For years, airports throughout Europe have been working to become more reliable by achieving structural improvements in Target Off Block Time (TOBT) quality. Airport Collaborative Decision Making (A-CDM) is capable of driving progress in this area, but many airports have now reached a performance plateau, with little sign of further systemic improvements. That’s why new AI-driven solutions, including Deep Turnaround, are such a gamechanger. Deep Turnaround, which captures all turnaround events and provides accurate predictions, can help airports overcome previous barriers and reach new heights of operational success.
By Jeffrey Schäfer and Yiannis Alexopoulos
Many airports have reached a performance plateau in improving TOBT quality, which is hampering capacity utilisation and increasing workload.
AI-driven turnaround management solutions, such as Deep Turnaround, offer a breakthrough by providing accurate insights and predictions.
Integrating AI predictions into existing A-CDM tools and processes can significantly improve efficiency, predictability and communication.
Non-A-CDM airports focusing on slot coordination can use AI-driven insights for more predictable asset planning and departure sequencing.
Deep Turnaround can speed up the implementation of A-CDM processes, helping airports become more proactive and realise benefits sooner.
Effective implementation of A-CDM requires ongoing collaboration between the airport, airlines, ground handlers and Air Traffic Controllers (ATC). In this world of fine margins, predictable TOBTs are crucial for a reliable outbound sequence, enhancing capacity utilisation, and achieving the momentum needed for success.
For decades, European airspace has become increasingly crowded. This has led airports across the continent to target structural improvements in TOBT quality – the main input for demand and capacity balancing. However, many airports are struggling to make improvements. One reason for the slow progress is ineffective communication. This is hardly surprising: with workers facing so many urgent tasks, operational challenges tend to get priority over updating information. Moreover, many airports remain highly reliant on manual input, which is prone to errors. All these factors hamper capacity utilisation.
At the same time, growing competition in the aviation industry is causing airports to cut ground handling costs by reducing staffing levels and increasing workloads for remaining employees.
Airport Collaborative Decision Making is a joint initiative designed to maximise airport and airspace capacity utilisation by optimising processes from the gate to the runway. The focus is on improving efficiency, predictability, and communication between airlines, ground handlers, airport operators, air navigation service providers and other key stakeholders.
The most important aspect of A-CDM operations is predicting the end-time of ground handling operations. This is where AI-driven turnaround management solutions, such as Deep Turnaround, can make a positive impact, complementing human ingenuity to improve the predictive capabilities of airports and their operational partners.
The cornerstone of good A-CDM performance lies in predictability and communicating process duration times, with TOBT quality being the most crucial metric. The sooner this information is known, the more effectively capacity can be planned and utilised.
As airports and ground handlers work to enable predictable TOBTs, their main challenge is low visibility into the status and duration of various ground processes. Increased workloads create further difficulties, contributing to poor communication between relevant parties and a lack of objective information that could improve decision making.
Realising the potential of A-CDM operations also requires significant manual input and dedicated focus to orchestrate the timely, accurate and stable communication of milestone updates. Handling agents typically don’t have the resources for such tasks, as they sit on top of their regular responsibilities.
With recent advances in AI and machine learning, however, these challenges are becoming a thing of the past. Results from Deep Turnaround at multiple airports show that accurate insights and predictions are now very achievable.
The key metric in turnaround operations is Predicted Off Block Time (POBT) – and this is where Deep Turnaround truly excels. Delivering highly accurate TOBT predictions, it helps airports unlock the full potential of A-CDM by improving departure sequences, capacity and asset utilisation.
Turnaround management solutions enable airports to monitor each turnaround in real time. Deep Turnaround, for example, has analysed over 750,000 turnarounds, captured all 40+ related processes and knows each individual turnaround scenario (see also: How does Deep Turnaround work?)
With such impressive capabilities, Deep Turnaround can go beyond the insights captured by the human eye. It enables ground handlers to focus on safe aircraft handling while being fed with fact-based predictions on the progress of their turnarounds, leading to better TOBT determination.
Since Deep Turnaround’s inception, its POBT has outperformed the TOBT in terms of predictability. Specifically, once a turnaround starts showing signs of delays, the accuracy of the POBT outperforms the accuracy of the of the TOBT. This advantage has helped Schiphol become more reliable when it comes to take-off predictability (as seen in the ACI TTOT report below).
