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Brussels airport forecast

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Mohammed Awad
Mohammed Awad

This study is addressing, Brussels Airport, Optimum solution vs Best practive, two approaches head to head for the main performance of the airport

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Setting Targets Mohammed Salem Awad Aviation Consultant Optimum Solution Vs Best Practice Brussels Airport Forecast
The wise choice “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
3 Outline Forecasting. Basic concept of the forecasting model Model constrains Max/min signal tracking approach Accuracy forecasting Matrix Setting Targets K.P.I level for the airports Case Study : Brussels Airport Basic Data Base Trend Analysis Scenarios Optimum Solution Passengers Forecasting Aircraft Movements Forecasting Cargo Forecasting Best Practice Icao Monitoring Report Passengers Forecasting Aircraft Movements Forecasting Cargo Forecasting Last Year – Passengers Last Year – Aircraft Cycles Results Passengers Aircraft Movements Cargo Summary Conclusion's
1- FORECASTING
1.1 Basic Concept of Forecasting Model
1.1 Basic Concept of Forecasting Model Directional Displacement
1.2 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
1.3 Max.& Min Signal Tracking Analysis
1.4 Accuracy Forecasting Matrix Case Study : ( Lufthansa Group )
1.5 Setting Targets Most of airports 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 levels for the airports
1.6 K.P.I level For Airports Key Performance Indicators Passengers (Pax) Aircraft Movements ( Cycles ) Cargo (Tonne)
2. Case Study : ( Brussels Airport ) Brussels Airport (IATA: BRU, ICAO: EBBR) (also called Brussel-Nationaal / Bruxelles-National (Brussels-National) or Zaventem) is an international airport 6.5 NM (12.0 km; 7.5 mi) northeast[2] of Brussels, the capital of Belgium. In 2015, more than 23 million passengers arrived or departed at Brussels Airport, making it the 21st busiest airport in Europe. It is located partially in Zaventem, partially in the Diegem area of Machelen,[3] and partially in Steenokkerzeel, in the Flemish Region of Belgium. It is home to around 260 companies, together directly employing 20,000 people and serves as the home base for Brussels Airlines, Jetairfly and Thomas Cook Airlines Belgium.
2.1 Basic Data Base ( Three years data ) 36 months data files
2.1 Basic Data Base ( Three years data ) 36 months data files
2.2 Trend Analysis Trend Analysis : The concept of trend analysis is to study the impact of displacement factor that will lead to minimize the error of the last year data over Optimum Solution.
2.2 Trend Analysis 1- Scenario One Optimum Solution 2- Scenario Two Best Practice
2.2 Trend Analysis
2.3 Scenarios First Scenario: Optimum Solution Second Scenario: Best Practice
2.3.1 First Scenario First Scenario: Optimum Solution This is the solution of minimum resulting errors, i.e the deviations are uniformly distributed on both sides of the trend line, we will find some errors here and some errors there over all the period (three years) , but it is the best solution to represents the data – by the highest R and min bound of signal tracking.
2.3.1.1 Passengers Forecasting Scenario One- Optimum Solution
2.3.1.2 Aircraft Movements Forecasting Scenario One- Optimum Solution
2.3.1.3 Cargo Forecasting Scenario One- Optimum Solution
2.3.2 Second Scenario Second Scenario: Best Practice It reflects the impact of most recent data in the analysis (Last Year), with the general trend model, this can be done by adding a new constrains for the model for last year data. The result, of that will be minimum deviations – this reflects the best practice to compare the recent data with same data of the same period of last year, the good thing here is defining the general trend of the model that reflects the last year data and used to forecast without stacking in the past..
2.3.2.1 ICAO Report Second Scenario: Best Practice Good example of using last year data to evaluate the performance in aviation industry is ICAO Monitoring Report, as shown in the graph. ICAO Monitoring Report – Aug 2016 But with out any inference of the trend just stacking in the past.
2.3.2.2 Best Practice 1- Scenario One Optimum Solution 2- Scenario Two Best Practice
2.3.2.3 Passengers Forecasting Scenario Two - Best Practice
2.3.2.3 Aircraft Movements Forecasting Scenario Two - Best Practice
2.3.2.4 Cargo Forecasting Scenario Two - Best Practice For Cargo Forecasting – Best Practice, this is not Applicable, as there are a lot of discrepancies and high deviations in last year Data. But in general the Model is Fair for the first scenario, as R square is 85% (greater than 80%) and the signal tracking is bounded by ±4.5 ( close to ±4)
2.4 Last Year - Passengers Optimum Solution Scenario Best Practice Scenario This slight change/ adjustment cause 2016 Pax forecast to drop by 1,178,043
2.5 Last Year– A/C Movements Optimum Solution Scenario Best Practice Scenario This slight change/ adjustment cause 2016 A/C movements forecast to drop by 7172 cycles
3. Results
3.1 Passengers Passengers Best Practice: (Second Scenario) 2016(F) = 25,491,735 Pax 2017(F) = 27,873,109 Pax at R2 = 98.4 % S.T.= ± 9.75 (2013-2015)
3.2 Aircraft Movements Aircraft Movements Best Practice: (Second Scenario) 2016(F) = 247,958 Cycle 2017(F) = 258,525 Cycle at R2 = 97.4 % S.T.= ± 8.78 (2013-2015)
3.3 Cargo Cargo ( Tonne ) Optimum Solution: ( First Scenario) 2016(F) = 525,207 Tonnes 2017(F) = 562,239 Tonnes at R2 = 85 % S.T.= ± 4.5 (2013-2015)
4. Summary First Scenario : Optimum Solution In this scenario we use a max/min signal tracking approach, which simply define the best possible solution for the fitting data, so to minimize the errors, it act to distribute on both side of the trend model, defining the general trend of all data at the highest value of R - square. This lead us to the following results R2 = 98.5 % S.T.= 6.47 (2013-2015) 2016(F) = 26,669,778 Pax 2017(F) = 29,687,771 Pax
4. Summary Second Scenario : Best Practice Based on the previous scenario , we add additional constrains to force the model to pass on the last year data, the impact of that is a slight change in R – square and expand range in signal tracking, but the result of forecasting is significantly change. This lead us to the following result R2 = 98.4 % S.T.= 9.75 (2013-2015) 2016(F) = 25,491,735 Pax 2017(F) = 27,873,109 Pax
5. Conclusions First : The scenario that has lower value forecast will be the fair one. Second : Best Practice - Scenario By setting the new constrains in the model, this will impact the general trend of the previous scenario , we add additional constrains to force the model to pass on the last year data, the impact of that is a slight change in R – square and expand range in signal tracking, but the result of forecasting is significantly change. This lead us to the following result 2016(F) = 25,491,735 Pax 2017(F) = 27,873,109 Pax 2016(F) = 247,958 Cycle 2017(F) = 258,525 Cycle
Conclusions 2016(F) = 25,491,735 Pax 2017(F) = 27,873,109 Pax 2016(F) = 247,958 Cycle 2017(F) = 258,525 Cycle
39 Thank You

