Improving supply-chain-management based on semantically enriched risk descriptions
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
04.10.2012
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Proceedings of 4th Conference on Knowledge Management and Information Sharing (KMIS2012)
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
70-80
Patentnummer
Verlag / Herausgebende Institution
Verlagsort / Veranstaltungsort
Barcelona
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
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-management-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. Research paper: Improving supply-chain-management based on semantically enriched risk descriptions.
Schlagwörter
Veranstaltung
International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Lizenz
Zitation
Emmenegger, S., Laurenzini, E., & Thönssen, B. (2012). Improving supply-chain-management based on semantically enriched risk descriptions. Proceedings of 4th Conference on Knowledge Management and Information Sharing (KMIS2012), 70–80. https://doi.org/10.5220/0004139800700080