In this case study we had to build for a client a way to analyze the difference between the planned flight tracks (from flight planning systems) and the actual flight tracks really flown by the airline. Indeed, planned flight tracks are supposed to be optimized as a function of meteorological and operational conditions. When an aircraft does not follow the planned flight track, this leads to missed fuel-saving opportunities:
– The actual trip fuel burn can be higher than planned, and in this case the aircraft burns the flight reserves: this represents a missed opportunity to fly a more optimized track and have lower fuel reserves.
– The actual trip fuel burn can be lower than planned, which looks at first sight like a good thing, but represents again a missed opportunity to carry less fuel for the trip — and burn even less if the planned track had been in line with the actual track.
So having the planned track aligned with what will be actually flown is a key rule of fuel efficiency, and the reasons for deviation have to be understood. But then, how to measure simply the difference between the planned flight track and the actual flight track?
We identified two key normalized parameters to analyze that help support fuel efficiency analysis.
1️⃣ Lateral trajectory deviation
We measure how much the actual flight deviates from the planned track from an XY coordinate perspective by calculating the absolute area between the two ground tracks, phase by phase (climb, cruise, descent). We normalize this area using the square of the ground distance between the origin and the destination to get a KPI related to lateral flight track compliance. We can then analyze very easily the compliance of the flights with planned tracks for the different phases of the flights.
2️⃣ Deviation from optimal altitude
Difference between the tracks can also be in terms of altitude. Flying at a different altitude than planned can lead to a fuel penalty, as illustrated in the literature like Airbus’s famous Getting to Grips with Fuel Economy: a typical 2% fuel increase might apply for flying 2000 ft below (but even sometimes above) optimum. That is why we chose as a KPI to compute in a normalized manner the average of the absolute gap between the planned and actual altitudes during cruise.
📊 By calculating these parameters across thousands of flights, combined with interviews of key airline personnel, we could conduct a robust statistical analysis to identify which factors are most impactful and target them to reduce fuel consumption.
Please contact us to know more about this case and how it could apply to your airline!
