The evolution of MRO data in aviation

Before you can predict anything, you need clean, structured data.

One common blocker? PDF-based shop reports. At many airlines, components are sent to external shops (e.g. Honeywell, RTX) and results come back in scanned PDFs. Technicians then manually enter the failure information into AMOS.

Using OCR and process automation, we can extract the failure type and reason and sync it with AMOS instantly.

Most MRO data lives across AMOS, ERP systems, spreadsheets and emails. Without a common structure, analytics are difficult or impossible. That’s why integrating and mapping key data points (such as failure types, part numbers, usage hours and shop visit results) across systems is essential.

Standardization also means applying the same data definitions across departments. For example, what counts as a “failure" in a shop report must be aligned with how AMOS classifies it in maintenance records.

Once data is digital and structured, it can be analyzed. Using machine learning, airlines can identify patterns such as:

• Which parts are most likely to fail in specific conditions?
• How does flight profile affect component wear?
• When should you replace a part before it fails?

Predictive maintenance is the outcome of reliable data and smart modeling. If you’re ready to shift from reactive to predictive let’s talk about how to unlock the value hidden in your MRO data. Our team has helped companies bridge the gap between disconnected logs and actionable predictions. Let us show you what’s possible with your data.