*Note: Data from EUROCONTROL’s July 2024 TTOT Error KPI demonstrating take-off predictability. The lower the value, the better. The TTOT prediction metric (TTOT Error) is based on average absolute difference between ATOT and TTOT, calculated for non-regulated flights 30 minutes prior to IOBT.
A frequently asked question is how to onboard airport stakeholders to using the new insights and predictions. Here, experience has shown the importance of integrating the POBT metric into the A-CDM community’s existing tools and ways of working. This makes it easier for others to see the information and act on it. It ensures the A-CDM community is alerted to potential delays. Harnessing additional information, stakeholders can determine whether the current TOBT is still feasible or if it needs to be updated. This proactive approach supports predictability during the A-CDM process and ensures the efficient usage of available capacity.
Target Off-Block Time (TOBT) is the exact moment the airline and ground handler expect to be finished with all ground handling processes, after which the aircraft could depart.
Predicted Off-Block Time (POBT) is the prediction, in this case by Deep Turnaround, of the Off-Block time, taking into account the status of all turnaround events and historical data. At Schiphol, the POBT calculated by Deep Turnaround is renamed as Predicted End of Ground Handling Time (PEGT), to more accurately reflect what it stands for: when the turnaround is ready and not when the aircraft is actually departing.
Target Start-Up Approval Time (TSAT) is based on TOBT and represents the time by which an aircraft should receive start-up approval from ATC. Not adhering to TSAT will lead to TSAT expiration and loss of runway capacity.
Beyond easy integration, intelligent tools like Deep Turnaround offer airports various other benefits. For example, ground handlers can use the Deep Turnaround app to track turnarounds from anywhere. The app shows the relevant A-CDM milestones for each flight and alerts users to manually update the TOBT if the current one differs significantly (by more than five minutes) from the POBT. Below, you can see how different stakeholders stand to benefit.
Airport Operator: Earlier warning of gate-planning conflicts and improved planning
Airline: Improved insights into expected time of departure and improved collaboration with ground handlers
Ground handling agent: Earlier signalling of delays when delays can still be resolved
Air Traffic Control: Better predictions of outbound flow and runway sequencing. This is also relevant for non A-CDM airports that see runway or taxiway constraints
If an airport is undergoing A-CDM implementation, Deep Turnaround can accelerate the realisation of benefits and trust in the system. The toughest challenge in implementing A-CDM is transitioning from reactive to proactive. This is closely followed by difficulties in accurately updating the turnaround status – a process that has always relied on manual input. The drawback of such an approach is that operations will focus on solving problems over communicating departure times, especially during periods of disruption.
The POBT can accelerate new ways of collaboration for all parties involved: it's not only more accurate, but also offers live tracking to show turnaround tasks in real time.
Specifically, many new data points are being introduced in the implementation of A-CDM, including the TOBT. The value of A-CDM lies in making this metric as accurate as possible. This requires a new way of working – one involving investments and change management for airports, ground handlers and other parties. With accurate predictions and forecasts, airports can achieve the TOBT metric faster. This speeds up the implementation of the CDM process and ensures that its benefits are realised sooner.
Non-A-CDM airports have a focus on slot coordination. The POBT can also be used at these sites, supporting stakeholders through more predictable asset planning and departure sequencing and serving as a direct input for estimated departure time. Currently, non-A-CDM airports use the Estimated Off-Blocks Time (EOBT), which is often inaccurate. Sharing the POBT metric with air traffic management organisations (like EUROCONTROL) in real time can help these airports provide the wider network with greater predictability.
In summary, A-CDM performance relies on being predictable, with TOBT quality being the key metric. To increase this predictability, Deep Turnaround has demonstrated that the POBT enables airports to improve their current A-CDM operations. Moreover, airports considering becoming A-CDM, or that want to improve their predictability, also benefit from the POBT: it speeds up A-CDM implementation and helps the airport provide more predictability to the wider network. Overall, Deep Turnaround provides a data-driven approach to A-CDM and removes ambiguity created by the various manual processes during the turnaround.
Interested in using Deep Turnaround at your airport? Reach out to request a demonstration.
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