Download PDF | Downoads: 49
Abstract:The results of personnel competence assessment obtained on the basis of neural network approach are presented. The term “competences“ is used in many scientific disciplines and practical applications. In personnel management, competence is formally described as requirements for the professional qualities of the employee. The importance of competencies is emphasized in ISO 9001 international standards and models of national and transnational product quality awards. However, in assessing competencies, the final point of view has not yet been formed. There are various methods and techniques that allow from one position or another to obtain an assessment of the competencies of employees, which is reduced, most often, to a subjective approach. In this paper, it is proposed to use artificial neural networks to assess competencies, with their help, the possibility of classifying workers by competence level is shown, the results of modeling the neural network using the Simulink tool are presented.
Keywords:assessment of competencies, competence of the employee, neural network, simulation of the evaluation system
JEL-Classification: J01, M53, M59
References (transliterated):(2015). GOST R ISO 9001:2015 Cistemy menedzhmenta kachestva. Trebovaniya [GOST R ISO 9001:2015 Quality management system. Requirements] Moscow: Standartinform. (in Russian).
Atkins P., Wood R. (2002). Self-versus others' ratings as predictors of assessment center ratings: Validation evidence for 360-degree feedback programs Personnel Psychology. (55(4)). 871-904.
Coppin B. Atificial intelligence illuminated. SudburyJones & Bartlett Publishers. Retrieved from https://www.abebooks.com/9780763732301/Artificial-Intelligence-Illuminated-Jones-Bartlett-0763732303/plp.
Education and Culture DG (Education and Training)The European Qualifications Framework for Lifelong Learning (EQF). Luxembourg: Office for Official Publications of the European Communities. Retrieved from http://ec.europa.eu/dgs/education_culture
Gorbachevskaya E.N., Leonidov A.V. (2015). Model neyronnoy seti dlya reytingovoy otsenki kompetentnosti sotrudnikov Vestnik Volzhskogo universiteta imeni V.N. Tatischeva. 1 57-71.
Houé R., Grabot B., Tchuente G. (2011). Fuzzy logic in competence management European Society for Fuzzy Logic and Technology (EUSFLAT-LFA). 651-656.
Hr-tv.ru. Retrieved August 06, 2018, from https://hr-tv.ru/articles/author-opinion/kak-provesti-sobesedovanie-po-kompetentsijam-tehnika-star.html
Hrhelpline.ru. Retrieved August 06, 2018, from https://hrhelpline.ru/otsenka-i-attestatsiya-personala-po-delo-2
Jevšček M. (2016). Competencies assessment using fuzzy logic Journal of Universal Excellence. 5 (2). 187-202.
Khaykin S. (2006). Neyronnye seti: polnyy kurs [Neural networks: full course] Moscow: Izd. dom Vilyams. (in Russian).
Kim P. (2017). MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence Seoul, Soul-t'ukpyolsi, Korea (Republic of ).
Krichevskiy M.L. (2018). Prikladnye zadachi menedzhmenta [Applied management tasks] Moscow: Kreativnaya ekonomika. (in Russian).
Macwan N., Srinivas S. (2013). Performance Appraisal using Fuzzy Evaluation Methodology International Journal of Engineering and Innovative Technology. 3 (3). 324-329.
McClelland D.C. (1973). Testing for competence rather than for intelligence American Psychologist. (1(28)). 1-14.
Neural Network Toolbox™MathWorks. Retrieved from http://www.mathworks.com
Rossiyskiy standart tsentra otsenki. Federatsiya otsenki personala NK RChKDocviewer. (in Russian). Retrieved from https://docviewer.yandex.ru/view/0
Rutkovskiy L. Metody i tekhnologii iskusstvennogo intellektaMoskva: Goryachaya liniya-Telekom. (in Russian). Retrieved from http://www.techbook.ru/book.php?id_book=400
Spenser L.M., Spenser S.M. Kompetentsii na raboteIzdatelstvo GIPPO. Retrieved from http://hr-portal.ru/article/informaciya-o-knige-layl-m-spenser-ml-sayn-m-spenser-kompetencii-na-rabote-modeli