Future Performance

Reading In The Future (5)Case Study

83 % Prepared By

Mohammed Salem AwadhAviation Consultant

Future Performance

“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• Key Performance

Indicators For Turkish Airlines

• Errors Vs KPIs• Forecasting (Model)

– Basic concept of forecasting Model

– Model Constrains– Max.& Min Signal Tracking

Analysis– Accuracy Forecasting Matrix

• Case Study : ( TK ) • Basic Data Base ( Three

years data )• Forecasting ( Actual )

– Traffic Passengers 2015-2016– RPK 2015 - 2016– ASK 2015 – 2016– Load Factor 2015 -2016– Distance Km 2015-2016– Cycles 2015-2016– Traffic 2015 -2016 – Cargo and Mail 2015 -2016

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Outline 2/2

• Forecasting Accuracy Matrix • Analysis• Results• Summary • Contact

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Performance Factors of Turkish Airline

- Seven Performance Factors that Turkish airline are addressing in their reports , Mainly

1- Number of Landing, 2- Available Seat Km. 3- Revenue Passenger Km, 4- Passengers Load Factor (%) , 5- Passenger Carried, 6- Cargo And Mails,7- Km Flown.

- So most of airlines 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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Performance Factors of Turkish Airline

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K.P.I For Airlines

• Key Performance Figures• Capacity = ASK( available seat Kilometers )

• Traffic = RPK ( revenue passenger Kilometers )

• Load Factor ( LF ) = RPK/ASK

Traffic

Capacity Load Factor

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K.P.I For Airlines• K. P. I for Lufthansa Group:

Each Airline has its own KPIs policy, LANDING, ASK , RPK, PAX & L/F are main measuring KPIs for

Turkish Airline

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Errors Vs KPIs - RYANAIR

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FORECASTING

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FORECASTING• Forecasting is a unique science, very useful in practice, and use widely in many fields,

especially Aviation.• Today we are concerning on the application of Forecasting for Airlines, and also

Airports. Airlines can define their seasonality, and its impacts on operations and maintenance programs, we have to define the right demand for the right sector in the right segment.

• While most of Investors in Airline Industry are concerned for the performance factors that’s Traffic and Capacity , RPK ,ASK , and Load Factor. They evaluate them by comparing their values in past according to month by month approach. here we look forward, to future to set targets.

• Forecasting is tilling us the future patterns for these factors, which consequently, we can develop and forecast the expected Load Factor which means also define the future performance for airlines.

• There are many methods of forecasting, but the approach of Max/Min Signal Tracking, deliver the best scenario for the data that can be analyzed. No grey region, just in a black and white / Good or Bad based on the constrains that we are applied.

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Basic concept of forecasting Model

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Basic concept of forecasting Model

Directional Displacement

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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 AnalysisMaximum & Minimum Signal Tracking Analysis - It is almost as Quality Control Chart, that bond all

values in the control limits, but by adjusting the values of the two basic elements in the forecasting model, (Displacement and Rotational one) we try to satisfies the constrains i.e ± 4 – Accepted Region , if not we have to match the values of max & min as a final solution for best value of R (Coeff. of correlation ).

- By implementing this approach, we can get the best answer ( in black or white no grey answer).

R2 > 80%

AND

-4 < T.S.< 4

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Accuracy Forecasting MatrixBy setting the constrains for Accuracy of Forecasting, Four possible outcomes we can get : Fair, Mislead, Unrelated and Poor

R2 > 80%

AND

-4 < T.S.< 4

Fair : All Constrains are satisfied.Mislead: Even R is GOOD, while T.S. is not, the possibility of Mislead is there, due to displacement effect. (almost Fair – need visual sight)Unrelated : R is poor even T.S. is GOOD. ( no relation )Poor : Both ( R + T.S) are out of the constrains region.

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Case Study :

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Basic Data Base (Three years data: 2012-2014)

36 months data

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Total PassengersSegment

2015-2016

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Total Passengers Forecasting 2015-2016

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(RPKs) Revenue Pax KilometersForecasting - (2015-2016)

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Revenue Passenger Kilometers - RPKs Forecast 2015 - 2016

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(ASKs) Available Seat KilometersForecasting - (2015-2016)

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Available Seat Kilometers - ASKs Forecasting - (2015-2016)

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Load Factor % Forecasting – (2015-2016)

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Load Factor % Forecasting – (2015-2016)

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( Distance Flown )Kilometers × 1000 - Km

Forecast 2015 - 2016

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Kilometers × 1000 - KmForecasting 2015 - 2016

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CYCLES - Total Forecasting – (2015-2016)

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Cycles – Total Forecasting – (2015-2016)

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GARGO SegmentCARGO and MAIL (tons)Forecasting 2015-2016

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CARGO and MAIL (tons)Forecasting 2015-2016

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Accuracy Forecasting Matrix

The outcomes of Accuracy Forecasting Matrix are :

- PAX, RPK, ASK, Flights, Cargo & Mails, and Km are in (Mislead) but they are almost Fair as R is High and S.T. lay on both sides of trend line i.e displacement effect is Zero not on one side, but they are greater than ±4 so they lay on the actual data.

- Load Factor is in Poor region.

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Analysis – Seven parameters are addressed in the analysis, all shows a positive

trend.– Many performance parameters are studied as – Traffic : - in terms of Passengers ( Pax )– Capacity :- in terms of Available Seat Kilometers (ASKs)– Load Factor :- it is the outcome of RPK/ASK.– Distance :- in terms of Kilometers.– Cargo and Mail : in terms of tons. – The period of 2015-2016 is forecasted for the propose of setting targets . – The study shows, that Turkish Air work in many business unit to develop a

business chains i.e travelers, tourism and cargo in a feeding loop system.– The outcomes are fairs with very high Coefficient of Correlations.

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Results

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Summary• Most of Investors in Airline Industry are concerned for the performance

factors that’s Traffic and Capacity , RPM ,ASM , 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.

• Most of the data are fairly fitted, with a minimum errors, Except Load Factor comes with a poor forecast.

• The results shows that there will be a significant increase in Passengers, ASK, RPK, CYCLES, Kilometers. While the forecasting results for load factor is not accurate figure due to low coefficient of correlations and low value of signal tracking.

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Reading In The Future

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Welcome In The Club

Thanks !

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Contact

• Mohammed Salem Awad • Chairman Adviser – Yemenia • Tel: 00967 735222692• Email: mohammed.hadi@yemenia.com smartdecision2002@yahoo.com