An Approach to Tacit Knowledge Classification in a Manufacturing Company

Abstract

This article attempts to classify the tacit knowledge acquired using the example of the research and development (R&D) department of a manufacturing company. Based on studies in the literature and direct interviews in the company analysed, the authors' own model for classifying the tacit knowledge in an R&D department was proposed. The description of this model has been divided into two parts. In the first part, viz., the classification of knowledge, through three planes: (1) selection of algorithm inputs and the grouping of knowledge accumulated in an enterprise; (2) algorithm activity, that is, the use of algorithms based on clustering them, for calculations; (3) interpretation of results. The Bayesian Network was used for this purpose, which was modelled on the defined relationships between representing of tacit knowledge. Then, on the basis of the case study, the classification of knowledge was prepared according to: (1) definition of knowledge in the R&D department and its modelling, (2) implementation of a suitable number of training sets, (3) verification of the knowledge base, that is, declaration of the value of the knowledge observed, followed by (4) assignment of the probability of returning to the node of network clusters containing interpretations of business benefits.