Abstract
It is always acknowledged that industrial accidents usually happen due to human error, and estimates quote almost 80 per cent of accidents happening due to operator, a hole in the procedure or system bypassing. These mistakes not only undermine the safety of the workers but also cause huge economic losses, harm the environment, and tarnish the reputation. Conventional safety management systems that rely profoundly on monitoring safety compliance checklists and reactive measures have failed to respond adequately to the potential of current industrial risks.
This paper presents a case study of the importance of custom, risk-specific health, safety, security, environment, and quality (HSSEQ) software solutions in reducing the occurrence of human error and avoiding the bypass of safety systems. Based on the principles of human factors engineering and inherently safer design, the solutions combine real-time monitoring, multi-channel alert and notification, AI-driven hazard detection, digital permit-to-work (PTW) systems, and automated compliance reporting into one integrated platform. Such systems can provide multiple lines of defence, using predictive analytics and scenario-based safety validations, and automating procedures, to minimise the possibility of operational errors growing into accidents.
The evaluations will be performed based on the synthesis of empirical reports, case scenarios in the industry, and technical specifications, including core basics of the components, integration planning with legacy infrastructure, regulatory conformance architecture, training, and implementation models. Results show that the organizations that introduce such systems report as much as a 30 percent decline in accidents at workplaces, an 82 percent increase in retention rates of employees due to an increase in safety culture, and vast savings in costs, attaining an average of $2.00 on the dollar spent on safety schemes. In addition, the feature of safety data centralization, predictive risk detection, and ensuring that businesses will always comply with safety standards like OSHA, ISO 14001, and ISO 45001, ensures that the software will be a deciding element in enhancing industrial safety performance.
Keywords
- Digital Competency
- Employee Agility
- Employee Performance
- Higher Education Institutions
References
- 1. Abdeljalil, A., Nabil, S., & Rachid, M. (2022). Contribution to developing a new environmental risk management methodology for industrial sites. Journal of Applied and Natural Science, 14(1), 9โ16. https://doi.org/10.31018/jans.v14i1.3205
- 2. Adam, A., Saffaj, N., & Mamouni, R. (2023). The reliability of evaporation ponds as a final basin for industrial effluent: Demonstration of an environmental risk management methodology. MethodsX, 10. https://doi.org/10.1016/j.mex.2023.102055
- 3. Allian, A. D., Shah, N. P., Ferretti, A. C., Brown, D. B., Kolis, S. P., & Sperry, J. B. (2020). Process safety in the pharmaceutical industry-part I: Thermal and reaction hazard evaluation processes and techniques. Organic Process Research and Development, 24(11), 2529โ2548. https://doi.org/10.1021/acs.oprd.0c00226
- 4. Amyotte, P. R., & Khan, F. I. (2021). The role of inherently safer design in process safety. Canadian Journal of Chemical Engineering, 99(4), 853โ871. https://doi.org/10.1002/cjce.23987
- 5. Deore, S. S., Pawar, A. S., Divakaran, P., Maindargi, S. C., Paliwal, S., & Gaikwad, V. S. (2024). Machine Learning-powered Threat Detection: Mitigating Cybersecurity Challenges. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 373โ385.
- 6. Fu, G., Xie, X., Jia, Q., Tong, W., & Ge, Y. (2020). Accidents analysis and prevention of coal and gas outburst: Understanding human errors in accidents. Process Safety and Environmental Protection, 134, 1โ23. https://doi.org/10.1016/j.psep.2019.11.026
- 7. Gajek, A., Fabiano, B., Laurent, A., & Jensen, N. (2022). Process safety education of future employee 4.0 in Industry 4.0. Journal of Loss Prevention in the Process Industries, 75. https://doi.org/10.1016/j.jlp.2021.104691
- 8. HUANG, F., & LIU, B. (2017). Software defect prevention based on human error theories. Chinese Journal of Aeronautics, 30(3), 1054โ1070. https://doi.org/10.1016/j.cja.2017.03.005
