Abstract
This article raises the issue of decision support system (DSS) development in enterprises concerning the compensation system (CS). The topic is relevant as the CS is one of the main components in human resource management in business. A key element of such DSSs is CS models that provide predictive analytics. Such models are able to give information about how a particular CS affects output, product quality, employee satisfaction, and wage fund. Thus, the main goal of this article is to obtain a CS statistical model and its formulas for determining the probability densities of resultant indicators. To achieve this goal, the authors conducted several blocks of research. Firstly, mathematical formalization of CS functionality was described. Secondly, a statistical model of CS was built. Thirdly, calculations of CS result indicators were made. Reliable scientific methods were used: black box modeling and statistical modeling. This article proposes a statistical and analytical model. As an example, a piecework-bonus system statistical model is demonstrated. The discussion derives formulas of integral estimations showing the probability density of the resulting CS indicators and the related statistical characteristics. These results can be used to predict the behavior of the workforce. This constitutes the scientific novelty of the study, which will establish significant advances in the development of DSSs in the field of labor economics and HR management.
Highlights
A random variable Q4 is considered that expresses the output of a piecework-bonus
The purpose of this paper was to develop a statistical model of a piecework-bonus compensation system (CS), to be used in the data processing module of the CS decision support system (DSS)
Existing CS models do not take into account the random behavior of CS indicators, which means that the models are of little use
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. With increasing technological progress and the globalization of the economy, wages are becoming an extremely important enabler of economic development. With the rapid improvement of business processes in enterprises, the proportion of intellectual labor is increasing, the legal and regulatory framework is expanding, complex, diverse tasks are being addressed on a daily basis and, the social and labor relations of the employee and employer are changing. Yabanci O. claims: «People are a critical resource and capital for organizations»; it changes «due to the active integration of artificial intelligence» [1]. The enterprise is becoming a complex economic system where management decisions (MD), including those related to human resource management, are continually being made
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