Analys i s & commentary for a i rport decis ion makers

Enhancing airside operations by improving predictability

Quantifying the benefits

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About Helios

Helios is an independent consultancy providing business, operational, regulatory and technical advice to transport and telecoms sectors. Our specialist consultancy team helps clients to measure, understand and improve airport performance. This includes management of the runways; understanding the impact of operational procedures and capacity management on delays and operational resilience; management of the ATC-airport interface; and optimisation of landside processes (including safety management, passenger security screening, baggage systems, check-in and immigration). We also support clients in comparing and contrasting different development options and understanding the trade-offs and balances between various performance criteria, such as noise, emissions, capacity, connectivity, punctuality and economic value added.

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ATC AOBT EOBT EIBT

TSAT ACDM

See inside back page for help with acronyms!

This paper focuses on quantifying the benefits of increased predictability of airside operations

Optimising airside operations by improving predictability Introduction

Airport operations generally, and airside operations specifically, are target areas for reducing disruption and delay. Achieving this will deliver financial, operational and environmental benefits, and will also improve the passenger experience.

This paper describes how improving the predictability of airside activities will deliver the above benefits and it illustrates how those benefits can be quantified.

Many airports have begun to apply improvement programmes to optimise their processes with the aim of improving landside and airside operations. Activities include creating centralised control centres, applying lean six sigma techniques, introducing new technology and using training to reinforce existing processes or introduce new ones. In this paper we will demonstrate an analysis framework that we have developed, based on best practice in continuous process improvement, and applied flexibly to airside operations.

A case study: Arlanda airport

This paper is based on recent analyses that we have undertaken at Arlanda Airport in Stockholm. To preserve commercial confidentially some of the specific data have been made generic but the approach is based on a real-life situation. In addition, although the work was performed at Arlanda, the descriptions of airport processes and issues are generic and are widely applicable.

The objective of the project was to identify and quantify the benefits of improving the short-term predictability of the airside planning milestones of landing, on-blocks, off-blocks and take off.

The overall context is that Arlanda intends to deploy systems and processes that would allow the estimation of each of these events very accurately. This initiative was part of the Airport Collaborative Decision Making (ACDM) project at the airport.

In this paper, we describe:

• the concept of ACDM (page 2)

• airside planning milestones (page 3)

• the benefits of increasing predictability (page 4)

• understanding resource inefficiencies (page 5) and

• the Arlanda case study (page 7)

1

Airport Collaborative Decision Making

“Airport Collaborative Decision Making is the concept which aims at improving operational efficiency at airports by reducing delays, improving the predictability of events during the progress of a flight and optimising the utilisation of resources.

With ACDM the network is also served with more accurate take-off information to derive ATFM slots. As more airports implement ACDM, the network will be able to utilise available slots more efficiently and reduce the current buffer capacity.” www.euro-cdm.org

ACDM is supported by IATA, ACI and CANSO and major European programmes like SESAR. More than 30 airports across Europe have started local ACDM developments. The overall goal is connect airports into a network with automatic transfer of flight and departure planning information shared between airports. Several generic as well as specific cost-benefit analyses have been performed in the last few years and they all suggest that there is a strong case for implementing ACDM. The costs are comparatively small and benefits can be substantial.

However, different airports are interpreting ACDM in different ways. They each have their own priorities and bottlenecks they wish to address, and are adopting localised solutions. Regardless of the local interpretations of ACDM, there are some common factors:

• Linking the inbound, turnaround and outbound processes

• Sharing the right information at the right time to the right people, best placed to act on it

• A shift in culture towards greater mutual trust and cooperation

The largest benefits of ACDM will come from the network effect as Europe’s highly interconnected airports all increasingly share information. This data will need to be shared through Europe’s flow management centre (the CFMU) which will check information for integrity and accuracy. So, although airports may adopt local implementations, they will need to be compatible with the CFMU procedures.

ACDM aims to improve operational efficiency at airports

The greatest benefits of ACDM will emerge when many airports share data

2

Airside planning milestones

This report considers four key flight events, and each has an associated estimated time and actual time of occurrence.

The estimate is published in advance of the event occurring and may be updated throughout the flight as more accurate predictions become available. However, estimates are necessarily subject to change and inaccurate estimates create operational unpredictability. This causes staff and equipment to be in the wrong place at the wrong time, and facilities to be busy when they are expected to be available.

The reasons for inaccuracies are many fold. There are inherent uncertainties in any forward estimate, but in some cases more accurate estimates are known by one party but not distributed to other parties. So the planning situation becomes complex and sub-optimal.