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Brussels airport forecast

  • 1. Setting Targets Mohammed Salem Awad Aviation Consultant Optimum Solution Vs Best Practice Brussels Airport Forecast
  • 2. The wise choice “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
  • 3. 3 Outline Forecasting. Basic concept of the forecasting model Model constrains Max/min signal tracking approach Accuracy forecasting Matrix Setting Targets K.P.I level for the airports Case Study : Brussels Airport Basic Data Base Trend Analysis Scenarios Optimum Solution Passengers Forecasting Aircraft Movements Forecasting Cargo Forecasting Best Practice Icao Monitoring Report Passengers Forecasting Aircraft Movements Forecasting Cargo Forecasting Last Year – Passengers Last Year – Aircraft Cycles Results Passengers Aircraft Movements Cargo Summary Conclusion's
  • 5. 1.1 Basic Concept of Forecasting Model
  • 6. 1.1 Basic Concept of Forecasting Model Directional Displacement
  • 7. 1.2 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
  • 8. 1.3 Max.& Min Signal Tracking Analysis
  • 9. 1.4 Accuracy Forecasting Matrix Case Study : ( Lufthansa Group )
  • 10. 1.5 Setting Targets Most of airports 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 levels for the airports
  • 11. 1.6 K.P.I level For Airports Key Performance Indicators Passengers (Pax) Aircraft Movements ( Cycles ) Cargo (Tonne)
  • 12. 2. Case Study : ( Brussels Airport ) Brussels Airport (IATA: BRU, ICAO: EBBR) (also called Brussel-Nationaal / Bruxelles-National (Brussels-National) or Zaventem) is an international airport 6.5 NM (12.0 km; 7.5 mi) northeast[2] of Brussels, the capital of Belgium. In 2015, more than 23 million passengers arrived or departed at Brussels Airport, making it the 21st busiest airport in Europe. It is located partially in Zaventem, partially in the Diegem area of Machelen,[3] and partially in Steenokkerzeel, in the Flemish Region of Belgium. It is home to around 260 companies, together directly employing 20,000 people and serves as the home base for Brussels Airlines, Jetairfly and Thomas Cook Airlines Belgium.
  • 13. 2.1 Basic Data Base ( Three years data ) 36 months data files
  • 14. 2.1 Basic Data Base ( Three years data ) 36 months data files
  • 15. 2.2 Trend Analysis Trend Analysis : The concept of trend analysis is to study the impact of displacement factor that will lead to minimize the error of the last year data over Optimum Solution.
  • 16. 2.2 Trend Analysis 1- Scenario One Optimum Solution 2- Scenario Two Best Practice
  • 18. 2.3 Scenarios First Scenario: Optimum Solution Second Scenario: Best Practice
  • 19. 2.3.1 First Scenario First Scenario: Optimum Solution This is the solution of minimum resulting errors, i.e the deviations are uniformly distributed on both sides of the trend line, we will find some errors here and some errors there over all the period (three years) , but it is the best solution to represents the data – by the highest R and min bound of signal tracking.
  • 21. 2.3.1.2 Aircraft Movements Forecasting Scenario One- Optimum Solution
  • 22. 2.3.1.3 Cargo Forecasting Scenario One- Optimum Solution
  • 23. 2.3.2 Second Scenario Second Scenario: Best Practice It reflects the impact of most recent data in the analysis (Last Year), with the general trend model, this can be done by adding a new constrains for the model for last year data. The result, of that will be minimum deviations – this reflects the best practice to compare the recent data with same data of the same period of last year, the good thing here is defining the general trend of the model that reflects the last year data and used to forecast without stacking in the past..
