WORKSHOP PROCESS OPTIMIZATION BASED ON THE COLLECTIVE INTELLIGENCE OF WORKSHOP EMPLOYEES INVOLVED IN AFTER-SALES INTELLIGENCE OF MERCEDES-BENZ CARS

Abstract

The collective intelligence of Mercedes-Benz cars after-sales specialists, which includes customer service, car maintenance and repair, is captured in a large number of disparate and unconnected databases. It must be accessed through different retrieval systems and the information fragments found must be linked manually. In addition, best-practice and experience knowledge of workshop employees in maintenance, service and repair is not systematically gathered and evaluated. In this paper, we introduce a new method and support system for bootstrapping improvement of such workshop processes by system. Our method makes latent semantic links between information elements kept in different databases explicit in the form of an ontology whose links are used in subsequent retrieval processes. Frequent successful uses of individual links by the workshop employees increase their statistical weight in future retrieval processes supported by an ontology-based integrated retrieval system. Therefore, the ontology uses the collective intelligence of the workshop employees in order to optimize the search for relevant documents.