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This article attempts to create a model of converting tacit knowledge into explicit knowledge with Bayes algorithm for the research and development department in a manufacturing company. Based on the reference works, there have been proposed the characteristics of knowledge conversion process, with the focus on the factors supporting sharing tacit knowledge. The sources of tacit knowledge in the research and development department in a manufacturing company were identified, and then mechanisms of its collection were proposed. As a result, Bayes algorithm was implemented to perform the conversion process of the collected tacit knowledge into explicit knowledge. By stimulating knowledge workers to share tacit knowledge, the organisation increases its know-how value, by formalized specialist knowledge available for other procedures. The model is illustrated by the example of business practice. As a result, it is assumed to receive measurable benefits, i.e. reductions of cost, corrections, complaints and faster completion of a similar project, optimal selection of workers. It presents directions for further work, including the IT implementation of the presented model and its verification.
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