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Forecasting - MENA 2012 Conference

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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.

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Forecasting - MENA 2012 Conference

  1. 1. Driving Aviation BusinessTo Optimum Level( Forecasting )By : Mohammed Salem AwadAdviserYemeniaDate: 20 March 2012
  2. 2. Forecasting "The easiest way to predict the future is to invent it."Immanuel Kant German Philosopher Immanuel Kant
  3. 3. Outline- Introduction- Forecasting as planning tool for airlines and airports.- Models of forecasting and their implementations inpractice.- Factors, measuring the accuracy of forecasting.- Defining airline seasonality model. Short termforecasting- Impact of the human touch in refinery the forecastingresults.- Case Study- Summary- Contact
  4. 4. Forecasting- Introduction-Forecasting play a major roles inAviation.- Industry Forecast- ICAO , IATA, AIRBUS, BOEING,- FAA- Fleet Forecast, AIRBUS, BOEING- Traffic Forecast, Airlines andAirports- Financial Forecast
  5. 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. 6. Forecasting- Models of forecasting and their implementations in practice.
  7. 7. ForecastingTrend ForecastingTell us in which direction (Growth) ofthe historical data, and usually is along term forecast.Seasonal ForecastingTell us the Seasonal, Cyclic shocks,we used it to define the forecastingPatternTrend vs Seasonal ForecastingForecasted Year of TREND= Sum of 12 forecasted SeasonalMonths for same year, 7
  8. 8. Forecasting- Measuring the accuracy of forecasting- Model Fairness- Coefficient of Correlation- Signal Tracking Evaluation Forecasting R2 = Coef. Of Determination T. S. = Tracking Signal 8
  9. 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 9
  10. 10. Forecasting- Defining Airline Seasonality Model. Short term forecasting
  11. 11. Forecasting- Traffic ForecastingSelecting the right forecasting technique is the most successful factor, since the forecasting pattern of airlines are subjected to manyelements, and each route characterized by its growth and seasonalitypatterned, in term of seasonality, it is subjected to summer, winter, backto school, Haj and Umora. Basic Mathematical Output Data Model (Results) (Passengers)
  12. 12. Forecasting- Defining Airline Seasonality Model.
  13. 13. Forecasting- First Trail
  14. 14. Forecasting- First Trail
  15. 15. Forecasting- Impact of the human touch in refinery the forecasting results.- By Adjusting the Model parameters; in second trail
  16. 16. ForecastingAirlines
  17. 17. Forecasting
  18. 18. Forecasting
  19. 19. Forecasting
  20. 20. Forecasting
  21. 21. ForecastingAirports
  22. 22. Forecasting
  23. 23. Forecasting
  24. 24. Forecasting
  25. 25. Case Study:Amsterdam- Airport Schiphol Setting Goals And Targets 25
  26. 26. TargetsTargets:Most of the airlines and airports working onachieving goals, targets, and evaluate theirachievements by comparing the current achievedresults to results of previous week, month, or yeari.e looking backward to analysis current situation.But for setting targets we have to look forward,forecast, develop a plan for current situation, toachieved these targets in future in most efficientway, so we can compare the current achievement bythe target one, here we can measure ourperformance & KPI. 26
  27. 27. Classical Vs Planning SystemClassical System Planning SystemComparing with Past Values Comparing with Planned Targets 27
  28. 28. ForecastingAmsterdam - Airport Schiphol 28
  29. 29. Amsterdam- Airport SchipholInput Data: 1992 - 2011 ( October) 29
  30. 30. Amsterdam- Airport SchipholInput Data –Passengers Total –Column 6 in slide no. 27Trend Analysisy = 1E+07Ln(x) + 1E+07R2 = 0.9319Result:Forecast (2011) = 46,801,687 Pax 30
  31. 31. Amsterdam- Airport Schiphol 31
  32. 32. Amsterdam- Airport SchipholResults:R2 = 0.9319Forecast (2011)= 46,801,687 Pax 32
  33. 33. Amsterdam- Airport SchipholInput Data –Seasonal Model2008,2009,2010 33
  34. 34. Amsterdam- Airport Schiphol1- Optimum Solution (Pax2010 > Pax2011 Forecast))i.e 45,136,967 > 41,626,027 is not Practical) 34
  35. 35. Amsterdam- Airport Schiphol2- Practical Case – Seasonal Model 2011(Forecast) = 46,801,687 Pax 35
  36. 36. Amsterdam- Airport Schiphol2- Practical Case – Seasonal Model 2011(Forecast) = 46,801,687 Pax  n =12  ∑  i= 1 Monthi = 46,801,687 Pax   2011 Forecast   46,801,687 36
  37. 37. Amsterdam 37
  38. 38. Amsterdam- Airport SchipholComparison of Results 38
  39. 39. Summary Most of the airlines practice the classical methods, theyevaluate their current performance based on the past results, they justlooking to the back only for one Year ( or same period as month). While this study explore the effect of historical data in termsof trends forecast, in which direction the airline business moves, andthe second part is addressing the short term impacts of seasonality(here months) based on three (3) years monthly data base, keeping inminds the model fairness constrains i.e (R2) and (T.S.) to minimise theforecasting errors, then compare the forecasted/planned figures by theactual one. The new constrain for this model is to match the accumulated forecastedmonths by (Seasonal Model – 3 years data base) with the proposed forecasted year ofTrend analysis (Trend Model – 19 years data base).Results:By Planning method the accuracy is high in terms of Standard Deviation i.e 0.037while the Classical method is 0.092. 39
  40. 40. ContactFurther Information:Mohammed Salem AwadTel: 00967 736255814Email: mohammed.hadi@yemenia.com smartdecision2002@yahoo.com 40

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