The most critical milestone is the EOBT (estimated off-blocks time). It is taken from filed flight plans and usually only updated if a delay is longer than 15 minutes (and sometimes not at all). So a recent focus has been to provide a more dynamic estimate of it. To do this, two new milestones have been created: the Target Off-Blocks Time (TOBT) and Target Start-up Approval Time (TSAT). Some airports have implemented these new milestones, although they do not always use the same terminology and some have retained the term EOBT. We will not use TOBT and TSAT here but instead stick to EOBT which should here be interpreted as the “best estimate of off-blocks time by any party”.

More than 20% of Air Traffic Flow Management (ATFM) slots are wasted because of incorrect EOBTs Brussels CDM Information Booklet, June 2010

3

Event Estimated time of event Actual time of event

Landing ELDT (Estimated Landing Time) ALDT (Actual Landing Time)

On-blocks EIBT (Estimated On-Blocks Time) AIBT (Actual On-Blocks Time)

Off-blocks EOBT (Estimated On-Blocks Time) AOBT (Actual Off-Blocks Time)

Take-off ETOT (Estimated Take-Off Time) ATOT (Actual Take-Off Time)

Accurate estimates of key events are essential for efficient resource planning Operational benefits need to be quantified into financial metrics and airport KPIs

The benefits of improving predictability

The interconnected nature of airport operations means improved operations in one area can lead to multiple improvements in other areas. Conversely degraded operations in a particular area ripple through the system and can influence and degrade performance both over time and space. Increased predictability allows better utilisation of resources, such as facilities, equipment (fixed and mobile) and staff. Not only does it allow individual resources to be used better (eg to reduce ground crew waiting time), but it reduces knock-on effects when one delayed resource affects another.

Increased resource efficiencies and utilisation translate into direct benefits for airport stakeholders. These benefits are realised both as financial benefits (eg reduced fuel burn), reputational benefits through improved passenger experience and the airport KPIs – Key Performance Indicators—which can include items such as:

• Punctuality

• Environmental impact, eg greenhouse gases and local air quality

• Proportion of pier-served passengers

The relationship between resource efficiency and airport KPIs needs to be calculated as part of the process of estimating the benefits of improved predictability. The following diagram illustrates how improved predictability can deliver better resource utilisation and lead to direct benefits.

4

Understanding resource inefficiencies

The efficient planning of all ground resources relies on accurate time estimates of the key events in the aircraft turnaround cycle. For example:

• Ground handling crews are a limited resource and they need accurate estimates to ensure that they are available at the stand at the correct time—not too early or too late. Without an accurate estimate they may arrive too early and must wait for the aircraft to arrive (wasting their own time but also denying other aircraft their service), or too late potentially causing delays (to disembarkation affecting the passenger experience, to baggage delivery again affecting the passenger experience and causing congestion at baggage reclaim; and to service the aircraft potentially causing departure delays with all of its

associated penalties).

• If the EOBT of an aircraft is inaccurate then this may have an implication for an arriving aircraft that has been assigned the same stand. If the arriving aircraft arrives at the apron prior to the occupying aircraft pushing back there can be a number of consequences:

• the arriving aircraft holds on the taxiway, with at least some engines running, waiting for the stand to be cleared;

• the arriving aircraft makes a late stand change, which may mean that resources that have been allocated to the aircraft have to be moved;

• (if there is no alternative stand available) the arriving aircraft parks at a remote stand.

These effects cause additional taxi-in time (and increased fuel burn and emissions as a result), delay to disembarkation and may result in an increase in the use of remote stands, which can also reduce customer satisfaction.

The “fishbone” diagram below shows some factors that can affect the airside planning milestones and some of the resources involved.

Operational delays create knock-on effects that need to be understood

5

Resource optimisation is complex because each action can have knock-on effects both at the airport concerned and at other airports in the wider network De-icing in progress Photo: Swedavia/Daniel Asplund

Resource inefficiencies are created primarily by: • Lack of co-ordination of different actors when plans change;

• Schedule changes or delays that are not efficiently accommodated;

• Uncertainties in the schedule which mean that resources must arrive earlier or stay longer than expected.

Resource optimisation at an airport depends on multiple actors and there are many dependencies on different stakeholders. The point here is that a simple action by one actor can benefit many others. Similarly network benefits will emerge when multiple airports share similar information — again a simple action by one actor can have large and unseen benefits downstream.

