Integration of Human Factors Analysis and Safety Management Systems in Aviation Maintenance: A Multi-Dimensional Approach to Error Reduction

This study proposes and evaluates a novel framework for integrating Human Factors (HF) analysis with Safety Management Systems (SMS) in aviation maintenance to reduce errors and enhance safety. Utilizing a mixed-methods approach, data was gathered from 14 military and civilian organizations, including analysis of 378 maintenance error reports and 42 interviews with personnel. Methodologies included the HFACS-ME taxonomy for error classification, digital task load monitoring, cognitive process mapping, and physiological stress biomarker analysis. Key findings revealed a 37% reduction in errors in organizations with high HF-SMS integration and identified three critical intervention points: task planning, documentation review, and post-maintenance verification. A machine learning-based predictive model achieved 83% accuracy in forecasting error-prone scenarios. The integrated approach significantly improved safety culture and proactive reporting. This research provides practical tools and guidance for systematically embedding human factors into SMS to strengthen risk management in aviation maintenance.