Improving supply-chain-management based on semantically enriched risk descriptions

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-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.