Key Performance Indicators for Performance-Based

Airport Management from the perspective of airport

operations

Lisa Kosanke∗ and Michael Schultz†

Since the total number of flight movements increases annually, existing capacity restraints at
airports are expected to worsen and new restrains will arise considering the air traffic demand and
current airport infrastructure. In this context, the current airport operations have to be significantly
optimized using the given infrastructure and new airport operational strategies have to be devel-
oped regarding to the ambitious targets of Europes Flightpath 2050 and the associated Strategic
Research and Innovation Agenda of the ACARE organization. To evaluate the performance of the
airport operations reliable, quantifiable and resilient Key Performance Indicators are required. In
the context of air traffic, performance is primarily characterized by the amount of flight movements,
handled aircrafts on ground, delays in operations and passengers operated. The objective of our
research is to derive a consolidated set of Key Performance Indicators, primary focuses the airport
airside operations, which will allow both a comprehensive view of the airport system and an effi-
cient, holistic and performance based management of the day of operations enabled by realtime
measurements and modelbased predictions for the future system states. To meet this challenge,
factors with relevant impact on the airport airside performance are identified, clustered and eval-
uated to derive reasonable Key Performance Indicators. Three Key Performance Indicators are
chosen which are determined with the help of 22 Performance Indicators split in two levels. Apply-
ing those Key Performance Indicators in the concept of Performance-Based Airport Management
an improved airport performance is expected as the knowledge about upcoming disturbing events
during the operations enables the various stakeholders to intervene ahead of time and such to re-
duce negative impacts. The validation of the chosen Key Performance Indicators and expansion of
investigations on further impacts will be in focus of future research.

Nomenclature

A/C Aircraft
ACZT Actual Commencement of De-icing Time
ADIT Actual De-icing Time=AEZT-ACZT
ADORC Actual Duration of Runway Closure
AEZT Actual End of De-icing
AIBT Actual In-Block Time
ALDT Actual Landing Time
AOBT Actual Off-Block Time
AT DR Actual Time of Driving on the Runway
AT F T Actual Time of Fly Over the Threshold
AT LR Actual Time of Leaving the Runway
AT OT Actual Take-Off Time
AT RO Actual Time of Runway Occupancy
AT T T Actual Turn-round Time
EDIT Estimated De-icing Time
EDORC Estimated Duration of Runway Closure
∗ German Aerospace Center, l.kosanke@tu-braunschweig.de
† German Aerospace Center, Michael.Schultz@dlr.de

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ET T T Estimated Turn-round Time
GH Ground Handling
ICAO International Civil Aviation Organisation
KP A Key Performance Area
KP I Key Performance Indicator
P BAM Performance-Based Airport Management
SESAR Single European Sky ATM Research Programme

I. Introduction

There are several approaches to improve the performance of Airport Management and to raise the ef-
ficiency of an airport, although the traffic volume at airports increases at the same time. These include
amongst others Airport Collaborative Decision Making (A-CDM), Total Airport Management (TAM)1, 2 and
Performance-Based Airport Management (PBAM),4, 5 a new concept which is currently under development
at the German Aerospace Center. All of these aim better cooperation between the various stakeholders
operating at an airport to reach performance improvements.
To evaluate the performance of the airport operations Key Performance Indicators (KPIs) are required and
for PBAM they are also applied as constant control mechanism. The International Civil Aviation Organisa-
tion (ICAO),6 EUROCONTROL7 and other institutions establish KPIs. SESAR D18 and SESAR D29 specify
eleven Key Performance Areas (KPAs), which are clustered in three groups called: Societal Outcome, Op-
erational Performance and Performance Enablers. KPAs are defined as a way of categorizing performance
subjects related to high-level ambitions and expectations by ICAO 2008.6
Today KPIs are mainly individual or applied for post analysis and are slightly used for pre-planning of airport
processes.10 Other than that, this paper deals with the derivation of Key Performance Indicators, which per-
mit the management of daily flight operations through real-time measurements and predictions for the day
of operations. Furthermore those KPIs aim at a better situation awareness, which allows more reasonable
reactions from the airport stakeholders.
The paper considers the performance of the airport air-side and ignores the airport land-side as well as
network effects. These effects will be analysed in a next step.

