This presentation is a comparison between by a planned system and classical system, in terms of controlling seasonality and annual forecasting, the final accuracy is measured by S. D.

1. Driving Aviation Business
To Optimum Level
( Forecasting )
By : Mohammed Salem Awad
Adviser
Yemenia
Date: 20 March 2012

2. Forecasting
"The easiest way to predict
the future is to invent it."
Immanuel Kant
German Philosopher
Immanuel Kant

3. Outline
- Introduction
- Forecasting as planning tool for airlines and airports.
- Models of forecasting and their implementations in
practice.
- Factors, measuring the accuracy of forecasting.
- Defining airline seasonality model. Short term
forecasting
- Impact of the human touch in refinery the forecasting
results.
- Case Study
- Summary
- Contact

4. Forecasting
- Introduction
-Forecasting play a major roles in
Aviation.
- Industry Forecast
- ICAO , IATA, AIRBUS, BOEING,
- FAA
- Fleet Forecast, AIRBUS, BOEING
- Traffic Forecast, Airlines and
Airports
- Financial Forecast

5. Forecasting
- Forecasting as planning tool for airlines and airports.
- Airline Starting up
- Budget preparation
- Opening new route
- Airport Expansion
- Setting Targets
- Maintenance Planning
- Defining Seasonality
- Financial Planning

6. Forecasting
- Models of forecasting and their implementations in practice.

7. Forecasting
Trend Forecasting
Tell us in which direction (Growth) of
the historical data, and usually is a
long term forecast.
Seasonal Forecasting
Tell us the Seasonal, Cyclic shocks,
we used it to define the forecasting
Pattern
Trend vs Seasonal Forecasting
Forecasted Year of TREND
= Sum of 12 forecasted Seasonal
Months for same year,
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8. Forecasting
- Measuring the accuracy of forecasting- Model Fairness
- Coefficient of Correlation
- Signal Tracking
Evaluation Forecasting
R2 = Coef. Of Determination T. S. = Tracking Signal
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9. Forecasting
- Measuring the accuracy of forecasting- Model Fairness
- Coefficient of Correlation R
- Tracking Signal T.S.
 Two Main factors: (conditions)
R2 > 80%
AND
-4 < T.S.< 4
R2 = Coef. Of Determination T. S. = Tracking Signal
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10. Forecasting
- Defining Airline Seasonality Model. Short term forecasting

11. Forecasting
- Traffic Forecasting
Selecting the right forecasting technique is the most successful factor,
since the forecasting pattern of airlines are subjected to many
elements, and each route characterized by its growth and seasonality
patterned, in term of seasonality, it is subjected to summer, winter, back
to school, Haj and Umora.
Basic Mathematical Output
Data Model (Results)
(Passengers)

12. Forecasting
- Defining Airline Seasonality Model.

13. Forecasting
- First Trail

14. Forecasting
- First Trail

15. Forecasting
- Impact of the human touch in refinery the forecasting results.
- By Adjusting the Model parameters; in second trail

16. Forecasting
Airlines

17. Forecasting

18. Forecasting

19. Forecasting

20. Forecasting

21. Forecasting
Airports

22. Forecasting

23. Forecasting

24. Forecasting

25. Case Study:
Amsterdam- Airport Schiphol
Setting Goals
And Targets
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26. Targets
Targets:
Most of the airlines and airports working on
achieving goals, targets, and evaluate their
achievements by comparing the current achieved
results to results of previous week, month, or year
i.e looking backward to analysis current situation.
But for setting targets we have to look forward,
forecast, develop a plan for current situation, to
achieved these targets in future in most efficient
way, so we can compare the current achievement by
the target one, here we can measure our
performance & KPI.
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27. Classical Vs Planning System
Classical System Planning System
Comparing with Past Values Comparing with Planned Targets
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28. Forecasting
Amsterdam - Airport Schiphol
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29. Amsterdam- Airport Schiphol
Input Data: 1992 - 2011 ( October)
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30. Amsterdam- Airport Schiphol
Input Data –
Passengers Total –
Column 6 in slide no. 27
Trend Analysis
y = 1E+07Ln(x) + 1E+07
R2 = 0.9319
Result:
Forecast (2011) = 46,801,687 Pax
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31. Amsterdam- Airport Schiphol
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32. Amsterdam- Airport Schiphol
Results:
R2 = 0.9319
Forecast (2011)
= 46,801,687 Pax
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33. Amsterdam- Airport Schiphol
Input Data –
Seasonal Model
2008,
2009,
2010
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34. Amsterdam- Airport Schiphol
1- Optimum Solution (Pax2010 > Pax2011 Forecast))
i.e 45,136,967 > 41,626,027 is not Practical)
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35. Amsterdam- Airport Schiphol
2- Practical Case – Seasonal Model
2011(Forecast) = 46,801,687 Pax
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36. Amsterdam- Airport Schiphol
2- Practical Case – Seasonal Model
2011(Forecast) = 46,801,687 Pax
 n =12 
∑
 i= 1
Monthi = 46,801,687 Pax 
 2011 Forecast
 
46,801,687
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37. Amsterdam
37

38. Amsterdam- Airport Schiphol
Comparison of Results
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39. Summary
 Most of the airlines practice the classical methods, they
evaluate their current performance based on the past results, they just
looking to the back only for one Year ( or same period as month).
 While this study explore the effect of historical data in terms
of trends forecast, in which direction the airline business moves, and
the second part is addressing the short term impacts of seasonality
(here months) based on three (3) years monthly data base, keeping in
minds the model fairness constrains i.e (R2) and (T.S.) to minimise the
forecasting errors, then compare the forecasted/planned figures by the
actual one.
 The new constrain for this model is to match the accumulated forecasted
months by (Seasonal Model – 3 years data base) with the proposed forecasted year of
Trend analysis (Trend Model – 19 years data base).
Results:
By Planning method the accuracy is high in terms of Standard Deviation i.e 0.037
while the Classical method is 0.092.
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40. Contact
Further Information:
Mohammed Salem Awad
Tel: 00967 736255814
Email: mohammed.hadi@yemenia.com smartdecision2002@yahoo.com
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