ENTROPY OF SAFETY IN THE OCCUPATIONAL HEALTH AND SAFETY MANAGEMENT SYSTEM
DOI:
https://doi.org/10.32782/3041-2080/2025-4-49Keywords:
safety entropy, negentropic functions, occupational health and safety management system, proactive risk management, injury preventionAbstract
This study explores the concept of safety entropy as a fundamental and comprehensive stochastic indicator that determines the degree of disorder and susceptibility to degradation processes within occupational health and safety management systems (OHSMS). The approach proposed by the authors is based on the principles of classical thermodynamics and the provisions of the international standard ISO 45001:2018, which makes it possible to consider OHSMS as an open socio-technical system operating under the influence of numerous internal and external stochastic factors. An abstract model has been developed to describe the relationship between the level of entropy and non-compliance with safety requirements, which reflects the dynamic behavior of the system and the necessity of maintaining it in a controlled low-entropy state to prevent hazardous events. This study explores introduces a diagnostic checklist designed for the prompt identification of safety entropy symptoms, including process degradation, informational and communication barriers, cultural apathy, knowledge loss, and resistance to organizational change. The application of this tool enables proactive risk management, timely detection of critical points, and implementation of effective preventive measures. The results confirm that the stable functioning of OHSMS is achievable only through continuous import of ordering energy and information, realized via leadership, staff training, systematic monitoring, and well-developed feedback mechanisms. The practical significance of this work lies in the establishment of a scientifically substantiated methodological framework for developing safety strategies, minimizing occupational injuries, and enhancing the resilience of organizational systems. This makes the proposed approach a universal instrument applicable across various industrial and service sectors.
References
Довідник з питань охорони праці : навчальний посібник / авт.-укл. Т. П. Поведа, О. Г. Чорна. Кам’янець-Подільський : Друкарня «Рута, 2021. 116 с.
Istudor N., Ursacescu M., Sendroiu C., Radu I. Theoretical Framework of Organizational Intelligence: A Managerial Approach to Promote Renewable Energy in Rural Economies. Energies 2016, 9, 639. https://doi.org/10.3390/en9080639
Hsu L.-C. A hybrid multiple criteria decision-making model for investment decision making. Journal of Business Economics and Management, 2014. 15(3). 509–529. DOI: https://doi.org/10.3846/16111699.2012.722563
Von Bertalanffy, L. General System Theory: Foundations, Development, Applications; George Braziller: New York, NY, USA, 1968.
Wang T.-W. From general system theory to total quality management. J. Am. Acad. Bus. Camb. 2004, 4, 394–400. [Google Scholar].
Rasberry S. D. Entropy in quality systems. Accredit. Qual. Assur. 2009, 14, 65–66.
Hancock F., Rosas F. E., Mediano P. A., Luppi A. I., Cabral J., Dipasquale O., Turkheimer F. E. May the 4C’s be with you: An overview of complexity-inspired frameworks for analysing resting-state neuroimaging data. J. R. Soc. Interface. 2022. 19. 20220214. DOI: 10.1098/rsif.2022.0214. DOI: 10.1098/rsif.2022.0214
Вarouch G., Ponsignon F. The epistemological basis for quality management. Total Quality Management & Business Excellence. 2016, 27, 944–962. DOI: 10.1080/14783363.2016.1188659
Margolin L. On the Convergence of the Cross-Entropy Method. Annals of Operations Research. 2005, 134, 201–214.
Jia H., Wang L. Introducing Entropy into Organizational Psychology: An Entropy-Based Proactive Control Model. Behav Sci (Basel). 2024 Jan 15. 14(1). 54. https://doi.org/10.3390/bs14010054
Hillen M. A., Gutheil C. M., Strout T. D., Smets E. M. A., Han P. K. J. Tolerance of uncertainty: Conceptual analysis, integrative model, and implications for healthcare. Social Science & Medicine. 2017. 180. 62–75. https://doi.org/10.1016/j.socscimed.2017.03.024
Fabac R. Complexity in organizations and environment – Adaptive changes and adaptive decisionmaking. Interdisciplinary Description of Complex Systems. 2010, 8, 34–48.
