1.
Setting KPIs Level
For measuring Airports
Performance
Life is a continues
learning course
Dears,
One of the key success factoris to have a targets,
Target that the airport authority try to achieve them.
One of these, is traffic flow,
Yes it is hard to predict them, but we can move towards to achieved them by
using excel spreadsheet.
Setting targets for airports can be set for
1- Traffic Passengers
2- Number of Flights
3- Air Cargo Flow
Moreover, KPIs level can be define by the difference between the forecasted
figures and the achievable numbers. According to the following Performance
Scale.
GoodCase Studies
Let the Figures Talk – Issues No. 1 to No. 7
Hope to enjoy
Mohammed Salem Awad
By: Mohammed Salem Awad
Aviation Consultant

2.
22
Let the Figures Talk – Issue No. 1
Defining Airport Performance in 2018
Case Study: Heathrow Airport
Let the Figures Talk:
This issue is addressing the answer, for the question, why we are forecast the traffic of the
Airports. The forecasting analysis is FAIR, when R-square is greater than 90 %, and
consequently the forecasted figures are set as TARGETS to be achieved.
Errors: are the deviations between the
Historical Data with Model Fitted, always
addressing by a range percentage.
Performance: are the deviations between the
target figures and the actual figures for the
proposed target period (2018).
Results above targets are good performance
and represented by a green color, and results
below targets are poor one, represents by red
color. However, airports should set up the
right target level to define KPIs system. i.e
less than (- 10.0 % ) should consider as a poor
one.
Case Study: Heathrow Airport
By referring to issue no. 29 Heathrow
Airport,
R- Squared (monthly model) = 98.01 %
Errors range: + 4.73 % - 3.82 %
Analysis:
The figures of seven months for LHR shows an
excellent performance, keeping in mind the red color indicates below the targets level (very minor
percentage),while the green one is good performance. The performance range – 0.57 % to +2.22 %

3.
33
Let the Figures Talk – Issue No. 2
Defining Airport Performance in 2018
Case Study: Dublin Airport
Let the Figures Talk:
Each Airport Forecasting can be review, The
forecasting analysis is FAIR, when R-square is
greater than 90 %, and consequently the
forecasted figures are set as TARGETS to be
achieved.
Errors: are the deviations between the
Historical Data with Model Fitted, always
addressing by a range percentage.
Performance: are the deviations between the
target figures and the actual figures for the
proposed target period (2018).
Results above targets are good performance
and represented by a green color, and results
below targets are poor one, represents by red
color. However, airports should set up the right
target level to define KPIs system. i.e less than
(- 10.0 % ) should consider as a poor one.
Case Study: Dublin Airport
By referring to issue no. 30 Dublin Airport,
R- Squared (monthly model) = 99.00 %
Growth: 7.81 %
Errors range: + 7.43 % - 4.26 %
Analysis:
The figures of six months for DUB shows an Good performance,keeping in mind the red color
indicates below the targets level (only two readings make a significant impact -4.63% & -5.21 % ),
while the green one is good performance. The performance range – 5.21 % to +1.13 %.

4.
44
Let the Figures Talk – Issue No. 3
Defining Airport Performance in 2018
Case Study: ATHENS Int. Airport
Let the Figures Talk:
Each Airport Forecasting for 2018 can be
reviewed, the forecasting analysis is FAIR,
when R-square is greater than 90 %, and
consequently the forecasted figures are set as
TARGETS to be achieved.
Errors: are the deviations between the
Historical Data with Model Fitted, always
addressing by a range percentage.
Performance: are the deviations between the
target figures and the actual figures for the
proposed target period (2018).
Results above targets are good performance
and represented by a green color, and results
below targets level are poor one, represents
by red color. However, airports should set up
the right target level to define KPIs system.
i.e less than (- 10.0 % ) should consider as a
poor one.
Case Study: Athens Int. Airport
By referring to issue no. 32 Athens Airport, R- Squared (monthly model) = 99.12 %
Annual Growth = 8.48 % Errors range: + 6.75 % - 8.08 %
Analysis:
The actual data of eight months of 2018 for ATH shows an excellent performance, keeping
in mind the red color indicates below the targets level (very minor percentage), while the
green one is good performance. The performance range – 3.34 % to +7.03 %