II. Methodology

Section II.A explains the approach to derive KPIs in general. Therefore schematic methods exist, which
are described in ICAO6 and EUROCONTROL.11 Afterwards the specific derivation of KPIs describing the
airport operations of the airport air-side is made in section II.B.

II.A. Necessary steps to determine air-side Key Performance Indicators

The purpose of this paper is to determine possible air-side Key Performance Indicators which allow con-
troled airport processes based on KPIs. To identify KPIs fulfilling those demands six steps are supposed
to be passed through.12 These steps are shown in figure 1. The target of step 1 is determining goals to
improve the airport air-side operations. Therefore it is necessary to be aware of possibly arising difficulties,
which may significantly influence the normal operations. After analysing the potential difficulties objectives
can be derived and used for the establishment of KPAs.
During the second step KPIs are identified. Therefore the impact of the influencing factors on the airport
air-side operations is analysed and rated. Afterwards measures detecting the arising impact are deduced
and clustered.
The selection of reasonable KPIs takes place in step 3 based on certain selection criteria,12 see table 1.
The regulation EC 691/201013 describes the requirements for the KPIs as follows:

”Key performance indicators should be selected for being specific and measurable and allowing the allo-
cation of responsibility for achieving the performance targets. The associated targets should be achievable,
realistic and timely and aim at effectively steering the sustainable performance of air navigation services. ”

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 • Definition of objectives and derivation of KPAs
Step 1

• Identification of possible indicators

Step 2

• Selection of indicators
Step 3

• Quantification of indicators
Step 4

• Implementation of indicators
Step 5

• Assessment of results
Step 6

Figure 1. Selection process used for derivation of air-side KPIs, cf. HELM12

Although these requirements are set for KPIs measuring the performance of air navigation services,
they can be used as evaluation basis of the Key Performance Indicators monitoring air-side airport oper-
ations as well. The main selection criteria used in this paper are based on HELM12 and are shown in table 1.

Significance and measurability are described by EUROCONTROL.11 The number of covered objectives
is important, because the objectives which are set are used to find the KPIs. To be able to measure the
performance it is necessary to get a reliable and complete set of required data. Real-time ability is needed
to fulfill the criteria in the context of PBAM to provide KPIs with control abilities.
Table 1. Selection criteria for KPIs for the airport air-side operations, HELM12

Criteria Description

Significance • KPI has the ability to monitor respective airport activity

• Changes in performance should be clearly recognizable in
KPI value changes

Number of covered objectives • Each objective defined in step 1 needs to be covered by at

least one KPI
• KPIs covering several objectives are preferred

Measurability • General ability to be measured is prerequisite

• Direct measurement of KPI is possible or need of express-
ing the KPI in terms of supporting metrics
• Performance is quantitatively expressed
• Abidance to privacy regulations

Data availability • Necessary investments to provide required data

• Sufficient data quality is a prerequisite
• Necessary data granularity

Real-time availability • Calculation of KPIs has to be possible in real-time

• KPIs should be able to be forecasted for a time-frame of 24
hours

The quantification of the indicators in step 4 is necessary, that it can be used as decision support indi-
cating deviations during the airport operations. In this context a quantified indicator is more significant than

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a quality statement, which could be used as decision support as well. The quantified standard values vary
from one airport to another, which leads to necessary adaptations for each airport using the chosen KPIs.
If the KPIs are defined, the implementation in the airport system is the next step (step 5). Therefore it is
important to get all data in an adequate quality.
In step 6 the quality of the KPIs is reviewed and improvements of the airport air-side operations are sup-
posed to be observed. This represents a feedback loop, whether the identified indicators fulfill their require-
ments.

