Title :
Supply Chain Risk Management by Mining Business Dependencies
Author_Institution :
SoftProEuro s.r.l., Cluj-Napoca, Romania
Abstract :
Current supply chain risk management techniques rely on the integration of the business information systems of the entities forming the supply chain. The method is well-suited for small and medium entities, but large Entities are rather reticent in integrating Information Systems with the ones of SMEs. This article presents a risk-reduction mechanism based on mining Business Dependencies that were previously automatically discovered from the Web logs of our multi-server search application. The material gathered in the previous experiments (business dependencies inferred from Web usage logs and information on entities) serves as a support for building Virtualized Supply Chains. The generation of the Virtualized Supply Chains and the computation of the associated risks conduct to the derivation of a risk reduction technique, which can be later applied in the redesign of Supply Chains and replace suppliers having high risk measures with those exhibiting lower risk measures.
Keywords :
Internet; business data processing; data mining; risk management; supply chain management; Web usage logs; business information systems; medium entities; mining business dependencies; multiserver search application; risk-reduction mechanism; small entities; supply chain risk management; virtualized supply chains; Business; Companies; Floods; Management information systems; Radiofrequency identification; Risk management; Scientific computing; Supply chain management; Supply chains; Web search; Business Dependency Map; Supply Chain Risk Management; Virtualized Supply Chain; Web Usage Mining;
Conference_Titel :
Symbolic and Numeric Algorithms for Scientific Computing, 2008. SYNASC '08. 10th International Symposium on
Conference_Location :
Timisoara
Print_ISBN :
978-0-7695-3523-4
DOI :
10.1109/SYNASC.2008.47