This analysis, decribe a new method to set targets, by using two forecasting models, the first model is trend, while the second is seasanlity model

1. Setting Goals
And Targets
Case Study:
Amsterdam- Airport
Schiphol
By: Mohammed Salem Awad
Consultant
Yemen
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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
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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.
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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.
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5. Introduction
Classical System Planning System
Comparing with Past Values Comparing with Planned Targets
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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,
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7. Model Fairness
 Two Main factors:
Evaluation Forecasting
R2 = Coef. Of Determination T. S. = Tracking Signal
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8. Model Fairness
 Two Main factors:
R2 > 80%
AND
-4 < T.S.< 4
R2 = Coef. Of Determination T. S. = Tracking Signal
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9. Case Study
Amsterdam - Airport Schiphol
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10. Amsterdam- Airport Schiphol
Input Data: 1992 - 2011 ( October)
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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
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12. Amsterdam- Airport Schiphol
Input Data – Trend Analysis
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13. Amsterdam- Airport Schiphol
Results:
R2 = 0.9319
Forecast (2011)
= 46,801,687 Pax
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14. Amsterdam- Airport Schiphol
Input Data –
Seasonal Model
2008,
2009,
2010
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15. Amsterdam- Airport Schiphol
1- Optimum. Solution Seasonal Model (Pax2010 > Pax2011 Forecast)
i.e 45,136,967 > 41,626,027 is not Practical)
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16. Amsterdam- Airport Schiphol
2- Practical Case – Seasonal Model
2011(Forecast) = 46,801,687 Pax
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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
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18. Amsterdam- Airport Schiphol
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19. Amsterdam- Airport Schiphol
Comparison of Results
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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.
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21. Forecasting
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22. Forecasting
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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
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