Development of a digital employee rating evaluation system (DERES) based on machine learning algorithms and 360-degree method

Abstract

Increasing the efficiency of an enterprise largely depends on the productivity of its employees, which must be properly assessed and the correct assessment of the contribution of each employee is important. In this regard, this article is devoted to a study conducted by the authors on the development of a digital employee rating system (DERES). The study was conducted on the basis of machine learning technologies and modern assessment methods that will allow companies to evaluate the performance of their departments, analyze the competencies of the employees and predict the rating of employees in the future. The authors developed a 360-degree employee rating model and a rating prediction model using regression machine learning algorithms. The article also analyzed the results obtained using the employee evaluation model, which showed that the performance of the tested employees is reduced due to remote work. Using DERES, a rating analysis of a real business company was carried out with recommendations for improving the efficiency of employees. An analysis of the forecasting results obtained using the rating prediction model developed by the authors showed that personal development and relationship are key parameters in predicting the future rating of employees. In addition, the authors provide a detailed description of the developed DERES information system, main components, and architecture.

Keywords

• ratіng
• ratіng analysіs
• remote
• post-covid
• software
• employee evaluatіon
• digital system
• evaluatіon methods
• іnformatіon systems
• machine learning