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Setting Goals
            And Targets

Case Study:
Amsterdam- Airport
Schiphol
By: Mohammed Salem Awad
Consultant
Yemen


                          0
Outline
- Introduction
- Forecasting –Trend vs Seasonal
- Model Fairness.
- Case Study
   -   Amsterdam- Airport Schiphol - Input Data
   -   Trend Forecast - period 1992-2010
   -   Seasonality Model period 2008-2010 – Optimum Solution
   -   Seasonality Model period 2008-2010 – Practical Solution

- Summary
                                                                 1
Introduction
Targets:

Most of the companies working
on achieving goals, targets, and
evaluate their achievements by
comparing the current achieved
results to results of previous
week, month, or year i.e looking
backward to analysis current
situation.

                                   2
Introduction

But for setting targets we have
to look forward, forecast,
develop a plan for current
situation, to achieved these
targets in future in most efficient
way, so we can compare the
current achievement by the
target one, here we can measure
our performance & KPI.

                                      3
Introduction
Classical System             Planning System
Comparing with Past Values   Comparing with Planned Targets




                                                              4
Forecasting –Trend vs Seasonal
Trend Forecasting
Tell us in which direction (Growth) of
the historical data, and usually is a
long term forecast.
Seasonal Forecasting
Tell us the Seasonal, Cyclic shocks,
we used it to define the forecasting
Pattern
Trend vs Seasonal Forecasting
Forecasted Year of TREND
= Sum of 12 forecasted Seasonal
Months for same year,
                                         5
Model Fairness
 Two Main factors:




              Evaluation              Forecasting


  R2 = Coef. Of Determination   T. S. = Tracking Signal

                                                          6
Model Fairness
 Two Main factors:

                       R2 > 80%
                           AND
                     -4 < T.S.< 4



  R2 = Coef. Of Determination    T. S. = Tracking Signal

                                                           7
Case Study
Amsterdam - Airport Schiphol




                               8
Amsterdam- Airport Schiphol
Input Data: 1992 - 2011 ( October)




                                     9
Amsterdam- Airport Schiphol

Input Data –
Passengers Total –
Column 6 in slide no. 9

Trend Analysis

y = 1E+07Ln(x) + 1E+07
R2 = 0.9319
Result:
Forecast (2011) = 46,801,687 Pax


                                   10
Amsterdam- Airport Schiphol

Input Data – Trend Analysis




                              11
Amsterdam- Airport Schiphol
Results:


R2 = 0.9319

Forecast (2011)


= 46,801,687 Pax
                              12
Amsterdam- Airport Schiphol
Input Data –
Seasonal Model
2008,
2009,
2010




                              13
Amsterdam- Airport Schiphol
1- Optimum. Solution Seasonal Model (Pax2010 > Pax2011 Forecast)
i.e 45,136,967 > 41,626,027 is not Practical)




                                                               14
Amsterdam- Airport Schiphol
2- Practical Case – Seasonal Model
   2011(Forecast) = 46,801,687 Pax




                                     15
Amsterdam- Airport Schiphol
2- Practical Case – Seasonal Model
   2011(Forecast) = 46,801,687 Pax




       n12                         
      
       i 1
             Monthi  46,801,687 Pax 
                                      2011 Forecast
                                    


         46,801,687
                                                       16
Amsterdam- Airport Schiphol




                              17
Amsterdam- Airport Schiphol
        Comparison of Results




                                18
Summary
         Most of the companies practice the classical methods, they
evaluate their current performance based on the past results, they
just only looking to the back only for one Year ( or same period as
month).
          While this study explore the effect of historical data in
terms of trends forecast, in which direction the company business
moves, and the second part is addressing the short term impacts of
seasonality (here months) based on three (3) years monthly data
base, keeping in mind the model fairness constrains i.e (R 2) and
(T.S.) to minimise the forecasting errors, then compare the
forecasted/planned figures by the actual one.
        The new constrain for this model is to match the accumulated forecasted
months by (Seasonal Model – 3 year data base) with the proposed forecasted year of
Trend analysis (Trend Model – 19 years data base).
Results:
By Planning method the accuracy is high in terms of Standard Deviation i.e 0.037
and Classical method is 0.092.