II.B. Realization of steps to determine air-side Key Performance Indicators

This section describes the implementation of necessary steps to derive air-side KPIs.
Step 1: Possible impacts on the airport air-side operations are shown in figure 2. The solid line framed
factors build the exterior framework and define the capacitya . Factors which influence the daily operation
are framed with a dashed line. The dotted framed factors occur unpredictable, which means that a control
is impossible. The result is that the dotted framed factors are ignored in further investigations. The dashed
framed factors build the basis for the determination of the KPIs. The analysis of the dashed bordered influ-
encing factors results in the identification of three objectives: improved resource usage, improved capacity
usage and improved efficiency. These aims lead to two Key Performance Areas: capacity and efficiency,
which goes hand in hand with resource usage.
Step 2: The identification of potential KPIs is made by analysing the impact of the influencing factors on

•Airport layout (location, geometry, number of runways, orientation,

surrounding obstacles, type and location of taxiway exits, etc).
Legend: Exterior framework
•Personnel deployment Daily influencing factors

•Number of allocated airportslots

•Noise constraints on runway usage (e.g. ban on nightflights)
•Separation minima

•Strike Unpredictable influencing factors

•De-icing
•Aircraft types

•Parking position of the aircraft

•Arrival sequencing organisation in the air and departure sequencing
organisation on the ground
•Separation minima
•Weather phenomena affecting Air Traffic Management operations
•Demand shift

•Aircraft technology failures

• Number of parking positions

•System failures
•Communication
•Priorities
• Construction works

•Aircraft types
•Parkingposition of the aircraft
•Number of passengers
•Number of luggage
•Number/size of hand luggage

• Instrument approach equipment (ILS, VOR/DME, RWY Lighting System)

• Surveillance Systems (TAR, SMR, A-SMGCS)
• Landside infrastructure impacting airside operations

Figure 2. Influencing factors on the performance of the airport air-side. Factors, which build the exterior framework and define the declared
capacity are framed with a solid line. Factors with an influence on the daily operation are framed with a dashed line, while dotted framed
factors describe unpredictable incidents.

a ‘Capacity is a measure of the maximum number of A/C operations which can be accommodated on the airport or airport component

in an hour.’3

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 the airport air-side. Possibilities to measure the arising impact are deduced and clustered. A pre-
selection based on the degree of influence on the airport process is made to minimize the number of
potential Performance Indicators.
Step 3: The selection of KPIs is based on the selection criteria shown in table 1. De-icing, Snow
removal and utilization rate are the chosen KPIs, which are described by several Performance Indicators,
see figure 3. These Performance Indicators are necessary to evaluate the chosen KPIs and to point out
possible deviations. De-icing is evaluated by de-icing resources per interval, de-icing-queue per interval
and the de-icing duration per interval. If deviations concerning the airport operations in one of those fields
occur, it will be indicated by changing the displayed KPI ‘De-icing’. Snow removal is characterized by the
limitation of aircraft stands and taxiways, the snow removal duration of the runway, which leads to a runway
closure and thus to a significant decline of capacity. The third KPI ‘Utilization rate’ is divided into four
lower levels. These Performance Indicators deal with the utilization rate regarding taxiways, apron, runway
and turnaround process. These four Performance Indicators are subdivided again and described by the
red marked indicators in figure 3. The necessary measurement variables to determine the Performance
Indicators are summarized in table 2.
Figure 3. Chosen Key Performance Indicators with their describing Performance Indicators in two levels.

Key Performance
Indicators Performance Indicators (Level 1) Performance Indicators (Level 2)

De-icing resources per interval

De-icing De-icing queue per interval

Keeping the de-icing duration per interval

Snow removal resources per interval

Limitation of aircraft stands per interval

Snow removal
Limitation of taxiways per interval

Keeping the snow removal duration of the run-

way per interval

Runway queue per interval

Taxiway utilization rate per interval
Taxiway resources per interval

A/C stand resources per interval

Apron utilization rate per interval
Keeping the planned A/C stand per inter-
val
Utilization rate Runway occupancy time per interval

Runway utilization rate per interval

Runway utilization rate per interval
Utilization rate per interval (departure)

