A Multiagent System Prototype of a Tacit Knowledge Management Model to Reduce Labor Incident Resolution Times

The transformation of the tacit knowledge of a company's human resources into permanent organizational capital in spite of possible staff turnover is of business interest. This research focuses on the management of tacit knowledge to resolve labor incidents and reduce resolution times. We present the GESTAC model, a name derived from the first syllables of the Spanish words "gestion" (management) and "tacito" (tacit), which identifies, locates and rates people in the business domain capable of resolving a labor incident logged by a user employed by the company. In order to achieve its objective, the GESTAC model follows the tacit knowledge management paradigm, according to which tacit knowledge that could eventually resolve the logged incidents is identified, captured and stored in a permanent database, and then evaluated and disseminated to the people who have need of the knowledge. This could lead to the knowledge source being automatically rerated, and the entire process restarted. The aim is to contribute to the state of the art, showing that by applying tacit knowledge management to a specific domain the GESTAC model is able to reduce incident resolution times with respect to traditional systems. The model was prototyped (GESTAC_APP) using the multiagent systems paradigm.