Reading in the future2

ReadingIn The

FUTURE

By: Mohammed S. AwadChairman Adviser Yemenia

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

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

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

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

K.P.I For Airlines ( Lufthansa Group )

• Key Performance Figures ( June 2014 )

K.P.I For Airlines

• K. P. I for Lufthansa Group:

FORECASTING

Basic concept of forecasting Model

Basic concept of forecasting Model

Directional Displacement

Basic concept of forecasting Model

Evaluation Forecasting

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,

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

Max.& Min Signal Tracking Analysis

Accuracy Forecasting Matrix

• Case Study : ( Lufthansa Group )

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.

Basic Data Base ( Three years data )

• 36 months data files

Forecasting

• Forecasting– Lufthansa Group - Passengers– Lufthansa Group – Flights– Lufthansa Group – ASK– Lufthansa Group – RPK– Expected Load Factor

Lufthansa Group - Passengers

Lufthansa Group - Flights

Lufthansa Group – ASK

Lufthansa Group - RPK

Expected Load Factor -2014

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.

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.

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).

Welcome In The Club

Thanks !

Contact

• Mohammed Salem Awad • Chairman Adviser – Yemenia • Tel: 00967 736255814• Email: [email protected]