By Peter Velikin, General Manager, CAMP Aviate ERP Solutions
A scheduled inspection running longer than planned is not unusual at an MRO. The work may look straightforward at the outset: structural checks, an avionics update, and a series of routine inspections. Then, three days in, everything changes when a technician removes an access panel and finds corrosion nobody expected. Suddenly nothing is going according to plan. The part needs to be replaced — a quick fix if a spare is on the shelf, weeks of delay if it has to be ordered. The planner now has to shift technician schedules to keep other aircraft on track, build a new repair timeline, and account for hangar space if the aircraft ends up grounded longer than planned.
The operator absorbs the cost too. Crew, insurance, and hangar fees on a business jet do not pause while the aircraft sits on jacks and every unplanned day adds to the bill with nothing to show for it. Most planners have faced this trade before: bump another aircraft, bring in overtime, or absorb the delay.
Versions of this story play out at shops across the industry, and the effects compound. While the aircraft sits, the operator cannot plan around it and the shop cannot use the bay for anything else. For the MRO, it also means capital tied up in surplus parts stocked ‘just in case’ — a hedge against exactly this kind of surprise. Zoom out to a shop running several jobs at once, and the effect compounds. A single disruption rarely stays contained; it reshuffles the schedules of multiple aircraft.
The Answer Is in the Records
Much of this is preventable if MROs leverage the data that already exists in their systems. Every aircraft carries an extensive maintenance record: years of discrepancies, replaced parts and closed squawks, maintained with care to keep air travel the safest modes of transportation. What if an AI model could read all that data — the discrepancies, the replaced parts, the closed squawks and much more — and forecast the repairs yet to come? It’s less far-fetched than it sounds.
A single jet alone comes with extensive history: a part replaced earlier than the type usually requires, a squawk that keeps returning, a repair on the same panel a year ago: - each is a signal of what a technician is likely to find when the panels come off. With those signals surfaced in advance, parts can be staged, tools lined up and the right technicians booked before the aircraft arrives. The likely fix can be mapped out ahead of time rather than after the discovery.
Now imagine that same approach extended across every similar aircraft the shop has worked on. Patterns emerge: a bracket that corrodes at roughly the same age, a malfunction reported across several tails, the same squawk appearing again and again. The planner is no longer guessing from one aircraft’s past and can plan with real foresight. The shelf can hold what is likely to be needed rather than everything that might be, so less capital sits tied up in parts stocked ‘just in case.’
None of this is automatic. It depends on the quality and completeness of the records, and on being honest about what a forecast is: a probability, not a certainty. A prediction that is wrong costs credibility, so the output must show its reasoning and its confidence, so planners can judge it rather than simply accept it.
From Scramble to Schedule
While automation is the motto behind AI implementation in many industries, the same can’t be said about aviation maintenance. With the stakes as high as they are, human judgement is not optional, and return-to-service decisions rest with certified people. AI's greatest value here is predictability: protecting capacity and giving operators and MROs more time to make better decisions.
In a shop that works this way, the corrosion found on day three is not a crisis. It’s already accounted for, the schedule largely holds, and other aircraft stay on track. The technicians also spend less time searching for answers. They can see what fixed the same squawk last time, find the manual or service bulletin section behind the repair, and be alerted to items that deserve a second look before sign-off. The AI here becomes a preserver of experience and a handy assistant, even a mentor: what more experienced colleagues have found and fixed stays available to newer technicians, a form of institutional memory that does not retire.
Taken together, these changes make a real difference in the conversation with operators. They allow for a more realistic first quote, one that states the extra work likely to emerge, with estimates and timelines to match. The operator, in turn, can approve probable additional work before the aircraft is opened and plan upcoming trips around the visit. The result is planned downtime instead of a reactive aircraft-on-ground event, which is easier to manage and to absorb. Planners gain room to act rather than react. They can allocate the right technicians, reserve tools and ask procurement to order likely parts before the aircraft arrives, and they can hold schedule buffers where extra work is probable. Operators, with the same foresight, can decide whether to bring the aircraft in earlier or later.
What Day Three Actually Costs
The corrosion found on day three was always going to be there. What changes is whether it arrives as part of the routine or as an event that disrupts the schedule. The costs of express shipping, ad-hoc technicians and extended hangar occupancy all add up. But the larger cost of knowing too late is trust, lost when a routine inspection stretches into weeks and an operator is on the phone asking why.
Every aircraft comes through the shop and leaves with a record. The cost depends on whether anyone reads it before the panel comes off.
Latest News
