Case Study

Long Term Down

Freya delivers an analytics solution resulting in more than an 8% increase in fleet availability

NEED

Ability to predict potential Long Term Down aircraft.

THE BACKGROUND

Over the past several years, a particular military aviation platform has been challenged with readiness issues. Readiness is defined as the availability of the aircraft to fly a mission; low readiness means the fleet are not able to fly. Nearly half of the aircraft in the fleet were not able to fly missions due to maintenance and/or supply difficulties. A significant factor to low readiness rates has been the number of Long Term Down (LTD) aircraft. Long Term Down is defined as aircraft that are not able to fly for more than 60 days.

With many aircraft down for long periods of time, the overall fleet readiness suffered and aircraft were not able to fly their missions. In addition to a lack of readiness, pilot training also suffered as there are not enough aircraft to support the amount of flight hours needed in a given month.

Restoring these aircraft to a flyable condition has had an immediate positive impact to the overall fleet readiness. However, it had proved difficult to identify which aircraft will reach a Long Term Down state and to determine how much effort it would take to bring the aircraft back into service.

Boxplot Chart

APPROACH

Utilized subject matter expertise and data analytics to create a model.

THE QUESTION

“Do we have enough data and subject matter expertise at our disposal to create a useable model for our customers to restore LTD aircraft to flyable conditions?”

THE APPROACH

Working together with a team of analysts and subject matter experts, Freya Systems was able to develop two algorithms to help solve the problem.

The first algorithm produces a report that determines which aircraft are likely to become Long Term Down with greater than 82% accuracy. The algorithm allows experts to drill down the data set to a smaller subset of aircraft, identified as high risk for achieving LTD state.

The second algorithm estimates the number of hours it will take to bring an aircraft back to a flyable state. The team applied predictive analytics to create a prioritized list of aircraft at risk and in need of attention. This report provides actionable data for the response team who are empowered to efficiently plan repairs and execute maintenance for optimal fleet performance.

RESULTS

Helped our customer exceed their contractual obligation and increased the platform’s readiness by 8%.

THE RESULT

Within a year, the team was credited for assisting 26 Long Term Down aircraft back to flight status, three times the target number. Some aircraft that were restored to flyable conditions were down for more than four years!

A high-ranking military official on the platform has publicly stated that the LTD effort has been a significant reason why the fleets’ mission capability or “readiness” rate has increased by more than 8%. Through the use of analytics, the platform’s increase in readiness allows more missions to be completed, more pilots to meet their requirements, and allows Freya’s customers to exceed their contractual obligations.

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