Aircraft maintenance has always been data-based. Every flight hour, every defect, every removal gets logged. But there’s a difference between using data for documentation and using it to drive decisions.Now more than ever, operators need to be thoroughly prepared before embarking on their next visit. Supply chain constraints, competitive parts market, production hurdles, and reliability issues, have all contributed to long queues and rising service and material costs.

Maintenance costs are rising and now account for a significant portion of an airline's operating expenses. Spare part prices are increasing rapidly, with supply chain issues also cited as factors in Jetstar Asia's base closures and Azul's recent Chapter 11 filing. Additionally, constraints on available shop visit slots are adding to the challenge. In this environment, operators are under growing pressure to act more strategically.

One approach to this is going beyond routine execution, using data not just to close tasks, but to optimize how maintenance decisions are made.

How Data Is Already Used to Drive Maintenance Decisions

Data already supports a wide range of day-to-day decisions in airline maintenance, here are a few examples where data plays a direct role in shaping what gets done and when:

1. Component Reliability

   Metrics like MTBUR (Mean Time Between Unscheduled Removals) and numbers of removals in a certain FH/FC are reviewed regularly and compared against both global benchmarks and operator-defined thresholds. When a component’s performance falls below this threshold, it enters Alert Status, prompting the issuance of a Component Alert Notice and Reliability Summary Reports, or equivalent document outlining the potential root cause and required follow-up actions.

   
   

   This can result in engineering decisions such as:

   • Adjusting task intervals

   • Introducing a preventive replacement strategy

   • Increasing spares stock for high-failure items

   • Escalating to the OEM or cross-check for similar cases

   • Minimum Stock Level Calculation

     
     

2. Aircraft Reliability

   Reports such as from MAREPs, PIREPs, and dispatch reliability data (and technical delays if any) are analyzed and codified by reliability engineers according to ATA chapter and subchapter. If a specific ATA system appears frequently, it will trigger alert and the issuance of Aircraft Follow On Notice (or similar reports). 

   
   

   This data is used to:

   • Identify systems that are consistently causing delays and analyze for any underlying issue

   • Recommend inspections, additional checks, or other troubleshooting methods

   • Support escalation to OEM or reference similar issues from past case data (such as via MyBoeingFleet)

3. Engine Health Monitoring

   Engines are monitored continuously using sensor data such as EGT margin, vibration levels, oil consumption, and fuel flow. These parameters are tracked over time to detect early signs of degradation.

   
   

   When anomalies are detected, planners may:

   • Advance borescope inspections

   • Schedule removals ahead of AOG risk

   • Flag the engine for deeper evaluation if say there are anomalies in oil consumption for a certain period

   • Recommend inspections, additional checks, or other troubleshooting method

   
   

4. Engine Staggering Plan

   Operators use LLP life tracking, daily utilization rates, and engine performance trends to decide when to remove engines. Some engines may be scheduled for shop visits earlier than technically necessary to avoid too many engine shop visits during the period, both for engine availability as well as to spread out shop visit expenses and support financial projections.

The Next Step: Predictive Maintenance

Predictive maintenance is often seen as the next evolution in aviation data use, shifting from reactive measures and historical reliability data to anticipating failures before they impact operations. This approach promises smarter decision-making from early removals to prevent AOGs, to inventory planning based on failure probabilities. Financially, it allows for better cost forecasting, optimizing spare parts inventory, reducing unplanned shop visits, and ultimately minimizing downtime and operational disruptions.

But reaching that level of insight depends on a critical assumption, that the underlying data is consistent, structured, and meaningful. And for most operators, that’s still a major challenge.

The Data Homework: No Single Source of Truth

Predictive tools rely on structured and unified data, but in practice, most operators face issues like:

• Different interpretations across teams

  The same issue is described differently by mechanics, engineers, and planners.

  Example: “Engine dripping,” “oil leak,” and “out of service” all refer to the same defect but are logged as separate events.

  
  

• Inconsistent terminology

  Variations in abbreviations, spelling, or naming prevent data from being grouped reliably.

  Example: “ENG OIL LEAK” vs. “oil leaking” vs. “engine fluid loss.”

• Non-standard data formatting

  Some systems use codes and structured fields, others rely on free text.

  Example: Some logs may be coded, while AMLs are handwritten with mixed formats.

• Data spread across multiple platforms

  Information lives in SAP, maintenance software, Excel, PDFs, emails, to physical logbooks.

  Example: The defect is in the AML, the removal is in SAP, and the analysis is in an emailed report.

Human Intervention is still needed to some extent, at least for now. Engineers are still responsible for verifying input data, what should be included or excluded, identify outliers and interpret results in context to avoid drawing the wrong conclusions from raw trends. This is why domain expertise remains essential. Experience helps determine which signals are operationally relevant and which can be disregarded.

Our team includes experienced professionals in reliability and maintenance planning who understand the technical and operational realities on the ground. If you're looking to optimize your current processes or need support interpreting your fleet data effectively, reach out to us at info@tbmaviation.com to start the conversation.