Data Analytics in the Aviation Industry

Predictive Maintenance in Aviation: Failing to Plan is Planning to Fail

Scheduling maintenance is critical to fleet management, aircraft safety and fleet readiness. Aviation experts know that scheduled maintenance alone is not enough to ensure performance and fleet optimization.

An unplanned event in aviation can have a long term negative impact to your organization including loss of revenue due to downtime, damage to reputation and inability to meet the needs of your customers.

For over ten years, leaders in aviation have partnered with Freya Systems, a software and data analytics firm that specializes in design, development, testing and deploying custom enterprise applications for workflow, decision support and aircraft maintenance.

Freya’s data analytics team utilizes leading edge technologies including, Python, Studio, Jupyter for algorithm development, statistical model development, predictive analysis and data modeling to deliver reliable, accurate data.


aviation technician

The predictive analysis is reviewed in partnership with the customer to uncover the best solution. Freya Systems then starts work on the software development, applying Angular, Java, JavaScript, Ionic and Solt to build Agile software, Java Web application development and custom application deployment.

The Results of Data Analytics in Aircraft Maintenance

ROI Savings Chart

  • One customer reported that Freya’s predictive analysis was within 3-4% accurate over actual performance and provided more than $500,000 in savings for three consecutive years.
  • Another reported they could halve the number of test flights needed and extend some vibration inspections by 100%.
  • A complex project with an aircraft manufacturer is delivering $3.5 million in savings over five years by improving aircraft operation and reducing maintenance time.
  • This unique approach, fusing data analytics with software to predict future outcomes has made Freya Systems a trusted advisor within the aviation industry.

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