METHODOLOGY FOR BUILDING A MATHEMATICAL MODEL FOR PREDICTING RISKS IN STRATEGIC PLANNING OF OCCUPATIONAL SAFETY MEASURES

Abstract

The article considers the relevance and scientific and methodological foundations of building mathematical models for predicting risks in the field of labor protection at industrial enterprises. It is noted that traditional methods of monitoring and analyzing the state of labor protection are based mainly on retrospective statistics, have limited effectiveness, while the increase in the technical complexity of production, the use of new materials and technological solutions is accompanied by an increase in the levels of occupational risks and potential hazards.The need to transition from reactive to proactive occupational safety management is substantiated, which involves, in particular, the use of regression correlation analysis to form reliable forecasts of the level of occupational risks using constructed mathematical models. The methodology for developing a mathematical forecasting model is presented, which involves the sequential implementation of stages that include determining the predicted indicator, forming an array of initial data, calculating pair correlation coefficients, building a set of regression models, assessing the accuracy of the forecast and choosing the optimal model. Particular attention is paid to the problem of the reliability of statistical data, the need for their pre-processing, as well as the use of scientific approaches in modeling complex production processes. The article provides an example of building a model based on actual data from a metallurgical enterprise. The scientific novelty of the work lies in the formalization of the methodology for building regression correlation forecasting models, which allows integrating mathematical methods into the practice of strategic planning of occupational safety measures. The practical significance of the results obtained lies in the possibility of using the obtained models to increase the efficiency of occupational safety management, optimize resource allocation, minimize occupational risks, and ensure compliance with modern international standards, in particular ISO 45001:2018.The implementation of regression correlation forecasting models contributes to the creation of an integrated risk management system at the enterprise, where preventive measures are planned taking into account forecasts and probabilistic scenarios for the development of production processes. This will allow for the effective allocation of resources, concentrating funding on those areas that provide the maximum impact on reducing the level of risk.