Smarter, Not Harder: Rethinking Predictive Maintenance in Aviation

Based on the Q&A published in MRO Management with Sander de Bree, Founder & Chief Visionary at EXSYN Aviation Solutions

Ahead of the Predictive Aircraft Maintenance (PAM) Conference returning to Dublin in November, MRO Management asked Sander de Bree, from event sponsor EXSYN Aviation Solutions, his thoughts on the landscape of predictive maintenance.

How do you define predictive maintenance, and where is it delivering the most value for operators today?

Predictive maintenance in aviation is ultimately about anticipating technical events before they occur, using data and technology to shift from reactive or scheduled investigations to timely, condition based actions. But I would stress that predictive is not a one size fits all capability. It is an outcome of maturity, of having the right data, tools and organisation trust in place.

Where we see it adding most value today is in very specific use cases: identifying developing trends in systems with strong historical baselines or critical operational impact. Think of repeaters in the cabin or ATA chapters like 36 (pneumatics) and 27 (flight controls), where early signals can translate directly into fewer AOGs, smoother operations, or longer component lifespans. The key is to keep it simple. Predictive isn’t about predicting everything; it’s about making a few key areas more predictable and reliable.

How is EXSYN Aviation Solutions helping airlines and MROs implement predictive maintenance?

We help operators take a step by step, data first approach. Rather than selling AI magic, we focus on aviation native tools that improve the trust, consistency and usability of aircraft data, because that’s the real foundation for predictive.
A big part of that is helping engineering teams build confidence in their own data. Many CAMO teams still question whether their systems reflect what’s happening on the aircraft. By surfacing discrepancies, standardising inputs and making data traceable, we help restore that trust.

Our platform is modular by design. Some operators start by automating reliability reporting or connecting OEM data streams. Others focus on standardising their records or improving how findings from different systems are correlated. In every case, we help teams work with the data they already have, but in a way that is simpler and more connected, and then predictive becomes a practical layer that makes operational sense.

Some areas include component failure prediction for specific identified critical parts based on maintenance history and flight data, or prediction of material allocation to maintenance bases for line and heavy maintenance.

Airlines often ask how others use these dashboards. The key is ease and relevance. Our tools integrate into daily CAMO workflows, with predictive alerts linked to maintenance events or tasks to make them actionable. Dashboards are intuitive for engineers yet detailed enough for analysts.

Predictive maintenance is complex under the hood, but unless it’s user friendly and easy to explain, it won’t be used, no matter how smart it is. That is a guiding principle in everything we build.

What challenges exist in scaling predictive maintenance across fleets, and how is EXSYN Aviation Solutions helping overcome them?

The main challenge isn’t technology but consistency. Airlines operate diverse fleets with different system maturity and data quality, making it hard to scale one predictive logic across all aircraft types or tail numbers.

Another challenge is trust. Many CAMO and engineering teams still use spreadsheets or manual analyses because past systems lacked transparency and reliability. Predictive tools must prove themselves by being consistent, easy to interpret and grounded in daily operations, with clear logic that teams can validate and act on.

EXSYN Aviation Solutions addresses this by focusing on data continuity. That means ensuring aircraft data remains accessible, usable and comparable across time, system changes, or fleet transitions.
We also prioritise transparency in how predictive logic works, so engineering teams can understand the reasoning behind insights and integrate them confidently into their workflows.

How do you see predictive maintenance evolving over the next few years?

I believe predictive maintenance will become more integrated into daily maintenance control and engineering teams. Already for operators who have merged predictive logic with operational workflows, the practical value is growing.

AI and machine learning will play a growing role, but they must remain tools, not goals. The key is having domain expertise with a system that augments technicians, rather than replaces human judgment. This hybrid approach is where predictive has the best chance of delivering real value.

I also expect regulatory progress to evolve. As predictive tools share more maintenance dependencies, clearer standards, validation and accountability will be needed. Transparency and data integrity will be as important as model sophistication. EASA has already moved with PART IS on information security and launched an AI working group publishing its first views.

Ultimately, predictive maintenance should feel less like a tech initiative and more like a natural evolution of how we manage aircraft health, supported by better data, clearer tools and smarter collaboration between OEMs, airlines and MROs.

Conclusion

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.

If you want to discuss how EXSYN can support your organization, you can schedule a conversation with Sander directly through: www.exsyn.com/contact