                                                                                   19
Forecasting




              20
Forecasting




              21
Contact




Further Information:
Mohammed Salem Awad                  www.freewebs.com/wingsofwisdom/

Tel: 00967 736255814                 www.freewebs.com/art-of-knowledge/

Email: smartdecision2002@yahoo.com   www.standout-from-the-crowds.webs.com


                                                                          22

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Setting Targets

  • 1. Setting Goals And Targets Case Study: Amsterdam- Airport Schiphol By: Mohammed Salem Awad Consultant Yemen 0
  • 2. Outline - Introduction - Forecasting –Trend vs Seasonal - Model Fairness. - Case Study - Amsterdam- Airport Schiphol - Input Data - Trend Forecast - period 1992-2010 - Seasonality Model period 2008-2010 – Optimum Solution - Seasonality Model period 2008-2010 – Practical Solution - Summary 1
  • 3. Introduction Targets: Most of the companies working on achieving goals, targets, and evaluate their achievements by comparing the current achieved results to results of previous week, month, or year i.e looking backward to analysis current situation. 2
  • 4. Introduction But for setting targets we have to look forward, forecast, develop a plan for current situation, to achieved these targets in future in most efficient way, so we can compare the current achievement by the target one, here we can measure our performance & KPI. 3
  • 5. Introduction Classical System Planning System Comparing with Past Values Comparing with Planned Targets 4
  • 6. Forecasting –Trend vs Seasonal Trend Forecasting Tell us in which direction (Growth) of the historical data, and usually is a long term forecast. Seasonal Forecasting Tell us the Seasonal, Cyclic shocks, we used it to define the forecasting Pattern Trend vs Seasonal Forecasting Forecasted Year of TREND = Sum of 12 forecasted Seasonal Months for same year, 5
  • 7. Model Fairness  Two Main factors: Evaluation Forecasting R2 = Coef. Of Determination T. S. = Tracking Signal 6
  • 8. Model Fairness  Two Main factors: R2 > 80% AND -4 < T.S.< 4 R2 = Coef. Of Determination T. S. = Tracking Signal 7
  • 9. Case Study Amsterdam - Airport Schiphol 8
  • 10. Amsterdam- Airport Schiphol Input Data: 1992 - 2011 ( October) 9
  • 11. Amsterdam- Airport Schiphol Input Data – Passengers Total – Column 6 in slide no. 9 Trend Analysis y = 1E+07Ln(x) + 1E+07 R2 = 0.9319 Result: Forecast (2011) = 46,801,687 Pax 10
  • 12. Amsterdam- Airport Schiphol Input Data – Trend Analysis 11
  • 13. Amsterdam- Airport Schiphol Results: R2 = 0.9319 Forecast (2011) = 46,801,687 Pax 12
  • 14. Amsterdam- Airport Schiphol Input Data – Seasonal Model 2008, 2009, 2010 13
  • 15. Amsterdam- Airport Schiphol 1- Optimum. Solution Seasonal Model (Pax2010 > Pax2011 Forecast) i.e 45,136,967 > 41,626,027 is not Practical) 14
  • 16. Amsterdam- Airport Schiphol 2- Practical Case – Seasonal Model 2011(Forecast) = 46,801,687 Pax 15
  • 17. Amsterdam- Airport Schiphol 2- Practical Case – Seasonal Model 2011(Forecast) = 46,801,687 Pax  n12    i 1 Monthi  46,801,687 Pax   2011 Forecast   46,801,687 16
  • 19. Amsterdam- Airport Schiphol Comparison of Results 18
  • 20. Summary  Most of the companies practice the classical methods, they evaluate their current performance based on the past results, they just only looking to the back only for one Year ( or same period as month).  While this study explore the effect of historical data in terms of trends forecast, in which direction the company business moves, and the second part is addressing the short term impacts of seasonality (here months) based on three (3) years monthly data base, keeping in mind the model fairness constrains i.e (R 2) and (T.S.) to minimise the forecasting errors, then compare the forecasted/planned figures by the actual one.  The new constrain for this model is to match the accumulated forecasted months by (Seasonal Model – 3 year data base) with the proposed forecasted year of Trend analysis (Trend Model – 19 years data base). Results: By Planning method the accuracy is high in terms of Standard Deviation i.e 0.037 and Classical method is 0.092. 19
  • 23. Contact Further Information: Mohammed Salem Awad www.freewebs.com/wingsofwisdom/ Tel: 00967 736255814 www.freewebs.com/art-of-knowledge/ Email: smartdecision2002@yahoo.com www.standout-from-the-crowds.webs.com 22