CASE STUDY: easyJet Improves Descent Performance

Author: David Buckley, Flight Operations Manager – Efficiencies & Sustainability, easyJet

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EASYJET

easyJet is a European airline operating 356 Airbus A320 family aircraft, operating up to 2,000 flights on a typical summer day. In my role focused on delivering efficiencies and sustainability, I manage a team of flight efficiencies specialists, each concentrating on key functional areas: airports, airspace, aircraft, and operational efficiencies capitalizing on sustainable practices. In this article, I’ll explain the steps we took to optimize the descent profile for every flight.

PATHWAY TO NET ZERO

As a responsible business, easyJet is committed to achieving Net Zero by 2050 and established a Net Zero pathway in 2022, as depicted in Figure 1.

Figure 1

The Net Zero pathway identifies two milestones: a 35% reduction by 2035 and net-zero by 2050. Focusing on the 2035 emissions intensity reduction milestone, it is achieved through fleet renewal, Sustainable Aviation Fuel (SAF), airspace modernization, and operational efficiencies. Airspace modernization and operational efficiencies contribute significantly to the 35% reduction target by 2035 and are the primary focus areas of the flight efficiencies team.

By 2050, we aim for a 78% direct emissions reduction, which will be achieved through a range of existing and future technologies and solutions – with the remaining 22% of emissions accounted for through Direct Air Carbon Capture, through a facility in Texas being built by 1PointFive in partnership with Airbus. As part of our Net Zero roadmap, we’ve identified various levers that can be applied as technologies mature.

DECARBONISATION LEVERS

Our strategy is Reduce, Replace, and Remove, as illustrated in Figure 2.

Figure 2

Reduce

In flight operations, we’re primarily focused on ‘Reduce’, implementing actions today to optimize efficiency.

Operational efficiency examples;

• This year we’ve deployed Pre-conditioned Air Units (PCAs) at Milan Malpensa Terminal 2 across 16 contact stands.

This allows easyJet aircraft to turn off their Auxiliary Power Units (APUs), gas turbines at the tail that provide electric power and air conditioning during turnaround.

APUs consume significant fuel when aircraft are stationary, contributing to noise and carbon emissions on the apron. To create a cleaner, quieter ground environment for staff, passengers, and communities, we’ve replaced APUs with hybrid PCAs.

• For the second example, airspace modernizations has been a major focus, involving close collaboration with internal and external stakeholders. We’re using big data to build digital twins of our aircraft and airspace.

This enables us to quantify total airspace inefficiency and identify the most inefficient areas. This approach allows us to prioritize and target airspace modernization where it’s needed most.

It supports our Net Zero roadmap by fully optimizing existing operations within today’s airspace constraints, while working with Air Navigation Service Providers (ANSPs) to modernize airspace structure for the future.

Replace

‘Replace’ primarily involves fleet renewal with neo aircraft, but it also relies on the availability of zero-emissions aircraft before 2050. It extends to replace the energy sources we use, such as SAF and eventually hydrogen.

Remove

‘Remove’ refers to Direct Air Carbon Capture (DACC) technology to extract carbon from the atmosphere. There have been exciting developments in this area, and we’re working closely with Airbus and 1PointFive to make it a reality.

DESCENT EFFICIENCY

A key aspect of operational efficiency is the descent phase of flight. We’ve collaborated with Airbus for many years to enhance aircraft performance during descent, aiming to make the performance database as accurate as possible with real-world aircraft data.

Figure 3

Descent Profile Optimization (DPO) is standard on every new A320 family neo aircraft, and we chose to retrofit it to the rest of our fleet, over 200 aircraft to achieve fuel burn and CO₂ savings during descent.

We’ve also deployed NAVBLUE IDLE Factor Optimizer application (IFO) across all aircraft to update each aircraft’s IDLE Factor, further reducing fuel burn and CO₂ emissions.

What’s advantageous about these solutions is that they’re seamless for the crew. Optimized IDLE Factors are regularly uploaded into the Flight Management System (FMS) by avionic engineers, maintaining the accuracy of a descent path computed by the FMS with real aircraft performance.

