Improving a process of managing dynamic occupational risks

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Authors:


V.A.Tsopa, orcid.org/0000-0002-4811-3712, International Institute of Management, Kyiv, Ukraine

S.I.Cheberiachko, orcid.org/0000-0003-3281-7157, Dnipro University of Technology, Dnipro, Ukraine

O.O.Yavorska*, orcid.org/0000-0001-5516-5310, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.V.Deryugin, orcid.org/0000-0002-2456-7664, Dnipro University of Technology, Dnipro, Ukraine

О.M.Borovytskyi, orcid.org/0000-0003-1111-7960, Dnipro University of Technology, Dnipro, Ukraine

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


повний текст / full article



Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2023, (4): 110 - 117

https://doi.org/10.33271/nvngu/2023-4/110



Abstract:



Purpose.
To improve the process of managing dynamic occupational risks, which considers changes in time in hazardous factors of the organization’s environment in the occupational safety and health management system.


Methodology.
To improve the process of managing occupational risks, we have applied a well-known “Bow-Tie” model (ISO 31010:2018). The model allows assessing occupational risks as the product of the probability of hazardous event occurrence and severity of the consequences, taking into account the influence of hazardous external and internal factors, hazardous actions or dangerous inactions, which, according to the requirements of Clause 4.1 of the ISO 45001:2018 standard, are interconnected and subject to the influence of time.


Findings.
A model of the connection of hazardous factors of the internal and external environment of an organization, related to their negative influence on the growing probability of hazardous event (incident) occurrence and a degree of severity in time, has been developed. The process of managing occupational risks is proposed, taking into account changes in the time of exposure to hazardous factors, which will allow determining the acceptability or unacceptability of the occupational risk in time. The analysis of changes in occupational risks is proposed to be considered in the following time intervals (specifically in those where there is a corresponding change in risk factors): time of the day, day of the week, month of the year, quarter, half year, year, years etc. All the proposed professional risks were divided into two groups of professional risks considering the changes in their levels in time: static and dynamic ones. To calculate the occupational risk level, it is also proposed to determine all combinations of hazardous factors that can occur simultaneously in time within the corresponding intervals of the time under analysis.


Originality.
It has been determined that identification of the acceptable level of an occupational risk in the maximum combination of all hazardous factors acting simultaneously at a certain point in time will lead to the fact that all other combinations of hazardous factors will also have an acceptable level of occupational risk. This provision follows from the fact that the level of occupational risk from a smaller number of hazardous factors will not exceed the indicator of occupational risk from the exposure to a larger number of hazardous factors in time.


Practical value.
The forms for dynamic occupational risk assessment have been developed; a matrix has been proposed for determining the number of combinations of hazardous factors acting simultaneously in time.



Keywords
: hazardous factor, incident, occupational risk, safety of work

References.


1. Paltrinieri, N., Scarponi, G. E., Khan, F., & Hauge, S. (2014). Addressing dynamic risk in the petroleum industry by means of innovative analysis solutions. Chemical Engineering Transactions, 36, 451-456. Retrieved from https://www.semanticscholar.org/paper/Adressing-dynamic-risk-in-the-petroleum-industry-Paltrinieri-Scarponi/1756d8c41cd76a694ab912488642c34708f11056.

2. Cheberiachko, S., Yavorska, O., Deryugin, O., Lantukh, D., Bas, I., Kruzhilko, O., & Melnyk, V. (2023). Improving safety of passenger road transportation. Transactions on transport sciences, 14(2), 1-10. https://doi.org/10.5507/tots.2023.003.

3. Lee, S., Landucci, G., Reniers, G., & Paltrinieri, N. (2019). Validation of Dynamic Risk Analysis Supporting Integrated Operations Across Systems. Sustainability, 11, 6745. https://doi.org/10.3390/su11236745.

4. Wahyu, N. C., Trika, P., & Bagus, D. (2020).  Risk Analysis Using Job Safety Analysis-Fuzzy Integration for Ship Maintenance Operation. IPTEK The Journal for Technology and Science, 31(3), 327-342. https://doi.org/10.12962/j20882033.v31i3.5655.

5. Hu, Y. (2019). A New Mode of HSE Risk Management for Construction Projects. Risk Management in Construction Projects. https://doi.org/10.5772/intechopen.84358.

6. Pivnyak, G., Samusia, V., Oksen, Y., & Radiuk, M. (2014). Parameters optimization of heat pump units in mining enterprises. Progressive Technologies of Coal, Coalbed Methane, and Ores Mining, 19-24. https://doi.org/10.1201/b17547-5.

7. Misiurek, K., & Misiurek, B. (2020). Improvement of the safety and quality of a workplace in the area of the construction industry with use of the 6S system. International Journal of Occupational Safety and Ergonomics, 26(3), 514-520. https://doi.org/10.1080/10803548.2018.1510564.

8. Villa, V., Paltrinieri, N., & Cozzania, V. (2015). Overview on Dynamic Approaches to Risk Management in Process Facilities. Chemical engineering transactions, 43, 2497-2502. https://doi.org/10.3303/CET1543417.

9. Khakzad, N., Khan, F. I., & Paltrinieri, N. (2014). On the application of near accident data to risk analysis of major accidents. Reliability Engineering & System Safety, 126, 116-125. https://doi.org/10.1016/j.ress.2014.01.015.

10. Bondarenko, V., Tabachenko, M., & Wachowicz, J. (2010). Possibility of production complex of sufficient gasses in Ukraine. New Techniques and Technologies in Mining, 113-119. https://doi.org/10.1201/b11329-19.

11. Goerlandt, F., Khakzad, N., & Reniers, G. (2017). Validity and validation of safety-related quantitative risk analysis: A review. Safety Science, 99, 127-139. https://doi.org/10.1016/j.ssci.2016.08.023.