Utilization rate per interval (arrival)

Equipment resources per interval

Turn-round process utilization rate per interval Waiting queue for GH-service per interval

Keeping the estimated duration of turn-

round per interval

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 Table 2. Chosen KPIs and possible measurement variables

Performance Indicator Measurement Variable

# used de-icing equipment
De-icing resources per interval # available de-icing equipment

De-icing queue per interval # A/C in de − icing queue

ADIT AEZT −ACZT
Keeping the de-icing duration per interval EDIT = EDIT
# used snow removal equipment
Snow removal resources per interval # available snow removal equipment

Limitation of A/C stands per interval # snow covered A/C stands (Terminal/Apron)
Limitation of taxiways per interval # snow covered taxiways
ADORC
Keeping of the snow removal duration of the EDORC
runway per interval
Runway queue per interval # A/C in runway queue
# open taxiways
Taxiway resources per interval #available taxiways
# occupied stands (Terminal/Apron)
A/C stand resources per interval #available stands (Terminal/Apron)

Keeping the planned A/C stands per inter- # changed A/C stand positions
val
P P
Runway occupancy time per interval (AT LR−AT F T )+(AT DR−AT OT ) = AROT
# flight movements
Runway utilization rate per interval capacity
handled traffic (departure)
Utilization rate per interval (departure) capacity (departure)
handled traffic (arrival)
Utilization rate per interval (arrival) capacity (arrival)
# used equipment
Equipment resources per interval # available equipment

Waiting queue for GH-service per interval # declared A/C for GH − # handled A/C by GH
AT T T AOBT −AIBT
Keeping the estimated duration of turn- ET T P = ET T P
round per interval

III. Conclusion

The implementation of KPIs with control function for air-side airport operations aims at a better situa-
tion awareness of the airport stakeholders, resulting in an improvement of the overall air-side performance.
Therefore a colour coding might be intended , which enables an identification of occurring problems and
categorizing impacts in: no problem, minor problem, major problem. Deviations will be measured by Per-
formance Indicators and are displayed by the associated KPI. To find the real problem it is necessary to
investigate the Performance Indicators located at the lower level describing the KPI.
It has to be considered, that the personnel disposition has an influence on the Performance Indicator De-
Icing resources, Snow removal resources and the turn-round process,14 which means that it has to be
measured as well.
The selection criteria ‘Measurability’ and ‘Data Availability’ are not completely fulfilled, because the number
of snow covered stands and taxiways as well as the Actual Time of Runway Occupancy for instance is not
yet measured.
The Performance Indicator ‘Runway Occupancy Time’ is not significant on its own. If it is high it might indi-
cate a high number of flight movements, which stands for a satisfying utilization rate, but on the other hand
it stands for a long duration of stay of the aircraft on the runway, because taxiways are closed (e.g. due to
snow) or the braking distance is long (e.g. due to heavy rain), which is not an indicator for a good utilization

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rate. But if both Performance Indicators are considered together a significant conclusion concerning the
utilization rate is possible. Another possibility is an other definition of ‘Runway Occupancy Time’, which
refers to the occupancy time by one aircraft instead of the sum of the aircrafts starting and landing. This
allows a clear identification of the reason for the long occupancy time.
To meet the objective of real-time measurement a reasonable interval has to be defined, which will be de-
termined and proved in further research.
A disadvantage concerning the derived KPIs is the focusing on winter conditions, which means, that there
is just one KPI left which could be used perennially.
But the defined objectives (see II.B) improved resource usage, improved capacity usage and improved effi-
ciency are fulfilled even by this one KPI and its associated Performance Indicators.
Last but not least step 4, 5 and 6 have to be implemented, but at the present stage a quantification is not
reasonable, because the values will vary from airport to airport. The fulfilment of these steps will be in focus
of next research.
Although the derived KPIs are not proved until now a significant improvement concerning situation aware-
ness is expected.

References
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Flughafen Stuttgart GmbH, ARTRiCS),TAMS Operational Concept Document, 2012

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