Overview
In Maintenance, Repair and Overhaul (MRO) operations, an aircraft goes AOG the moment a required spare part is unavailable at the time of maintenance, grounding the asset and disrupting planned operations. Variable supplier lead times, frequently changing maintenance schedules and uneven spare availability across sites make these events hard to anticipate. Most organizations end up reacting after the fact and paying for costly emergency procurement and logistics.
To move from reaction to anticipation, the AOG Risk Intelligence workflow, built on the AppsTek Sovereign AI Platform, brings predictive analytics in aviation to AOG management. MRO teams can act before groundings happen, supporting more effective aircraft spare parts inventory management, while maintenance and airworthiness decisions remain with certified personnel.
Key Highlights
- The AOG Risk Intelligence workflow aligns maintenance plans, spare consumption, inventory and supplier lead times across aircraft, sites and time periods.
- Reliability and failure modeling estimates part failure probabilities and projects upcoming spare demand.
- Predictive machine learning forecasts aircraft-level AOG probability and site-level spare shortages over the planning horizon.
- Optimization models recommend pre-positioning, redistribution and expediting under budget and supply constraints, flagging parts behind repeat AOG events.
- LLM-generated explanations keep every recommendation traceable, in a sovereign environment suited to military and government fleet data.