12. Yang, X., Haugen, S., & Paltrinieri, N. (2018). Clarifying the concept of operational risk assessment in the oil and gas industry. Safety Science, 108, 259-268. https://doi.org/10.1016/j.ssci.2017.12.019.

13. Pasman, H. J., Rogers, W. J. & Mannan, M. S. (2017). Risk assessment: What is it worth? Shall we just do away with it, or can it do a better job? Safety Science, 99(Part B), 140-155. https://doi.org/10.1016/j.ssci.2017.01.011.

14. Landucci, G., & Paltrinieri, N. (2016). Dynamic evaluation of risk: From safety indicators to proactive techniques. Chemical engineering transactions, 53, 169-174. https://doi.org/10.3303/CET1653029.

15. Liu, R., Liu, H.-C., Shi, H., & Gu, X. (2023). Occupational health and safety risk assessment: A systematic literature review of models, methods, and applications. Safety Science, 160, 106050. https://doi.org/10.1016/j.ssci.2022.106050.

16. Paltrinieri, N., Comfort, L., & Reniers, G. (2019). Learning about risk: Machine learning for risk assessment. Safety Science, 118, 475-486. https://doi.org/10.1016/j.ssci.2019.06.001.

17. Bazaluk, O., Koriashkina, L., Cheberyachko, S., Deryugin, O., Odnovol, M., Lozynskyi, V., & Nesterova, O. (2022). Methodology for assessing the risk of incidents during passenger road transportation using the functional resonance analysis method. Heliyon, 8(75), e11814. https://doi.org/10.1016/j.heliyon.2022.e11814.

18. Bucelli, M., Paltrinieri, N., & Landucci, G. (2018). Integrated risk assessment for oil and gas installations in sensitive areas. Ocean Engineering, 150, 377-390. https://doi.org/10.1016/j.oceaneng.2017.12.035.

19. Andersen, S., & Mostue, B. A. (2012). Risk analysis and risk management approaches applied to the petroleum industry and their applicability to IO concepts. Safety Science, 50(10), 2010-2019. https://doi.org/10.1016/j.ssci.2011.07.016.

20. Foss, B. (2012). Real-time production optimization and reservoir management at the IO center. IFAC Proceedings Volumes, 45(8), 7-12. https://doi.org/10.3182/20120531-2-NO-4020.00048.

21. Yuan, S., Yang, M., Reniers, G., Chen, C., & Wu, J. (2022). Safety barriers in the chemical process industries: A state-of-the-art review on their classification, assessment, and management. Safety Science, 148, 105647. https://doi.org/10.1016/j.ssci.2021.105647.

22. Villa, V., Paltrinieri, N., Khan, F., & Cozzani, V. (2016). Towards dynamic risk analysis: A review of the risk assessment approach and its limitations in the chemical process industry. Safety Science, 89, 77-93. https://doi.org/10.1016/j.ssci.2016.06.002.

23. Paltrinieri, N., & Reniers, G. (2017). Dynamic risk analysis for Seveso sites. Journal of Loss Prevention in the Process Industries, 49, 111-119. https://doi.org/10.1016/j.jlp.2017.03.023.

24. Loh, T. Y., Brito, M.P., Bose, N., Xu, J., & Tenekedjiev, K. (2019). A Fuzzy-Based Risk Assessment Framework for Autonomous Underwater Vehicle Under-Ice Missions. Risk analysis: an official publication of the Society for Risk Analysis, 39(12), 2744-2765. https://doi.org/10.1111/risa.13376.

25. Georgousoglou, K., Mouzakitis, Y., & Adamides, E. D. (2022). The application of the Bow Tie approach in the risk assessment of a municipal solid waste management system. IOP Conference Series: Earth and Environmental Science, 1123, 012073. https://doi.org/10.1088/1755-1315/1123/1/012073.

26. Oakley, P. A., & Harrison, D. E. (2020). Death of the ALARA Radiation Protection Principle as Used in the Medical Sector. Dose Response, 18(2), 1559325820921641. https://doi.org/10.1177/1559325820921641.

27. Bernsmed, K., Frøystad, C., Meland, P. H., Nesheim, D. A., & Rødseth, O. J. (2018). Visualizing Cyber Security Risks with Bow-Tie Diagrams. In Liu P., Mauw S., & Stolen K. (Eds.) Graphical Models for Security. GraMSec 2017. Lecture Notes in Computer Science, 10744. Springer, Cham. Retrieved from https://ntnuopen.ntnu.no/ntnu-xmlui/bitstream/handle/11250/2489562/Visualizing+cyber.pdf?sequence=1.

28. Kuznietsova, N. V. (2016). Identification and dealing with uncertainties in the form of incomplete data by data mining methods. System research & information technologies, 2, 104-115. https://doi.org/10.20535/SRIT.2308-8893.2016.2.10.

29. Ehrlich, P. R., & Ehrlich, A. H. (2013). Can a collapse of global civilization be avoided?. Proceedings. Biological sciences, 280(1754), 20122845. https://doi.org/10.1098/rspb.2012.2845.

30. Cameron, P. J. (2017). Notes on Counting: An Introduction to Enumerative Combinatorics. Cambridge University Press. ISBN 978-110841736.

31. Pivnyak, G., Dychkovskyi, R., Bobyliov, O., Cabana, E. C., & Smoliński, A. (2018). Mathematical and geomechanical model in physical and chemical processes of underground coal gasification. Solid State Phenomena, 277, 1-16. https://doi.org/10.4028/www.scientific.net/ssp.277.1.

 

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ISSN (print) 2071-2227,
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Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

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