Of course many factors affecting aircraft operations are outside of the control of the airport stakeholders. For these events, the best an airport can do is to get early warning of the problem and then re-plan its operations — for example to try and prevent and knock-on problems.

Any changes to plans or schedules are obviously harder to accommodate the closer they are to a key event, such as push back. If changes occur well in advance of the next event, there is the best chance to rearrange other resources.

De-icing is a good example of an area where resource inefficiencies can occur. Historically, de-icing planning has not always been tightly integrated with other airside activities. But delays in de-icing can lead to knock-on or downstream delays for other aircraft. Conversely, if de-icing is performed too early then the operation may need to be repeated causing delays, wasting de-icing resources and causing an unnecessary environmental impact.

It is common that de-icing crews are requested by the pilot of the aircraft prior to push-back. This request is made based on the last available estimate of the off-blocks time. Therefore, an accurate off-blocks time will help ensure that the de-icing crew arrives at the aircraft early enough to carry out the de-icing, but not too early either to wait excessively or de-ice prematurely.

6

Case Study – Arlanda airport, Stockholm

Airport facts (2010)

16.9 million travellers 84 airlines 172 destinations 5 terminals, 3 runways 16,000 employees

Helios helped Arlanda Airport, Stockholm to assess the expected costs and benefits of increasing predictability. Arlanda intended to install systems and processes that would allow the key milestones (landing, on-blocks, off-blocks and take-off) to be estimated 30 minutes in advance to an accuracy of +/- 2 minutes (95%).

We looked specifically at how this improved operational predictability would improve resource utilisation in the turnaround process and the financial and other consequences.

An early action was to establish the kinds of delay causes which provide sufficient advance warning for re-planning to take place. In the analysis, a planning horizon of 30 minutes was used. Any changes arriving later than 30 minutes are considered “last minute”.

It is clear that the target of +/- 2 minutes (95%) will be impossible to meet if there are a significant number of last minute changes. However, it is possible to meet the target for delays announced more than 30 minutes ahead.

With sufficient advance warning, estimates can be updated and everyone informed — but this will not work for ‘last minute’ events

7

There are many different causes for delay — it helps to group them according to how much advance warning is typically given But resources do not act in isolation, so the next step is to explore knock-on effects

The following table shows typical delay causes and groups them according to the degree of likely advance warning. The delay codes are based on IATA code description. They can contain errors, but they are the best source of delay data available for this sort of analysis.

These are examples of the problem that can be encountered:

• ATC delays are usually imposed as flow management restrictions in the form of calculated take-off times (CTOTs). They may be known well in advance or applied at the last minute, depending on the cause. Local ATC delays (such as taxiway queues and airborne holding) may be known in advance if the airport has a suitable Departure/Arrival Manager (D/AMAN) system that can predict and help manage runway, taxiway and stand loading in advance.

• Late boarding passengers are not known 30 minutes in advance and are “last minute” delay causes. Mitigating this problem relies on passenger management in the terminals. Also, bag drops often close 40 minutes before departure but can cause delays if passengers have large amounts of baggage or arrive in large groups.

• De-icing does not start until the aircraft is ready to push-back. Any problems with the equipment will clearly only manifest themselves at this point. But the de-icing trucks may only arrive at the aircraft about 15 minutes before push-back is expected. If the truck is late from the previous aircraft then it may cause a knock-on delay.

8

Delay cause (IATA code description) Delays expected to be known well in advance (> 30 minutes ahead)

Aircraft rotation

Loading and unloading

Late check in

Late cabin crew

Operations control

Delays that may or may not be known well in advance

ATC delay

Airport facilities

Aircraft defects

Lack of standby aircraft (technical reasons)

ATFM restriction at destination

ATFM weather restriction

ATFM en-route delay

Delays that will usually happen at the last minute (< 30 minutes ahead)

Late boarding

De-icing of aircraft

Modelling and analysing performance

Events that are known about early have a very different impact to those that happen late or last minute. The following figure illustrates this for two events that could affect the AOBT. An air traffic flow restriction, known more than 30 minutes before EOBT, can be used to update the EOBT. The revised EOBT can be distributed to all relevant parties. On the other hand, a late passenger or “no show” is only known by definition at the last minute. The EOBT cannot be updated and some resource inefficiency may result, such as waiting ground crew.

To model the changes in resource activity, an understanding is required of how resources are used both when things go to plan and when they are delayed.

The diagram below shows how the different situations can be considered for aircraft arriving on-blocks.

This analysis helps us to understand individual resource activity. The next step is to describe the chain of events and how they interact.

