APPLICATION OF HUMAN RELIABILITY ANALYSIS FOR HUMAN ERROR PREVENTION IN ORGANIZATIONS: A SYSTEMATIC LITERATURE REVIEW

Abstrak

Human error remains the dominant cause of safety incidents across various high-risk sectors, including nuclear, energy, mining, manufacturing, and transportation. Human Reliability Analysis (HRA) has evolved into a comprehensive approach for assessing, predicting, and preventing human failure through probabilistic analysis, human-factor evaluation, and organizational condition assessment. This study conducts a systematic literature review (SLR) of 30 relevant scientific articles to map the development of HRA methodologies, identify key determinants of human error, and formulate prevention strategies based on empirical findings. The study selection process follows PRISMA guidelines and is supported by bibliometric analysis using VOSviewer. The findings reveal that modern HRA methods—such as Bayesian HRA, fuzzy HRA, Monte Carlo simulation, FRAM–CREAM, and Dynamic HRA—are more capable of capturing operator variability, expert-judgment uncertainties, and dynamic work conditions compared with classical deterministic models. Furthermore, individual factors (mental workload, situational awareness, experience), organizational factors (safety leadership, safety culture, coordination quality), and task factors (procedure complexity, time pressure, environmental conditions) interact strongly in shaping the likelihood of human error. The implementation of HRA has proven beneficial for organizations by enabling the redesign of work procedures, enhancing training programs, simplifying human–machine interfaces, and prioritizing interventions at the most critical risk points. Overall, this study reinforces that HRA is a strategic tool that must be integrated into modern safety-management systems to strengthen human-error prevention and improve organizational reliability.