Auditable AI Decision Intelligence for Aviation MRO A KPI Governance Architecture

SeyyedAbdolHojjat MoghadasNian

Aviation Maintenance, Repair and Overhaul (MRO) organizations increasingly possess enterprise resource planning data, inventory records, work-order histories, procurement evidence, quality documentation, finance approvals, and customer commitments, yet many operational decisions remain delayed, fragmented, and weakly auditable. This article develops the Aviation Maintenance, Repair and Overhaul Decision Intelligence Governance Framework (AMRO-DIGF), a design-science architecture for governing artificial intelligence-enabled decision intelligence in safety-critical aviation maintenance environments. The study synthesizes aviation digital-operations guidance, airworthiness and repair-station obligations, AI risk-management standards, design-science theory, aircraft-maintenance AI literature, and a prior author-developed corpus on KPI-governed airline logistics, AISA-L, MRO efficiency, AI performance measurement, stock control, and predictive maintenance. AMRO-DIGF consists of five interdependent layers: data integration and lineage, operational diagnostics, AI-assisted recommendation, human authority and compliance governance, and KPI-based feedback. The contribution is not another dashboard or predictive-maintenance tool; it is a governance architecture that converts fragmented MRO evidence into traceable, human-authorized, compliance-aware, financially disciplined decisions. The article argues that AI creates credible MRO value only when recommendations are connected to evidence quality, bottleneck diagnosis, decision rights, airworthiness boundaries, override records, cybersecurity, and post-decision learning. The paper offers formula-level KPI logic for turnaround risk, bottleneck severity, parts readiness, margin leakage, AI recommendation quality, and governance compliance. It concludes that AMRO-DIGF should be evaluated through digital-twin simulation, expert review, and longitudinal case comparison before any claim of causal performance improvement is made.

Also available via: European Organization for Nuclear Research