- 9. Hu, W., Carver, J. C., Anu, V., Walia, G. S., & Bradshaw, G. L. (2018). Using human error information for error prevention. Empirical Software Engineering, 23(6), 3768โ3800. https://doi.org/10.1007/s10664-018-9623-8
- 10. Khan, F., Amyotte, P., & Adedigba, S. (2021). Process safety concerns in process system digitalization. Education for Chemical Engineers, 34, 33โ46. https://doi.org/10.1016/j.ece.2020.11.002
- 11. Khoruzhy, L. I., Katkov, Y. N., Katkova, E. A., Khoruzhy, V. I., & Dzhikiya, M. K. (2023). Opportunities for the Application of a Model of Cost Management and Reduction of Risks in Financial and Economic Activity Based on the OLAP Technology: The Case of the Agro-Industrial Sector of Russia. Risks, 11(1). https://doi.org/10.3390/risks11010008
- 12. Kirkendall, E. S., Timmons, K., Huth, H., Walsh, K., & Melton, K. (2020, November 1). Human-Based Errors Involving Smart Infusion Pumps: A Catalog of Error Types and Prevention Strategies. Drug Safety. Adis. https://doi.org/10.1007/s40264-020-00986-5
- 13. Kudryavtsev, S. S., Yemelin, P. V., & Yemelina, N. K. (2018). The Development of a Risk Management System in the Field of Industrial Safety in the Republic of Kazakhstan. Safety and Health at Work, 9(1), 30โ41. https://doi.org/10.1016/j.shaw.2017.06.003
- 14. Lee, J., Cameron, I., & Hassall, M. (2022). Information needs and challenges in future process safety. Digital Chemical Engineering, 3. https://doi.org/10.1016/j.dche.2022.100017
- 15. Li, J., Goerlandt, F., Reniers, G., Feng, C., & Liu, Y. (2022). Chinese international process safety research: Collaborations, research trends, and intellectual basis. Journal of Loss Prevention in the Process Industries, 74. https://doi.org/10.1016/j.jlp.2021.104657
- 16. Liu, Y., Huang, A., Luo, Y., Huang, H., Liu, Y., Chen, Y., โฆ Yang, Q. (2020). Fedvision: An online visual object detection platform powered by federated learning. In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence (pp. 13172โ13179). AAAI press.
- 17. Liu, Y., Huang, A., Luo, Y., Huang, H., Liu, Y., Chen, Y., โฆ Yang, Q. (2020). FedVision: An online visual object detection platform powered by federated learning. In Proceedings of the 32nd Innovative Applications of Artificial Intelligence Conference, IAAI 2020 (pp. 13172โ13179). AAAI Press. https://doi.org/10.1609/aaai.v34i08.7021
- 18. Patel, P., Harmon, S., Iseman, R., Ludkowski, O., Auman, H., Hawley, S., โฆ Jamaspishvili, T. (2023). Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study. Modern Pathology, 36(10). https://doi.org/10.1016/j.modpat.2023.100241
- 19. Qian, Y., Vaddiraju, S., & Khan, F. (2023). Safety education 4.0 โ A critical review and a response to the process industry 4.0 need in chemical engineering curriculum. Safety Science, 161. https://doi.org/10.1016/j.ssci.2023.106069
- 20. Sameera, V., Bindra, A., & Rath, G. (2021, July 1). Human errors and their prevention in healthcare. Journal of Anaesthesiology Clinical Pharmacology. Wolters Kluwer Medknow Publications. https://doi.org/10.4103/joacp.JOACP_364_19
- 21. Siuta, D., Kukfisz, B., Kuczyลska, A., & Mitkowski, P. T. (2022). Methodology for the Determination of a Process Safety Culture Index and Safety Culture Maturity Level in Industries. International Journal of Environmental Research and Public Health, 19(5). https://doi.org/10.3390/ijerph19052668
- 22. Swuste, P., Theunissen, J., Schmitz, P., Reniers, G., & Blokland, P. (2016, March 1). Process safety indicators, a review of literature. Journal of Loss Prevention in the Process Industries. Elsevier Ltd. https://doi.org/10.1016/j.jlp.2015.12.020
- 23. Vasylyshyna, L., Popova, O., Hoholieva, N., Lyzunova, O., Medvedieva, M., Laskavets, K., โฆ Shevchenko, S. (2022). ASSESSMENT OF THE IMPACT OF DIGITALIZED MANAGEMENT ON THE FINANCIAL RISKS OF INDUSTRIAL ENTERPRISES. Eastern-European Journal of Enterprise Technologies, 6(13โ120), 87โ95. https://doi.org/10.15587/1729-4061.2022.268024
- 24. 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. https://doi.org/10.1016/j.ssci.2021.105647
- 25. Zhu, H. P., Shen, W. A., Lei, Y., Yuan, Y., Hu, Y. H., & Zhang, Y. (2020, January 1). Performance monitoring, evaluation, and improvement of structural vibration mitigation or isolation systems. Gongcheng Lixue/Engineering Mechanics. Tsinghua University. https://doi.org/10.6052/j.issn.1000-4750.2019.05.ST06