Methodology for training IT staff to work in the 1C environment

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Despite a significant increase in demand for specialists working in the 1C: Enterprise environment, the education system does not always meet employers' expectations. The shortage of personnel and the mismatch between graduates' competencies and the market demand are key challenges that require new approaches to training. Purpose of the study. To address the pressing issue of ensuring professional safety in the information society by managing risks associated with human errors, we describe optimal training methods for specialists working with 1C software, taking into account employer requirements. Materials and Methods. A multiple analysis of vacancies for employees working with 1C software was conducted, network models of personal career paths and the machine learning tools used to support them were described. The process of generating organizational development paths is automated using a network diagram of personal competencies and their descriptions. Results. An automated database has been developed that enables the creation of optimal personal development paths for IT specialists working on the 1C: Enterprise platform based on criteria of maximum work experience and the maximum number of certificates. The use of an automated HR manager database for work in the 1C environment makes it pos¬sible to reduce the workload of people involved in HR management, which allows for the effective training of existing personnel, as well as the hiring of new employees by minimizing personnel risks associated with the preparation of requirements for employees for their successful career growth. Conclusion. The application of the research results minimizes the risks associated with the training of IT specialists working on the 1C: Enterprise platform and the creation of job vacancies for their employment. The obtained results can be use in enterprises that operate the 1C: Enterprise platform.

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IT staff training, 1C: Enterprise, artificial intelligence methods, mathematical models, personal development paths

Короткий адрес: https://sciup.org/147253161

IDR: 147253161   |   УДК: 004.896   |   DOI: 10.14529/ctcr260110