Supply Chain Management aims at optimizing the flow of goods and services from the producer to the consumer. Closely interconnected enterprises that align their production, logistics and procurement with one another thus enjoy a competitive advantage in the market. To achieve a close alignment, an instant, robust and efficient information flow along the supply chain between and within enterprises is required. However, less efficient human communication is often used instead of automatic systems because of the great diversity of enterprise systems and models. This paper describes an approach and its implementation SCM Intelligence App, which enables the configuration of individual supply chains together with the execution of industry accepted performance metrics. Based on machine-processable supply chain data model (the SCORVoc RDF vocabulary implementing the SCOR standard) and W3C standardized protocols such as SPARQL, the approach represents an alternative to closed software systems, which lack support for inter-organizational supply chain analysis. Finally, we demonstrate the practicality of our approach using a prototypical implementation and a test scenario.
%0 Conference Paper
%1 petersen2016towards
%A Petersen, Niklas
%A Lange, Christoph
%A Auer, Sören
%A Frommhold, Marvin
%A Tramp, Sebastian
%B Proceedings of the 19th International Conference on Business Information Systems 6-8 July 2016, Leipzig, Germany
%D 2016
%K 2016 auer es frommhold group_aksw leds lucid tramp
%T Towards Federated, Semantics-based Supply Chain Analytics
%X Supply Chain Management aims at optimizing the flow of goods and services from the producer to the consumer. Closely interconnected enterprises that align their production, logistics and procurement with one another thus enjoy a competitive advantage in the market. To achieve a close alignment, an instant, robust and efficient information flow along the supply chain between and within enterprises is required. However, less efficient human communication is often used instead of automatic systems because of the great diversity of enterprise systems and models. This paper describes an approach and its implementation SCM Intelligence App, which enables the configuration of individual supply chains together with the execution of industry accepted performance metrics. Based on machine-processable supply chain data model (the SCORVoc RDF vocabulary implementing the SCOR standard) and W3C standardized protocols such as SPARQL, the approach represents an alternative to closed software systems, which lack support for inter-organizational supply chain analysis. Finally, we demonstrate the practicality of our approach using a prototypical implementation and a test scenario.
@inproceedings{petersen2016towards,
abstract = {Supply Chain Management aims at optimizing the flow of goods and services from the producer to the consumer. Closely interconnected enterprises that align their production, logistics and procurement with one another thus enjoy a competitive advantage in the market. To achieve a close alignment, an instant, robust and efficient information flow along the supply chain between and within enterprises is required. However, less efficient human communication is often used instead of automatic systems because of the great diversity of enterprise systems and models. This paper describes an approach and its implementation SCM Intelligence App, which enables the configuration of individual supply chains together with the execution of industry accepted performance metrics. Based on machine-processable supply chain data model (the SCORVoc RDF vocabulary implementing the SCOR standard) and W3C standardized protocols such as SPARQL, the approach represents an alternative to closed software systems, which lack support for inter-organizational supply chain analysis. Finally, we demonstrate the practicality of our approach using a prototypical implementation and a test scenario.},
added-at = {2024-06-18T09:45:34.000+0200},
author = {Petersen, Niklas and Lange, Christoph and Auer, S{\"o}ren and Frommhold, Marvin and Tramp, Sebastian},
biburl = {https://www.bibsonomy.org/bibtex/24405d6961992d59e4dfaf98a0f30d2ec/aksw},
booktitle = {Proceedings of the 19th International Conference on Business Information Systems 6-8 July 2016, Leipzig, Germany},
interhash = {74502acd2759ef7bf0d5f0df722293c9},
intrahash = {4405d6961992d59e4dfaf98a0f30d2ec},
keywords = {2016 auer es frommhold group_aksw leds lucid tramp},
timestamp = {2024-06-18T09:45:34.000+0200},
title = {Towards Federated, Semantics-based Supply Chain Analytics},
year = 2016
}