Reading in the future2
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ReadingIn The
FUTURE
By: Mohammed S. AwadChairman Adviser Yemenia
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Reading In The Future
“Excellence is never an accident. It is always the result of high intention, sincere effort, and intelligent execution; it represents the wise choice of many alternatives - choice, not chance, determines your destiny.”
― Aristotle
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Outline 1/2
• Introduction• Key Performance Indicators For Airlines• Forecasting – Basic concept of forecasting Model– Forecasting – Trend vs. Seasonality– Model Constrains– Max.& Min Signal Tracking Analysis– Accuracy Forecasting Matrix
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Outline 2/2
• Case Study : ( Lufthansa Group ) • Basic Data Base ( Three years data )• Forecasting– Lufthansa Group - Passengers– Lufthansa Group – Flights– Lufthansa Group – ASK– Lufthansa Group – RPK– Expected Load Factor
• Forecasting Accuracy Matrix (Lufthansa Group)
• SUMMARY
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Introduction – Clear Objectives
Most of airlines in the world working on a clear objectives and that’s come with clear targets which lead us to set a clear picture of forecasting process.Based on that, our objective is to develop a clear massage for top managements for the key performance figures of the airline, not just to compare month by month approach but to develop the right path ( time series ) in the future to set the right targets which consequently develop K.P. I for the airlines
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K.P.I For Airlines ( Lufthansa Group )
• Key Performance Figures ( June 2014 )
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K.P.I For Airlines
• K. P. I for Lufthansa Group:
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FORECASTING
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Basic concept of forecasting Model
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Basic concept of forecasting Model
Directional Displacement
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Basic concept of forecasting Model
Evaluation Forecasting
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Forecasting – Trend vs. Seasonality
Trend ForecastingTell us in which direction (Growth) of the historical data, and usually is a long term forecast.Seasonal ForecastingTell us the Seasonal, Cyclic shocks, we used it to define the forecasting Pattern
Trend vs Seasonal ForecastingForecasted Year of TREND
= Sum of 12 forecasted Seasonal Months for same year,
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Model Constrains
Two Main Constrains to get a fair model:
R2 = Coef. Of Determination T. S. = Tracking Signal
R2 > 80%
AND
-4 < T.S.< 4
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Max.& Min Signal Tracking Analysis
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Accuracy Forecasting Matrix
• Case Study : ( Lufthansa Group )
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Case Study : ( Lufthansa Group )
The Lufthansa Group is an aviation group with global operations and a total of almost 500 subsidiaries and associated companies. It consists of five business segments, whichbusiness segments, which encompass the areas of passenger transportation and airfreight, as well as downstream services: Passenger Airline Group, Logistics, MRO, Catering and IT Services. All the segments are market leaders in their respective areas.
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Basic Data Base ( Three years data )
• 36 months data files
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Forecasting
• Forecasting– Lufthansa Group - Passengers– Lufthansa Group – Flights– Lufthansa Group – ASK– Lufthansa Group – RPK– Expected Load Factor
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Lufthansa Group - Passengers
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Lufthansa Group - Flights
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Lufthansa Group – ASK
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Lufthansa Group - RPK
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Expected Load Factor -2014
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Reading In the Future
• Analysis:– Passengers: – there will be slight reduction in Passengers – Flights: - due to the presence of A380 there will be a
significance reduction in 2014, due to large capacity of A380 – ASK:- also ASK will tend to be less – RPK: - this factor will be stable, as LH will keep to serve their
markets – Expected Load Factor ( L/F ) : Since RPK stable and ASK will
slightly decrease. This will lead to increase the expected load factor.
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Forecasting Accuracy Matrix • Forecasting Accuracy :
– RPK is fair as it is satisfies the constrains of the forecasting.
– Passengers is also fair as the mislead is denied by Max/Min T. S. Analysis
( errors are distributed on both sides of the trend line).– ASK is also fair as the
mislead is denied by Max/Min T. S. Analysis.
– Flights is also fair as the mislead is denied by Max/Min T. S. Analysis.
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Summary• Most of Investors in Airline Industry are concerned for the
performance factors that’s Passengers, RPK ,ASK , and Load Factor. They evaluate them by comparing their values in past according to month by month approach.
• This presentation tilling us the future patterns for these factors, which consequently we can develop and forecast the expected Load Factor.
• This also will help the airline to set their targets, and developed the right KPI policy for measuring airline performance.
• The data is fairly fitted, with a minimum errors.
• The results shows that there will be slight decrease in ASK, with stability for RPK, THIS WILL LEAD TO INCREASE IN EXPECTED LOAD FACTOR IN THE FUTURE (2014).
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Welcome In The Club
Thanks !
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Contact
• Mohammed Salem Awad • Chairman Adviser – Yemenia • Tel: 00967 736255814• Email: [email protected]