Reducing human error in aircraft maintenance: a dual-framework analysis of smart technologies using SWOT and HFACS in Philippine MROs

Purpose This study investigates the integration of structured human error analysis frameworks with smart technologies in Philippine maintenance, repair and overhaul (MRO) facilities. This study aims to identify recurring error patterns and assess organizational readiness for adopting advanced technologies to enhance aviation safety. Design/methodology/approach A qualitative, multiple-case study design was used. Data were collected through document analysis of maintenance error logs, safety manuals, and audit reports, supplemented by expert interviews with quality assurance personnel and supervisors. Human errors were classified using the Human Factors Analysis and Classification System (HFACS), while SWOT (strengths, weaknesses, opportunities, threats) analysis was applied to evaluate internal strengths and weaknesses, as well as external opportunities and threats related to the adoption of smart technology. Findings Results revealed that unsafe acts and organizational influences were the most prevalent error categories, primarily associated with procedural lapses, supervisory deficiencies, and technician fatigue. The SWOT analysis showed that while Philippine MROs are technologically aware, they are hindered by fragmented infrastructure, skills gaps, and regulatory uncertainties. Mapping HFACS-coded errors with emerging technologies demonstrated that augmented reality mitigates skill-based errors, artificial intelligence supports predictive fatigue management, radio frequency identification improves tool control, and Digital Twins enable proactive planning. Originality/value This study presents a novel integration of HFACS and SWOT as a dual-framework approach for linking human error analysis with strategic technology adoption. The proposed technology-safety road map offers practical advice for regulators, MRO operators, and training institutions to align safety culture with digital transformation. It also establishes a foundation for future research to validate the framework quantitatively and extend its application to military and unmanned aerial vehicle maintenance contexts.