Strategic Aftermarket Modelling (SAM) (Phases 1 & 2)

This project advances predictive maintenance in aerospace by building a second phase of AI-driven demand forecasting models for engine part replacements. Five interconnected AI modules are being developed to deliver increasingly granular insights, from engine serial number–level part replacement predictions and time-on-wing estimations, to fleet-wide trend detection and unscheduled maintenance forecasting. The system will also feature “what if” scenario simulations to support cost sensitivity analysis, and a reinforcement learning layer enhanced by human feedback to continuously refine outputs. These models are underpinned by a suite of custom-built data products, including component genealogy, interchangeability, and service bulletin configuration by engine.