The only guidance we provide to the crew is to fly a managed descent where possible and respect the top of descent (TOD). Of course, Air Traffic Control (ATC) constraints often limit the full savings potential of DPO and IFO, but we’ve still observed significant improvements in fuel burn and CO₂ emissions.

DPO AND IFO

Figure 4.1 shows an introduction to DPO and IFO functions.

Figure 4.1

Descent Profile Optimization (DPO)

Descent Profile Optimization (DPO) is a cutting-edge enhancement to the Flight Management System (FMS) performance database, designed to calculate and execute a more energy-efficient descent trajectory. Its core principle is to maximize the time spent at fuel-efficient cruising altitudes by delaying the top of descent to its optimal position.

DPO integrates highly accurate, updated engine and aerodynamic performance models into the FMS. These models provide a precise understanding of the aircraft’s actual performance, particularly at the low thrust settings required for an IDLE-thrust descent. This precision significantly reduces the need for conservative safety margins that previously mandated earlier descents. Leveraging these models, the FMS can compute a descent profile that closely adheres to the aircraft’s true IDLE capabilities. In managed descents, the aircraft targets this optimized path, aiming to avoid unnecessary thrust application.

A primary benefit of DPO is its ability to facilitate a more continuous, ‘one-slope’ descent, aligning with the principles of Continuous Descent Approaches (CDA). This minimizes thrust-intensive level-off segments at lower altitudes, which are a significant source of fuel consumption. While our statistical analysis shows that fully managed descents are not always achievable due to ATC constraints, the DPO retrofit provides substantial benefits even in a real-world operational environment.

IDLE Factor Optimizer (IFO)

The IDLE Factor Optimizer (IFO) is a complementary solution that fine-tunes the DPO performance model on a tail-specific basis for every aircraft in the fleet. It accounts for minor performance deviations between individual aircraft and the fleet-average model used by DPO. This ensures that the benefits delivered by DPO are maintained and optimized throughout an aircraft’s service life.

The IFO solution utilizes DPO performance models to compare an aircraft’s actual in-service descent performance against FMS predictions. Based on this comparison, IFO computes a specific IDLE thrust correction, known as the IDLE Factor. This optimized factor is then implemented in the FMS, ensuring the FMS descent path more accurately matches the true IDLE thrust capabilities of that specific aircraft.

Operational Context

We worked closely with NAVBLUE and Airbus to approach the analysis of DPO and IFO holistically. To distinguish the projects: Descent Profile Optimization, retrofitted on all ceo aircraft, is part of the SESAR HERON (Highly Efficient Green Operations) project, a large-scale demonstrator.

Simultaneously, we’ve collaborated with NAVBLUE on IDLE Factor Optimizer (IFO) and Performance Factor Optimizer (PFO). Adopting a holistic approach, we have used the same methodology to quantify the benefits from both DPO and IFO, ensuring a comprehensive validation of the fuel savings.

Quantified Benefits

Moving on, figure 4.2 is a good visualization of the difference that we’ve seen with DPO.

Figure 4.2

By calculating the descent path more accurately, DPO enables fuel savings by allowing longer time at cruise level and less time at lower flight levels. Although this article focuses on DPO and IFO, we also retrofitted the Continuous Descent Approach (CDA) function, offering additional fuel and noise benefits, delivered by deceleration during descent before final approach. Additionally, with CDA, two pseudo waypoints are displayed on the Primary Flight Display (PFD), visualizing the FMS calculated flap retraction points.

Figure 4.2 quantifies the impact of the IDLE factor on the descent path for a flight at FL350 with a cost index of zero (CI=0), which corresponds to a minimum fuel strategy. A negative IDLE factor shifts the top of descent  closer to the destination, whereas a positive factor moves it farther away. Under these specific conditions, Descent Profile Optimization (DPO) extends the cruise phase by 11 nautical miles (NM), and applying an optimized IDLE factor of -1 provides an additional 1.5 NM extension.

It’s also worth highlighting the evolution from pre-retrofit to today. Previously, we used a fixed minus 3.5 IDLE factor across all aircraft, based on previous Airbus guidance.

Figure 5

Figure 6

METHODOLOGY AND VALIDATION

Statistical Approach and Population Definition

A direct flight-by-flight comparison of fuel burn is not feasible due to the numerous operational variables involved and how they impact fuel burn. Therefore, a statistical approach based on population comparisons was taken. We collected extensive data, and the analysis was completed as part of the HERON project.

