2013, Emmenegger, Sandro, Hinkelmann, Knut, Laurenzini, Emanuele, Thönssen, Barbara, Fred, Ana, Dietz, Jan L. G, Liu, Kecheng, Felipe, Joaquim

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. Our approach allows for analyzing internal resources (e.g. ERP and CRM data) and external sources (e.g. entries in the Commercial Register and newspaper reports) to assess known risks, but also for identifying black swans, which hit enterprises with no warning but potentially large impact. For proof of concept we developed a prototype that 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. The motivating scenario is derived from three business project partners real requirements for an EWS.

2012-10-04T00:00:00Z, Emmenegger, Sandro, Laurenzini, Emanuele, Thönssen, Barbara

2012-04-04T00:00:00Z, Thönssen, Barbara, Emmenegger, Sandro

Um Risiken in der Supply Chain früh zu entdecken, genügt es nicht, eigene ERP-Daten für Lieferanten auszuwerten. Vielmehr muss das gesamte Netzwerk betrachtet und Daten aus externen Quellen mit internen Daten verknüpft werden. Mit Hilfe semantischer Technologien können Daten integriert, automatisch analysiert und so Risiken früher identifiziert werden.