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Reading In The Future 1
Predicating 2016
Airline Industry Performance
Reading In The
FUTURE

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Reading In The Future 2
Predicating 2016
Airline Industry Performance
By : Mohammed Salem Awad1
Aviation Consultant
One of the major issues in the airline industry is setting
targets/forecasting, in the recent time, multiple aviation
sources report different predicated figures, based on
their analysis, and sight for the aviation market, off
course there is a well-known brand names and
companies doing these analysis, as IATA, ICAO,
ACI, Boeing, Airbus, and many Worldwide
companies. The dilemma, that, there is no basic
foundation and rules for the predicators, especially
manufacture companies as everyone comes with their
own assumptions and approaches which lead to
different results, yes there are many indicators to
testify the results, one of these is R square –
Coefficient of Determination, even though this
indicator is not fairly enough to implemented and get
the complete picture, It is worldwide aviation practice.
In this article, a new concept is addressed, and two
testify parameters are used. First is R - square–
Coefficient of Determinations and Signal Tracking,
S. T. , they are used as mapping tool on x-y
coordinates for Displacement and Rotational factors
that governed the mathematical model.
Aviation Data:
ICAO and IATA are considered the main data
source for aviation industry,
IATA - International Air Transport Association
- is a trade association of the world’s airlines.
About 250 airlines, primarily major carriers, carry
approximately 84% of total Available Seat
Kilometers - ASK of air traffic. IATA supports
airline activity and helps formulate industry policy
and standards. So airlines are the main stream
source of traffic data for IATA.
ICAO - International Civil Aviation Organization, is a specialized agency of the United
Nations. It codifies the principles and techniques of international air navigation and fosters the
1
Mohammed S. Awad, MBA,
Mobile: 00967735222692,
Email: smartdecision2002@yahoo.com
Aviation
Data
“Give me a place to stand on,
and I will move the Earth”
Archimedes
287 B.C. – 212 B.C.

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Reading In The Future 3
planning and development of international air transport to ensure safe and orderly growth.
ICAO is distinct from IATA, with the Civil Air Navigation Services Organization (CANSO),
an organization for Air Navigation Service Providers (ANSPs). These are trade associations
representing specific aviation interests, whereas ICAO is a body of the United Nations. While
airports are the main source of traffic data for ICAO. The main data source for this article is
the newsletter by ICAO, Economic Development – Air Transport Monthly Monitor. As
shown in the figures. The basic performance parameters for airlines are ASKs, RPKs, and
Load Factor.
Forecasting Model :
The classical method to forecast is to drag
and drop a trend line by using ADD
TREND LINE by Microsoft Excel, but
unfortunately that box measure only one
measuring critical element i.e coefficient
of Determination R2
and that may lead us
to Mislead region in a Fair – Poor
Forecasting Matrix.
The basic data span is 36 months ( Input )
with 12 months forecasting, the fair
boundary restricted by the preset design
values of R2
and Signal Tracking. Both
data of ICAO and IATA are addressed.
Actually the forecasting process has two
stages, Evaluation, and Forecasting. In the
evaluation stage we try to analysis the
input data, and align the practical data
with a mathematical model, we use state
of art forecasting program to fit data. Two control factors have a great impact on the model,
First displacement factor ( Displacement Issue ), this factor acts to shift the whole data from
its running path to a new one but keeping the trend and direction of the analysis. While the
second factor is Directional factor, definitely if we manipulate this factor and try to use many
trail values (positive and negative value), the model will position itself accordingly as a clock
about the origin. As in the above forecasting graph.
Fair – Poor Forecasting Matrix.:
One of a new measuring accuracy methods. It is
basically developed by two main estimated
mathematical parameters, Displacement and
Directional factors which have a consequence
impacts on R2
and Signal Tracking, so by setting
boundary accuracy (Constrains ):
For Fair forecasting, the model should fulfill these
criteria
R2
≥ 80 and
Signal Tracking should be - 4 ≤ S. T. ≤ + 4
Then by developed Fair – Poor Forecasting Matrix
the following outcomes will be concluded