Flights were sorted into four distinct populations to isolate the effects of the retrofits (see Figure 6):

Pre-DPO (IF=0): Aircraft without DPO and a neutral IDLE Factor.

Pre-DPO (IF=-3.5): Aircraft without DPO but with the standard legacy IDLE Factor of -3.5.

DPO-only (IF=0): Aircraft with DPO retrofitted and a neutral IDLE Factor.

DPO+IFO: Aircraft with both DPO and the tail-specific IDLE Factor Optimiser active.

Data Preparation and Filtering

Significant statistical rigor was applied, beginning with data quality checks on a dataset of nearly one million flights. To ensure a valid before-and-after comparison (No DPO vs DPO and IFO vs DPO), data was validated by normalizing key parameters of influence, such as cruise fuel flow. The analysis was performed on flight segments of 180nm before touchdown with at least 10nm of cruise. Furthermore, descent profiles were rigorously filtered to exclude outliers and flights with non-standard events (e.g., go-arounds, holding patterns, significant ATC vectoring) that would skew fuel consumption data.

Statistical Analysis

Processed data was split into buckets using key parameters: aircraft type, cruise altitude, aircraft mass, descent winds, distance-to-go (DTG) and top of descent. Each bucket was required to contain a minimum of 50 flights to ensure statistical validity. Multiple Welch’s t-tests were performed to confirm if the observed difference in average fuel burn between two groups of flights (for instance flights with DPO+IFO versus flight with DPO only) is statistically significant and not just due to random chance. This statistical test is particularly well-suited for real-world operational data, as it does not require the groups being compared to have equal sizes or equal variances, conditions rarely met in flight data.

Other methodologies were tested, and the key takeaway from this analysis is consistent benefits across methodologies. ATC vectoring and flight level constraints do reduce the benefit, but savings are still realized for the portions of the descent where the aircraft is flown in managed mode. Essentially, the closer the aircraft can fly to the optimum descent trajectory, the more savings can be achieved. While the solution is most effective for managed descents, fuel savings were also observed on flights with a low proportion of managed mode, primarily due to the improved prediction of the top of descent leading to longer cruise phase at optimum fuel burn.

Granular Descent Profile Analysis

To supplement the overall fuel burn comparison over the final 180nm, a granular analysis of the descent phase was also performed. This involved examining flight path angles, speeds, engine thrust ratings, and fuel flow, with each descent segmented into 1000ft altitude bins (from 35,000ft to 5,000ft). Within each bin, only segments flown in managed mode and adhering to the FMS profile were considered. Average values for each parameter were computed for all four populations within each bin.

This detailed analysis served two purposes. First, by comparing average speeds and flight path angles, we verified that operational conditions were comparable across all populations. Second, the results aligned with the expected effects of DPO and IFO (primarily fuel flow reduction), corroborating their contribution to the overall fuel savings. This analysis also revealed a lower variance of performance in the DPO+IFO population than in the DPO-only population, which is consistent with IFO’s function of fine-tuning the performance model at the individual aircraft level.

Figure 7

Our Descent Profile Optimization (DPO) retrofit program, covering 228 aircraft, has delivered validated savings of $9.3 million. An analysis of 370,000 flights confirmed an average fuel reduction of 25 kg per flight.

Crucially, the data shows a direct link between procedural compliance and efficiency. Fuel savings increased to 30 kg per flight when crews utilized the managed descent mode for at least 60% of the descent. This highlights that the full benefit of descent optimization technologies depends on a refined landing procedure, which requires effective coordination between flight crews and Air Traffic Control.

easyJet originally deployed IDLE factors on 309 aircraft as a large-scale demonstrator with NAVBLUE, yielding positive results of 7 kg average fuel savings per flight, $3.5 million total. Now, with a full fleet equipped with DPO and IFO, we’ve deployed the NAVBLUE Performance Factor Optimisation (PFO) solution. Collaboration with the OEM has been key to accelerating these initiatives to deliver tangible fuel savings on every flight. These savings are only possible thanks to the OEM solutions proposed by Airbus and NAVBLUE, with their unique knowledge of FMS performance.