  • 24. 2.3.2.1 ICAO Report Second Scenario: Best Practice Good example of using last year data to evaluate the performance in aviation industry is ICAO Monitoring Report, as shown in the graph. ICAO Monitoring Report – Aug 2016 But with out any inference of the trend just stacking in the past.
  • 25. 2.3.2.2 Best Practice 1- Scenario One Optimum Solution 2- Scenario Two Best Practice
  • 27. 2.3.2.3 Aircraft Movements Forecasting Scenario Two - Best Practice
  • 28. 2.3.2.4 Cargo Forecasting Scenario Two - Best Practice For Cargo Forecasting – Best Practice, this is not Applicable, as there are a lot of discrepancies and high deviations in last year Data. But in general the Model is Fair for the first scenario, as R square is 85% (greater than 80%) and the signal tracking is bounded by ±4.5 ( close to ±4)
  • 29. 2.4 Last Year - Passengers Optimum Solution Scenario Best Practice Scenario This slight change/ adjustment cause 2016 Pax forecast to drop by 1,178,043
  • 30. 2.5 Last Year– A/C Movements Optimum Solution Scenario Best Practice Scenario This slight change/ adjustment cause 2016 A/C movements forecast to drop by 7172 cycles
  • 32. 3.1 Passengers Passengers Best Practice: (Second Scenario) 2016(F) = 25,491,735 Pax 2017(F) = 27,873,109 Pax at R2 = 98.4 % S.T.= ± 9.75 (2013-2015)
  • 33. 3.2 Aircraft Movements Aircraft Movements Best Practice: (Second Scenario) 2016(F) = 247,958 Cycle 2017(F) = 258,525 Cycle at R2 = 97.4 % S.T.= ± 8.78 (2013-2015)
  • 34. 3.3 Cargo Cargo ( Tonne ) Optimum Solution: ( First Scenario) 2016(F) = 525,207 Tonnes 2017(F) = 562,239 Tonnes at R2 = 85 % S.T.= ± 4.5 (2013-2015)
  • 35. 4. Summary First Scenario : Optimum Solution In this scenario we use a max/min signal tracking approach, which simply define the best possible solution for the fitting data, so to minimize the errors, it act to distribute on both side of the trend model, defining the general trend of all data at the highest value of R - square. This lead us to the following results R2 = 98.5 % S.T.= 6.47 (2013-2015) 2016(F) = 26,669,778 Pax 2017(F) = 29,687,771 Pax
  • 36. 4. Summary Second Scenario : Best Practice Based on the previous scenario , we add additional constrains to force the model to pass on the last year data, the impact of that is a slight change in R – square and expand range in signal tracking, but the result of forecasting is significantly change. This lead us to the following result R2 = 98.4 % S.T.= 9.75 (2013-2015) 2016(F) = 25,491,735 Pax 2017(F) = 27,873,109 Pax
  • 37. 5. Conclusions First : The scenario that has lower value forecast will be the fair one. Second : Best Practice - Scenario By setting the new constrains in the model, this will impact the general trend of the previous scenario , we add additional constrains to force the model to pass on the last year data, the impact of that is a slight change in R – square and expand range in signal tracking, but the result of forecasting is significantly change. This lead us to the following result 2016(F) = 25,491,735 Pax 2017(F) = 27,873,109 Pax 2016(F) = 247,958 Cycle 2017(F) = 258,525 Cycle
  • 38. Conclusions 2016(F) = 25,491,735 Pax 2017(F) = 27,873,109 Pax 2016(F) = 247,958 Cycle 2017(F) = 258,525 Cycle