The relationship between events and activities can be modelled so that inter-relationships and knock-on effects are fully known. The distribution of event timings can be extracted from airport operational data, which means an accurate model of the current situation can be developed.

Modelling is essential to understand the impact of knock-on delays Resource utilisation “to plan” — aircraft arrives on time Resource utilisation “delay” — aircraft arrives late

9

Estimated versus actual Landing times

Helios developed the model of airside events for Arlanda shown on the right. Modelling allows knock-on effects to be fully taken into account and can be used to “re-run” a day of traffic, based on new operations instead of the old ones. Comparing the recorded data from the two runs allows the benefit of the changes to be quantified.

The changes modelled can include:

• New systems to allow more accurate time predictions to be made, eg as part of better arrivals and departure sequencing.

• Changes to airport systems that allow predicted and updated times to be distributed more widely.

• New procedures and behaviours to provide a more co-ordinated operation.

Measuring the benefits

The benefits of improved predictability will manifest themselves in many ways and can be observed in many different measureable metrics. Some examples of this are illustrated below.

The figure below shows how the spread of delay can reduce. This example shows the distribution of estimated landing time (ELDT) compared to the actual landing time (ALDT). The increased predictability after the improvements have been made is represented by the orange area with the key features that: (i) the average “delay” is reduced and approaches zero but; (ii) more importantly the unpredictability measured by the width of the distribution is decreased markedly.

10

A similar distribution for the difference between estimated and actual off-blocks times (EOBT—AOBT) is shown below. In this case, a “long tail” of delays is present even if there is greater predictability because some delays occur late here and cannot be known well in advance. Nevertheless, the tail after improvements are made is considerably smaller than the tail before.

Taxi times can also be reduced. This can be through, for example, better scheduling of departing aircraft to reduce ground taxi queues. One objective is to absorb delays through on-stand holding rather than off-stand holding since the former does not require engines to be running and therefore saves fuel and reduces environmental emissions. However, there can be an incentive for pilots to push back on time because of the way airline punctuality is measured. So some pilots prefer off-stand holding to on-stand holding because in order to meet punctuality targets. This needs to be addressed with the airline, airport operator and other partners.

Estimated versus actual off-blocks times Improving taxi times helps reduce fuel burn and environmental emissions

11

Another measure of resource efficiency is the proportion of time a resource is active rather than waiting. The following figure shows this. Note that this calculation does not include factors such as time spent moving between aircraft.

The final metric shown here is the “peak resources” required. This example shows the number of de-icing trucks simultaneously required throughout the day. It can be seen that the maximum number of trucks falls from 13 to 12, and other peak times are considerably reduced. This benefit will be reflected immediately in reduced operating costs and potentially lead to delayed capital expenditure.

Efficiency compares ‘active’ time to ‘waiting’ time

The peak requirement for limited resources is another effective measure

12

Conclusions

Unpredictability has a highly detrimental effect on airside operations. It causes delays and disruptions to aircraft operations and causes people, equipment and facilities to be used inefficiently.

Increasing predictability allows the use of airport resources to be optimised, removing some of the inefficiencies. It is becoming increasingly important as airports aim to reduce cost, cut congestion and improve the passenger experience. Some airports are delivering improved predictability as part of ACDM programmes.

This paper has shown how it is possible to quantify and measure the benefits of increased predictability — including knock-on effects — by modelling the operations on the airport surface. This can help quantify benefits, eg in terms of airport KPIs, financial impact or environmental consequences.

Unpredictability has a highly detrimental effect on airport operations

The benefits of improved predictability can be quantified in terms of KPIs or financially

13

ACRONYMS ACDM Airport Collaborative Decision Making

AMAN Arrival Manager

AIBT Actual On-Blocks Time

ALDT Actual Landing Time

AOBT Actual Off-Blocks Time

ATOT Actual Take-Off Time

ATC Air Traffic Control

ATFM Air Traffic Flow Management

CFMU Central Flow Management Unit

DMAN Departure Manager

EIBT Estimated On-Blocks Time

ELDT Estimated Landing Time

EOBT Estimated Off-Blocks Time

ETOT Expected Take-Off Time

KPI Key Performance Indicator

The content of this document is intended for general guidance only and, where relevant, represents our understanding of current status of transport industry matters. Action should not be taken without seeking professional advice. No responsibility for loss by any person acting or refraining from action as a result of the material in this document can be accepted and we cannot assume legal liability for

any errors or omissions this document may contain. © Helios Technology Ltd - January 2012

All rights reserved.

The first figure shows how the spread of

delay can reduce. This example is the

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