Please use this identifier to cite or link to this item:
Lober, Andreas
Baumgärtel, Hartwig
Verbeet, Richard
Year of Publication: 
[Editor:] Kersten, Wolfgang [Editor:] Blecker, Thorsten [Editor:] Ringle, Christian M. [Title:] Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 29 [ISBN:] 978-3-7531-2346-2 [Year:] 2020 [Pages:] 591-623
Purpose: A primary requirement of Industry 4.0 and realization of Cyber-Physical Systems in production and logistics is the dynamic connection of physical and digital components. Service-oriented Architectures are a well established approach to meet this requirement. However, a service discovery using syntactic descriptions of services limits efficient application of a Service-oriented Architecture concerning the complexity and variability of existing processes. Methodology: A semantics based mechanism for service discovery can solve this limitation. It uses an ontology management system containing a domain specific ontology and modelling specific Cyber-Physical Systems as individuals. SPARQL Protocol And RDF Query Language (SPARQL) queries searching this ontology with context-related parameters. A use case demonstrates the mechanism by realizing an in-house transport request. Findings: A syntax based service discovery requires a definition and publishing of unique service names. However, complex Cyber-Physical Systems using multiple parameters during service calls require disproportionate effort to implement and maintain these names. A semantics based service discovery considers various parameters by using a specific ontology calling services by their properties without knowing the service name. Originality: A semantics based is decoupled from specific service implementations of components in a Cyber-Physical Systems. Therefore, an explicit specification of parameter configurations in service descriptions is not necessary. A Service-oriented Architecture can be implemented in complex systems without extensive adjustments or coordination mechanisms.
Industry 4.0
Supply Chain Management
Artificial Intelligence
Data Science
Persistent Identifier of the first edition: 
Creative Commons License:
Document Type: 
Conference Paper

Files in This Item:

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.