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EXSYN has deployed Predictive Spare Parts Planning at Volotea, enabling long-term, data-driven forecasting of material demand. By combining maintenance program data with historical unscheduled events, Volotea can optimise procurement, reduce AOG risk, and improve fleet reliability. EXSYN demonstrates how predictive aviation data directly drives cost efficiency and operational resilience.
Predictive maintenance is advancing quickly, and the aviation sector is learning how to apply it in practical and operationally meaningful ways. EXSYN remains closely involved in supporting airlines and MROs as they strengthen their data foundation and introduce predictive capabilities into daily workflows.
Our recent webinar walked through a full working day for a CAMO team and showed, step-by-step, how automation apps shift effort from manual firefighting to repeatable, auditable processes. Sander de Bree, Robert Vermeij and Carlos Llopis led the session with live demos of the EXSYN Apps and ran real workflows records so engineers could see exactly how decisions are made and validated.
EXSYN has launched a new modular platform that unites its established products and services under one framework designed to deliver aviation data continuity. Each app can now be licensed separately. Operators can start with the apps that solve today’s most pressing challenges and expand step by step, without large-scale system overhauls.
MMP/OMP revisions can become locked when the Maintenance Program control flag activation_blocker_oprev is set to a non-zero OPREV ID, preventing edits, activation, or progression even if the OPREV is stale. Fix by backing up the affected rows, resetting activation_blocker_oprev to 0 in a transaction, and refreshing the MMP “Operator Maintenance Program” window to clear caches and re-sync the UI.
Predictive efforts stall at handovers. The cure is a single flow: OEM library for documented truth, AD integration for a shared regulatory picture, M&E health checks and airworthiness reviews for validated data. Reliability and engine health then run on leg- and serial-linked signals, refreshed by a scheduler. Result: stable KPIs, cleaner trends, fewer false positives.
Predictive maintenance succeeds only when people believe what the data is telling them. Engineers, reliability analysts, and CAMO staff do not reject models because they dislike innovation. They pause because their lived experience has taught them that small inconsistencies grow into audit findings, deferred defects, and avoidable AOGs.
Predictive maturity starts with disciplined reliability data, not telemetry or algorithms. Clean and connected M&E records create the only foundation models can stand on. Without that layer, prediction turns into noise instead of operational intelligence.