for Artificial Intelligence, Bremen, Germany e-mail: [email protected] J. Neidig Siemens AG, Sector Industry, Nuremberg, Germany e-mail: [email protected] M. Rosjat · R. Hengst SAP AG, SAP Research, Dresden, Germany M. Rosjat e-mail: [email protected] R. Hengst Current e-mail: [email protected] W. Wahlster (ed.), SemProM, Cognitive Technologies, DOI 10.1007/978-3-642-37377-0_17, © Springer-Verlag Berlin Heidelberg 2013
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1 Introduction Regarding the economy of today, companies producing and distributing industrially manufactured goods face a broad range of challenges. Forces such as the increasing globalization of markets as well as worldwide competition are leading to shorter innovation and product lifecycles. This even results in the fact that today production facilities sometimes have a longer lifetime than the products which are produced on them. Good examples for that are products such as cellphones. Their average development time dropped from about 18 months in the year 2002 (Gunasekaran et al. 2002) down to 4 months in 2004 (Viardot 2006) and is expected to shorten even more. This ongoing situation is further intensified by increasing customer demands for frequent product updates and highly individualized goods. In addition, not only legislative institutions but also quality-aware shoppers are beginning to ask questions about the origin and history of the products they buy (Garcia et al. 2003). This results in higher requirements regarding the traceability of products and the transparency of production and logistic processes. This is especially the case for high-priced quality products, perishable goods or health care products. In order to keep up with these challenging market demands, existing processes
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