Mohammed Salem Awadh provides forecasts for passenger traffic at Changi International Airport for 2018-2020. For annual forecasting, two trend models are used to minimize data discrepancies and set accurate targets. For monthly forecasting, seasonality patterns are determined without constraints. The document discusses two scenarios for each year, recommending the scenario with the lower predicted growth to avoid overestimating passenger levels. Overall, the forecasts provide a way to set targets and measure performance based on past traffic data and trends at Changi Airport.

1. Changi International Airport
Traffic Passengers Forecast (2018-2020)
Creativity Cannot be born without Suffering
And
Success Cannot be achieved without Pains
Dears,
Forecasting is very important in our life, without the right expectation, plans cannot be setup,
in many fields especially finance. (Preparing budgets), and performance can be developed if
there is no targets, and accordingly we cannot know the right levels of KPIs, to measure the
progress for what we are achieved .
Today I am looking back for my previous work to see what progress or change that we made,
off course numbers don’t lie, and that is the power of the knowledge, you can compare the
forecasted results with the actual figure, to see the level of expectation – by defining the
value of coefficient of determination ( R – Square).
Good Case Study is Changi International Airport for 2018 – 2020 (period)
Hope to enjoy
Mohammed Salem Awadh
Working In A Shadow
10 July 2020
By: Mohammed Salem Awad
Aviation Consultant

2. Setting Target
For Short Term Forecasting
Introduction
In short term forecasting, two objectives we address,
First: Annual Forecasting – which defining the trend
(Positive or Negative).
Second: Monthly Forecasting – which defining the
seasonality (Optimum Solution without constrains)
First : Annual Forecasting – which defining the trend (Positive or Negative).
The best way to set up annual target and minimize the data discrepancy is to address the data by two
trend models using the concept of 12 rolling months.
First – General Trend Model using the concept of Straight Line equation – defining general trend.
Second – Most Recent Data Trend Model Using a Polynomial Model – Second-degree equation.
This reflects the impact of most recent data on the path of general trend. The mid-point is the most
convenient forecast annual result at Dec 2019. So as long as the gap between two models is small, the
more accurate approaching value for setting annual target otherwise we have to select the half way
distance between two extreme targets of these two models provided that Dec 2019 > Dec 2018.
However,the outcomes can be represents by three directions
1- Positive Trends – both models in Upwards direction. The
selection will be either the mid-point of positive trends Dec
2019 (Red Column) or the value of optimum solution (blue
column) – select the one who has lower level, to avoid the
high-risk passenger growth that will affect the final result.
2- Negative Trends – both models in downwards direction –
Never set target with negative trend,here the best scenario
is to look to Dec 2018 (Red Column) and the annual actual
value for 2018 - select the one that has higher level.
3- High Discrepancy Data – and it is occur when we have
one positive, and the other negative trend. i.e mean large
gap between the two models, we follow step two provided
that the mid-point Dec 2019 or optimum solution scenario
are not higher than step two. In case they are higher, select
the one that haslowerlevel. As in step one.
Second: Monthly Forecasting – which defining the seasonality
(Optimum Solution without constrains)
This the main core program to define the seasonality pattern
without any preset constrains, it follows the trends based on
36 months database. Which always comes closely to the
preset values. Moreover, the blue column in the annual graph
represents it. Therefore, we get a complete picture to select
the right target. However, when it’s a higher value, then we
have to select the preset values as a target.
By: Mohammed Salem Awad
Aviation Consultant
Data Source:
https://ec.europa.eu/eurostat/data/database

3. Airport Forecasting - 2020
(Issue No. 37)
Changi
Airport Singapore (SIN)
Annual Passengers Forecast:
The best way to set up annual target and minimize the data discrepancy is to address the data by two
trend models using the concept of 12 rolling months.
First – General Trend Model using the concept of Straight Line equation – defining general trend.
Second – Most Recent Data Trend Model Using a Polynomial Model – Second-degree equation.
This reflects the impact of most recent data on the path of general trend. The mid-point is the most
convenient forecast annual result at Dec 2020. So as long as the gap between two models is small, the
more accurate approaching value for setting annual target otherwise we have to select the half way
distance between two extreme targets of these two models provided that Dec 2020 > Dec 2019.
Scenario 1: Preset Annual Target = Passengers = 70,799,274 Pax.
At 4.20 % Annual Growth (recommend)
Scenario 2: Optimum Solution. = Passengers = 71,342,742 Pax.
The objective is to minimize the risk of not achieving the desired goals. Both scenarios are fairs, and
the results are good. The first scenario is fair enough to select,as it has lower growth, we recommend
the 1st
scenario, to avoid the high-risk passenger growth that mislead the final results.
By: Mohammed Salem Awad
Aviation Consultant
Data Source: https://www.changiairport.com/corporate/our-
expertise/air-hub/traffic-statistics.html

4. Airport Forecasting - 2019
Issue No. ( 10 / 2019)
Airport Traffic Passengers Forecast - 2019 (SIN)
The Dilemma ofperfect solutions
As a general role, the model is fair when R-square is
greater than 80% and S.T is less than ± 4, for our case
these factors are very closely. Usually, we have two
approaches:
1- Scenario 1 : Preset AnnualTarget =
ForecastPax = 69,586,660
2- Scenario 2 : Optimum Solution.
Forecast Pax = 69,411,794 (recommended)
The objective is to minimize the risk of not achieving the desired goals. (Select, one who has lower
growth). Both Scenarios are excellent and seasonality graphs are almost identical but the first one
raise the target figure by 174,866 Pax. Therefore, we recommend the second one, to avoid the high-
risk passenger growth.
By: Mohammed Salem Awad
Aviation Consultant
Data Source: https://www.changiairport.com/corporate/about-
us/traffic-statistics.html

5. Airport Forecasting - 2018
(Issue No. 106)
Changi International Airport (SIN)
Annually Forecast: The best way to set up annual target and minimize the data discrepancy is to
address the data by two trend models using the concept of 12 rolling months. Here we implement two
trend models by using Add a trend line in XLS sheet:
First – General Trend Model using the concept of Straight Line equation – defining general trend.
Second – Most Recent Data Trend Model Using a Polynomial Model – Second-degree equation.
This reflects the impact of most recent data on the path of general trend. The mid-point is the most
convenient forecast annual result at Dec 2018. So as long as the gap between two models is small, the
more accurate approaching value for setting annual target otherwise we have to select the half way
distance between two extreme targets of these two models provided that Dec 2018 > Dec 2017.
Monthly Forecast – we define the monthly targets that fulfill the condition of the first point (annual
traffic setting forecast 2018 = 65,529,874 Pax) with minimum errors. Which shows a fair result in
the second graph, at R-squared = 94.94 %, AnnualGrowth = 5.72 % and a clear picture about the
seasonality pattern of the airport is defined as shown in table and graph.
By: Mohammed Salem Awad
Aviation Consultant
Data Source:www.anna.aero