Innovation in Company Labor Productivity Management: Data Science Methods Application

Abstract

The article considers the challenge of labor productivity growth in a company using objective data about economic, demographic and social factors and subjective information about an employees’ health quality. We propose the technology for labor productivity management based on the phased data processing and modeling of quantitative and qualitative data relations, which intended to provide decision making when planning trajectories for labor productivity growth. The technology is supposed to use statistical analysis and machine learning, to support management decision on planning health-saving strategies directed to increase labor productivity. It is proved that to solve the problem of employees’ clustering and design their homogeneous groups, it is properly to use the k-means method, which is more relevant and reliable compared to the clustering method based on Kohonen neural networks. We also test different methods for employees’ classification and predicting of a new employee labor productivity profile and demonstrate that over problem with a lot of qualitative variables, such as gender, education, health self-estimation the support vector machines method has higher accuracy.