Maintenance Prediction for an Aerospace Software Company

An engagement led by our consulting expert

Aircraft maintenance delays can cost millions, and traditional prediction methods often fall short due to incomplete or inconsistent data. In this case study, we share how one aerospace software provider partnered with us to develop an AI-based maintenance prediction capability that delivers measurable accuracy, de-risks future investment, and supports stronger sales conversations.

Objectives: Build Predictive AI for Smarter Maintenance Planning

The company aimed to:

• Reduce delays in aircraft maintenance
• Optimise spares inventory levels across fleets
• Introduce a reliable, explainable AI feature into their software platform
• Provide clear proof of model performance to prospective clients during sales

The Challenge: Sparse Maintenance Records

Aircraft are often serviced by multiple maintenance providers (MROs), meaning maintenance history is fragmented and inconsistent.
This sparse data environment made it difficult to generate reliable predictions using traditional statistical approaches.

Our client needed a way to extract meaningful insights from partial data, without compromising on accuracy or performance.

The Solution: Machine Learning for Sparse Data + Roadmap to Scale

We collaborated closely with the client’s technical team to explore and implement cutting-edge machine learning approaches built specifically for sparse datasets.

Key outcomes included:

• Two proof-of-concept AI models with strong predictive performance
• Accurate forecasting of maintenance job types and spare parts requirements
• A detailed Technical Roadmap for full-scale platform integration, including long-term model maintenance planning as new data becomes available

The models significantly outperformed traditional statistical methods, validating both the technical approach and the business case for future investment.

The Results: Strong Performance and Sales Enablement

Proof of Effectiveness Upfront

The proof of concept models demonstrated measurably better results than traditional statistical methods. This proof de-risked further investment in integration and scaling and provided quantifiable proof of effectiveness claims for their prospective customers during sales cycles.

Technical Architecture & Partnership

We worked with their technical team and produced a full Technical Roadmap covering integration into their platform and data architecture, with model maintenance steps to sustain the highest prediction rates over time as new data added.

“They took the time to truly understand the nuances of our challenges without any preconceived notions and worked collaboratively with our technical team to architect a solution that addressed those opportunity areas.” – Mark, CTO

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