5.
55
Let the Figures Talk – Issue No. 4
Defining Airport Performance in 2018
Case Study: CagliariInt. Airport
Let the Figures Talk:
Each Airport Forecasting for 2018 can be
reviewed, the forecasting analysis is FAIR,
when R-square is greater than 90 %, and
consequently the forecasted figures are set as
TARGETS to be achieved.
Errors: are the deviations between the
Historical Data with Model Fitted, always
addressing by a percentage range.
Performance: are the deviations between the
target figures and the actual figures for the
proposed target period (2018).
Results above targets are good performance
and represented by a green color, and results
below targets level are poor one, represents by
red color. However, airports should set up
their own right target levels to define KPIs
system. i.e less than (- 5.0 % ) should
consider as a poor one.
Case Study: Cagliari Int. Airport
By referring to issue no. 98 Cagliari Int.
Airport, R- Squared (monthly model) = 98.67 %
Annual Growth = 5.95 %
Errors range: + 8.39 % - 4.46 %
Analysis:
The actual data of eight months of 2018 for CAG shows an excellent performance, keeping
in mind the red color indicates below the targets level (but greater than – 5.0 %), while the
green one is good performance. The performance range – 3.72 % to +1.68 %

6.
66
Let the Figures Talk – Issue No. 5
Defining Airport Performance in 2018
Case Study:
Schiphol Amsterdam Airport
Let the Figures Talk:
Each Airport Forecasting for 2018 can be
reviewed, the forecasting analysis is FAIR,
when R-square is greater than 90 %, and
consequently the forecasted figures are set
as TARGETS to be achieved.
Errors: are the deviations between the
Historical Data with Model Fitted, always
addressing by a percentage range.
Performance: are the deviations between
the target figures and the actual figures for
the proposed target period (2018).
Results above targets are good performance
and represented by a green color, and results
below targets level are poor one, represents
by red color. However, airports should set
up their own right target levels to define
KPIs system. i.e less than (- 5.0 % ) should
consider as a poor one.
Case Study: Amsterdam Airport
By referring to issue no. 39 Schiphol Amsterdam
Airport, R- Squared (monthly model) = 99.03 %
Annual Growth = 8.27 %
Errors range: + 4.23 % - 2.72 %
Analysis:
The actual data of Seven months of 2018 for AMS shows a the performance of the airport, at
-5 % threshold level, keeping in mind the red color indicates below the targets level (but
greater than – 5.0 %), while the green one is good performance. The performance range
– 9.28 % to +1.15 %. Should we review the threshod level to -10 % !!!, it depends on the
airport policy and top management decision to review & setup these targets level.

7.
77
Let the Figures Talk – Issue No. 6
Defining Airport Performance in 2018
Case Study:
Aéroportsde Montréal (Int. Pax)
Let the Figures Talk:
Each Airport Forecasting for 2018 can be
reviewed, the forecasting analysis is FAIR,
when R-square is greater than 90 %, and
consequently the forecasted figures are set as
TARGETS to be achieved.
Errors: are the deviations between the
Historical Data with Model Fitted, always
addressing by a percentage range.
Performance: are the deviations between
the target figures and the actual figures for
the proposed target period (2018).
Results above targets are good performance
and represented by a green color, and results
below targets level are poor one, represents
by red color. However, airports should set
up their own right target levels to define
KPIs system. i.e less than (- 5.0 % ) should
consider as a poor one.
Case Study: Montreal Airport- Int. Pax
By referring to issue no. 68 Montreal Airport,
R- Squared (monthly model) = 98.01 % Annual
Growth = 10.01 %, Errors range: + 8.25 % - 6.41 %
Analysis:
The actual data of eight months of 2018 for YUL shows the performance of the airport, at
-5 % threshold level, keeping in mind the red color indicates below the targets level (but
greater than – 5.0 %), while the green one is good performance. The performance range :
– 6.70 % to +4.97 %.

8.
88
Let the Figures Talk – Issue No. 7
Defining Airport Performance in 2018
Case Study:
MiamiInternationalAirport
Let the Figures Talk:
Each Airport Forecasting for 2018 can be
reviewed, the forecasting analysis is FAIR,
when R-square is greater than 90 %, and
consequently the forecasted figures are set
as TARGETS to be achieved.
Errors: are the deviations between the
Historical Data with Model Fitted, always
addressing by a percentage range.
Performance: are the deviations between
the target figures and the actual figures for
the proposed target period (2018).
Results above targets are good performance
and represented by a green color, and
results below targets level are poor one,
represents by red color. However, airports
should set up their own right target levels
to define KPIs system. i.e less than (- 5.0 %
) should consider as a poor one.
Case Study: MIA Int. Airport
By referring to issue no. 75 Miami Int. Airport,
R- Squared (monthly model) = 81.60 % Annual
Growth = 00.00 %, Errors range: + 3.45 % - 4.49 %
Analysis:
The actual data of seven months of 2018 for MIA shows the performance of the airport, at
-5 % threshold level, keeping in mind the red color indicates below the targets level (but
greater than – 5.0 %), while the green one is good performance. The performance range :
– 1.73 % to + 3.96 %.