A Systematic Review of Methodologies for Assessing the Impact of Human and Organizational Factors on Safety Barrier Performance in Process Industries - Alireza Azarmehri; Ali Karimi; Omran Ahmadi (بهداشت و ایمنی کار)
Introduction: Barriers play a critical role in mitigating risks and preventing catastrophic incidents in process industries. Human and Organizational Factors (HOFs) significantly influence the performance of safety barriers. This systematic review investigates existing frameworks and methods for assessing the impact of HOFs on safety barrier performance. Material and Methods: A systematic search was conducted across the Scopus and Web of Science databases, following the PRISMA guidelines. The search aimed to identify studies presenting methodologies for evaluating the influence of HOFs on safety barrier performance in process industries. Data were subsequently extracted from the 16 included studies. Results: The 16 studies included in this research presented various methods and frameworks examining the impact of HOFs on different types of safety barriers, including technical, operational, and human barriers, across industries such as oil and gas, chemical, and steel. Barrier and Operational Risk Analysis (BORA) emerged as the predominant framework among the studies. Research on operational and human barriers, which depend on human actions and procedures, frequently identified factors such as competence, training, communication, and supervision as key influencers of performance. In contrast, studies on technical barriers highlighted the importance of assessing factors such as maintenance management and procedural compliance. Conclusion: This research highlights the critical role of HOFs in safety barrier performance within process industries. By systematically reviewing existing methodologies, the study identified their strengths and weaknesses. Findings underscore the need to account for uncertainties in expert judgments and the interplay between HOFs in evaluation models. The integration of fuzzy logic and Bayesian networks is proposed to enhance evaluation processes. Future research should prioritize the development of unified frameworks that address the limitations of current approaches while expanding their applicability across diverse industries.