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Reading In The Future 4
1- Fair Forecast – when R2
and Signal Tracking are in boundary .
2- Mislead – Displacement Issue. This case when R2
is in boundary and Signal Tracking
is out boundary. we can adjusted signal tracking to be in boundary when there is a
room for R2
in the same analysis so that it can be consider as a fair forecast.
3- Unrelated – Directional Issue. This case when R2
is out of the boundary and Signal
Tracking in the boundary. i.e the balance of accumulated error without any correlation
4- Poor Forecast – when both R2
and Signal Tracking are out of the boundary ( Total
Mess).
This matrix manipulate the four decision regions to develop the right and best picture of the
accuracy of forecasting. And to enhance the process of decision making for airline data
analysis especially traffic forecasting, that maps the overall forecasting accuracy of Airline
Industry Performance in terms of Load Factor .
Forecasting of ASK, RPK ( ICAO )
The analysis period is 36 months starting from Nov.2012 to Oct. 2015 for ASK and RPK.
The analysis shows the seasonality pattern of aviation industry figures for ASK and RPK,
which fairly fitted at R, 99.6 and 99.2 respectively.
In July and August shows the peaks values for the model while the activities reached to
lowest levels is in November and February.
Based on the reported figures of ICAO, the load factor is evaluated which is simply equal
RPK/ASK, this includes all aviation activities in the world with non-registered airlines with
IATA, while IATA represents about 84 % of ASK, the remaining 16% represents the others
airlines some of them in Russia ,African countries and other UN aviation activities.

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Reading In The Future 5
Predicating L/F based on ICAO Data
The reason to forecast ASK and RPK, is to evaluate the
L/F ( Load Factor ) as L/F equal RPK divided by ASK,
so a new data series based on total population are in our
hands. While the basic source of ASK and RPK is the
ICAO newsletter i.e Economic Development – Air
Transport Monthly Monitor which explore many useful
analysis and comparison. So most of the aviation
companies use the classical method in defining their
performance, by implementing month by month or year
to year comparison approach. ( i.e means looking to the
previous periods), so what we can get from this source is
the estimating figures of closest month.
The new data set is examine first by Min/Max Signal
Tracking approach, for 36 months period and calculating
the value of R – square . At the evaluation stage period,
the model is fair enough to proceed in forecasting process
as S.T = ± 3.90 (2013-2015) and R square = 87 %
So based on ICAO data , the expected Load Factor for
2016 = 79.87 %.
The model shows a lot of devoted data from the actual as
the population cover a non – IATA operators,
unscheduled flights, emergency flights, the peak season
will be on August and lowest one will be on November
.

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Reading In The Future 6
Predicating L/F based on IATA Data
Iata data does not represent the whole population figures of
the airline industry, it only reported 84 % of total sampling
of airline industry, while the quality of data is high which
shows clearly the seasonality’s periods of airline industry at
an excellent level data fitting, some analysis reported by air
passengers market analysis – IATA , using the concept of
the percentile changes either for month by month or year by
year which clearly declare the event at one time or period.
So IATA data are examine first by Min/Max Signal
Tracking approach, for 36 months period and calculating
the value of R – square . At the evaluation stage period, the
model is highly fair for the process of forecasting since S.T
= ± 3.35 (2013-2015) and R square = 94.9 % which
satisfies the pre-set constrains.
So based on IATA data , the expected Load Factor for 2016
= 80.26 %.
The model shows a smooth sequence of data flow from the
actual reading, defining the right seasonality patterned for
Airline Industry, the peak season will be on August
and lowest one will be on November.

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Reading In The Future 7
Implementation of Accuracy Forecasting Matrix.:
Four main performance factors are evaluated, i.e ASK, RPK, & L/F by ICAO and Load
Factor by IATA as it is mentioned in the following table:
And accordingly accuracy forecasting matrix is
constructed, exploring a clear picture for
accuracy and fairness of the analysis. Three
factors are consider Fair situation while one
consider Mislead, but by referring to the graph
of ASKs, we found the data were spread on both
sides of trend line which clearly shows by signal
tracking values ± 6.22 so in this case we can
consider ASKs also a Fair situation. R – square
for ASKs and RPKs very high, and this indicate
a strong relation between actual data and the
modeling values. The best performance factor is
Load Factor by IATA,
Results :
The expected ASK for 2016
will be = 8504 Billions
With a growth rate :5.40 %
The expected RPK for 2016
will be = 6778 Billions
With a growth rate :5.43 %
The expected Load Factor
for 2016 ( ICAO) =
79.66 %
The expected Load Factor
for 2016 ( IATA) =
80.26 %

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Reading In The Future 8
Summary
Two data aviation sources are addressed in this study ( ICAO and IATA), and a new analysis
concept is applied – Max/Min Signal Tracking approach – to forecast the Airline Industry
Performance in terms of Load Factor, while the accuracy is mapping on Forecasting Matrix,
defining the complete picture of the analysis. Three performance factors are in a Fair situation
i.e RPKs, L/F (ICAO), and L/F(IATA), while ASKs is the only performance factor that may
indicate a Mislead situation, but that also denied by the visual view for the forecasting graph
and the values of Max/Min values on both sides of the trend line by ± 6.22, the best analysis is
IATA Load Factor, which shows, the Airline Industry Performance ( Load Factor ) will be
80.26 % ■
Appendix:
Errors Evaluation:
By comparing actual data vs model values for the period – from Nov.2012 to Oct. 2015, the
maximum error is not exceed 2.5 %,