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Authors: Sandro Emmenegger 1 ; Emanuele Laurenzini 2 and Barbara Thönssen 3

Affiliations: 1 University of Applied Sciences and Arts Northwestern Switzerland FHNW, Switzerland ; 2 University of Camerino, Italy ; 3 University of Applied Sciences and Arts Northwestern Switzerland FHNW and University of Camerino, Switzerland

ISBN: 978-989-8565-31-0

Keyword(s): Supply-Chain-Management, Risk-Management, Enterprise Ontology, Semantic Technology.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Business Intelligence ; Collaboration and e-Services ; Complex Systems Modeling and Simulation ; Data Engineering ; e-Business ; Enterprise Information Systems ; Health Information Systems ; Integration/Interoperability ; Intelligent Information Systems ; Interoperability ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Sensor Networks ; Simulation and Modeling ; Software Agents and Internet Computing ; Software and Architectures ; Software Engineering ; Symbolic Systems

Abstract: To discover risk as early as possible is a major demand of today’s supply-chain- risk-management. This includes analysis of internal resources (e.g. ERP and CRM data) but also of external sources (e.g. entries in the Commercial Register and newspaper reports). It is not so much the problem of getting the information as to analyze and evaluate it near-term, cross-linked and forward-looking. In the APPRIS project an Early-Warning-System (EWS) is developed applying semantic technologies, namely an enterprise ontology and an inference engine, for the assessment of procurement risks. The approach allows for integrating data from various information sources, of various information types (structured and unstructured), and information quality (assured facts, news); automatic identification, validation and quantification of risks and aggregation of assessment results on several granularity levels. For representation the graphical user interface of a project partner’s commercial supply-manageme nt-system is used. Motivating scenario is derived from three business project partners’ real requirements for an EWS with special reference to the downstream side of supply chain models, to suppliers’ company structures and single sourcing. (More)

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Paper citation in several formats:
Emmenegger S., Laurenzini E. and Thönssen B. (2012). Improving Supply-Chain-Management based on Semantically Enriched Risk Descriptions.In Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2012) ISBN 978-989-8565-31-0, pages 70-80. DOI: 10.5220/0004139800700080

@conference{kmis12,
author={Sandro Emmenegger and Emanuele Laurenzini and Barbara Thönssen},
title={Improving Supply-Chain-Management based on Semantically Enriched Risk Descriptions},
booktitle={Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2012)},
year={2012},
pages={70-80},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004139800700080},
isbn={978-989-8565-31-0},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Management and Information Sharing - Volume 1: KMIS, (IC3K 2012)
TI - Improving Supply-Chain-Management based on Semantically Enriched Risk Descriptions
SN - 978-989-8565-31-0
AU - Emmenegger S.
AU - Laurenzini E.
AU - Thönssen B.
PY - 2012
SP - 70
EP - 80
DO - 10.5220/0004139800700080

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