Scientific Support of Occupational Risk Management Decisions in Industrial Sectors in Case of Uncertainty

Abstract

Introduction: One of the most important steps in risk assessment is the selection of assessment methods. Traditionally, when developing measures to reduce the level of occupational morbidity and industrial injuries, the results of an analysis of the causes, types of events and other factors that led to accidents are used. But such an approach does not meet modern requirements. For an adequate assessment of occupational risks, it is necessary to have objective data from different time periods: the onset of traumatic events in the past, the current state of threats to life and health of people, and the future state of threats in industrial sectors.

Methods: Mathematical modeling remains the main means of scientific support for occupational risk management. The Elmeri system was chosen for occupational risk assessment in this study, which can be easily and quickly used in any industry and in enterprises of all sizes. A critical analysis of various approaches to managing occupational risks in enterprises was applied to identify the strengths and weaknesses of these approaches. The method of generalizing the most effective approaches to occupational risk management was applied to develop the algorithm of occupational risk management decisions in industrial sectors in case of uncertainty.

Results: An occupational risk management algorithm has been developed to substantiate management decisions on planning measures to reduce risk, the implementation of which ensures the effectiveness of measures aimed at reducing risk. Research has shown that if the decision-making situation is characterized by conditions of uncertainty (it is impossible to obtain mathematical models of acceptable accuracy), the assessment of the predictive values of occupational risk is carried out exclusively by an expert. Thus, the occupational risk indicator used at the final stage of planning activities determines the degree of achievement of the result of solving the task.

Conclusion: It has been established that in conditions of uncertainty (lack of necessary data or available data are incomplete or unreliable), experts involved in solving the problem of risk management use their own knowledge and experience in solving similar problems. As a promising direction for further research, it should be noted the development of a methodology for a comprehensive assessment of the effectiveness of operational management decisions for planning and implementing measures to reduce risks