Piantadosi S. T., Tily H., Gibson E. The communicative function of ambiguity in language. Cognition 2012, 122, 280–291. https://doi.org/10.1016/j.cognition.2011.10.004
Fellingham J., Lin H. Is Accounting an Information Science? Account. Econ. Law A Conviv. 2020, 10, 1–17. DOI:10.1515/ael-2016-0026
Anupama G., Kesava Rao V. V. S. Some Objective Methods for Determining Relative Importance of Financial Ratios. International Journal of Management. 2019, 10, 76–96.
Neves A., Godina R., Azevedo S. G., Pimentel C., Matias J. C. O. The potential of industrial symbiosis: Case analysis and main drivers and barriers to its implementation. Sustainability. 2019;11:7095. https://doi.org/10.3390/su11247095
Stephen D. G., Dixon J. A., Isenhower R. W. Dynamics of representational change: Entropy, action, and cognition. J. Exp. Psychol. Hum. Percept. Perform. 2009;35:1811DOI: 10.1037/a0014510
Bondar A., Bushuyev S., Bushuieva V., Onyshchenko S. CEUR Workshop Proceedings. CEUR; Aachen, Germany: 2021. Complementary Strategic Model for Managing Entropy of the Organization; pp. 293–302.
Bailey K. D. Boundary maintenance in living systems theory and social entropy theory. Syst. Res. Behav. Sci. Off. J. Int. Fed. Syst. Res. 2008;25:587–597. DOI: 10.1002/sres.933
Kulisiewicz M., Kazienko P., Szymanski B.K., Michalski R. Entropy measures of human communication dynamics. Sci. Rep. 2018. 8. 15697. DOI: 10.1038/s41598-018-32571-3
Kruglanski A. W., Pierro A., Mannetti L., De Grada E. Groups as epistemic providers: Need for closure and the unfolding of group-centrism. Psychol. Rev. 2006. 113. 84. DOI: 10.1037/0033-295X.113.1.84
Lollai S. A. Quality Systems. A Thermodynamics-Related Interpretive Model. Entropy 2017, 19, 418 https://doi.org/10.3390/e19080418
Clausius R. The Mechanical Theory of Heat; Macmillan and Co.: London, UK, 1879.
Методичні вказівки з організації самостійної роботи студентів із дисципліни «Фізична та колоїдна хімія» за освітнім рівнем «Бакалавр» для студентів спеціальності 162 «Біотехнологія та біоінженерія» / уклад. В. С. Проценко. Дніпро : ДВНЗ УДХТУ, 2022. 44 с.
Lollai S.A. Quality Systems. A Thermodynamics-Related Interpretive Model. Entropy 2017, 19, 418. https://doi.org/10.3390/e19080418
Tiezzi E. B. P., Pulselli R. M., Marchettini N., Tiezzi E. Dissipative structures in nature and human systems. In: Brebbia C. A., editor. Design & Nature IV: Comparing Design in Nature with Science and Engineering. WitPress; Boston, MA, USA: 2008. pp. 93–300.
Смерічевський С. Ф., Клімова О.. Business model canvas як універсальна концепція управління бізнесом компанії. Інвестиції: практика та досвід. 2017. № 9. С. 11–14.
Гордевський В. Д., Гукалов О. О. Нескінченно модальні наближені розв’язки рівняння Больцмана. Український математичний журнал. 2017. Т. 69. № 3. С. 311–323.
Abad-Segura E., González-Zamar M.-D., Squillante M. Examining the Research on Business Information-Entropy Correlation in the Accounting Process of Organizations. Entropy 2021, 23, 1493. https://doi.org/10.3390/e23111493
Rényi A. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics. Volume 4. University of California Press; Oakland, CA, USA: 1961. On measures of entropy and information; pp. 547–562.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.