Keeping the same rules 2

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Business & Mgmt

Published on March 17, 2014

Author: Forecasting

Source: slideshare.net

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“Plans are nothing; planning is everything” One of the main factors for successes is good planning, especially when we plan for futures as to design objectives and set targets, but the issue when we plan for targets from some raw data, that may have concurrent results or figures as the case of Amsterdam airport. Really to set concurrent figures to get the same target is a hard task, So how to solve this dilemma!!!

Keeping The Same Rule ! by Mohammed Salem Awad Consultant Date of Issue : 05/03/2014

Keeping The Same Rule By Mohammed Salem Awad Consultant One of the main factors for successes is good planning, especially when we plan for futures as to design objectives and set targets, but the issue when we plan for targets from some raw data, that may have concurrent results or figures as the case of AMS airport which is shown in the following table. Really to set a concurrent figures to get the same target is a hard task, then how to solve this dilemma !!! As in the following table: + + +

Objective : So, is it possible to keep the same rule for forecasting figures? i.e to get target that reflects the long term data base of 21 years and set that target to reflect the seasonality models of short term data base – three years period to get the same forecasting design figure provide that all the addressed trend and seasonality models fulfills the required constrains, to be a fair forecasting for the following passengers data base of AMS airport - i.e Europe – Intercontinental and O&D - Transfer and Scheduled – Unscheduled as it reported in their reports. Models - Trend for 21 years Data Base ( 21 Data Set) – optimum case - Seasonality Model for 3 years Data Base ( 36 Data Set) – optimum case - Two seasonlity models for 3 years Data Base ( 72 Data Set ) – optimum case Constrains There many constrains that should be fulfill the analysis 1- R2 is greater than 80% 2- Signal Tracking is in the range of - 4 and + 4 3- The forecast trend of 2014, for 21 years data base is the landmark (targeting forecasting figure) 4- All the seasonality forecasting results, should fulfill the above statement. 5- So for forecast traffic passengers of 2014 – should be equal Trend Forecast x Europe Intercontinental x O&D Transfer x Scheduled Unscheduled x x 21 43

Trend Forecast: Based on 21 data set (21 years data base from 1992- 2012) By implement trend approach using the best of line fit ( Power Function ) the results of fair fitting are R2 = 96.5 while Signal Tracking = ± 5.71 The Forecasting of 2014 = 54,203,771 Passengers Max/Min Signal Tracking Analysis: The aim of this analysis is to keep most of the signal tracking values in constrain band ( -4 and + 4 ) maintaining high value of R2 . The graph shows the residual values by yellow color are out of the band for 21 set data base, which reached the highest extreme value by ± 5.71. Mathematical Model: The mathematical model is power function with the following equation Actual Data for 2013 ( 1-10 ) are not included as the data of 2013 are not available ( Nov. and Dec). Amsterdam Airport Schiphol 1

Seasonality Model ( Short Term ) : Europe + Intercontinental = Generally speaking the normal method to evaluate short range data with seasonality impacts is AREMA Model, but in this analysis we will try use the best of art technique that reflect two parameters only, they are displacement and Rotational, our approach is to find the line of fit that passing through the year of accumulated forecasted figures of 12 months for 2014, and that reflects a minimum errors and high relation factor ( R2 ) for both series ( Europe & Intercontinental ) which satisfies the following relation Europe + Intercontinental = = 54,203,771 Passengers 2 x x Intercontinental Europe2

Origin and Destination Transfer Scheduled Unscheduled 3 4

Keeping The Same Rule Final Results: Forecasting Accuracy Matrix: Forecasting Accuracy Matrix can be represented by four regions i.e Fair , Mislead, Poor, and Unrelated, for our cases : only one case ( Transfer ) is FAIR as it is satisfied the pre- request constrains while most of the other segments are Mislead which actually fairs results that deny the mislead issue for the following reasons : 1 2 3 4

1- The Signal Tracking values are defined on both sides of the trend line so the issue of displacement is not exist. 2- By visual inspection, the forecasted model is lay on the actual data. Conclusions: The study shows, that there is possibility to design our targets even though to have same target, off course it hard task but it needs patience and time to deliver a fine results. The rule of the signal tracking is to refine the final results and positioning the trend line in the final direction of analysis. Two methods can be used to get the forecasted figure of 2014 = = 54,203,771 Passengers either in one step ( analysis ) based on 72 data set – optimum case which is applied. Or in two steps ( two analysis ) one optimum and the other one is adjusted based on 36 data set each. All data segment are reported, and any researcher can compare the forecasted figure by the actual data to evaluate the forecasting approach. The study shows high accuracy.  Contact: Mohammed Salem Awad Consultant Tel: 00 967 736255814 Email: smartdecision2002@yahoo.com www.slideshare.net/wings_of_wisdom

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