Leading Arab Womenin Civil Aviation

A file highlights Arab women activities in the field of civil aviation

Civil Aviation & Meteorology Authority (Yemen) January - March 2013, issue 18

DUBAI AIRPORT SHOW 2013

A320 FAMILY VS B737NG

A JURNEY TO WADI DO’AN

www.camamagazine.com

Fair – Poor Forecasting MatrixAccuracy of Forecasting Model

“Nothing limits achievement like small thinking; nothing expands possibilities like unleashed imagination.” William Arthur Ward

24 study

U S Carriers Case StudyOne of the major challenges in the forecasting, is ACCURACY, how far we can except the results, is it reliable and practical or it might mislead us in undesirable direction, how we can set a reasonable targets, that can be achieved , is it good to forecast with a negative trends or not, and when we can to do that and how to adjust it. How we can interpret the trend analysis with seasonality model. All these issues have their own impact on the accuracy formula. So what is the best method to define and measure the accuracy of forecasting model. In this article we will address one of the new creative methodology, we will called it Fair – Poor Forecasting Matrix. It basically developed based on two

main estimated mathematical parameters, Displacement and Directional factors which has a consequence impacts on R and Signal Tracking.

Setting Boundary Accuracy:For Fair forecasting, the model should fulfill these criteriaR2 ≥ 80 and Signal Tracking should be - 4 ≤ S.T. ≤ + 4 Then to developed Fair – Poor Forecasting Matrix the following outcomes will be concluded;

1- Fair Forecast – when R2 and Signal Tracking are in bond.2- Mislead – Displacement Issue. This case when R2 is in bond and Signal Tracking is out bond. we can adjusted signal

tracking to be in bond when there is a room for R2 in the same analysis so that it can be consider as a fair forecast. 3- Unrelated – Directional Issue. This case when R2 is out of the bond and Signal Tracking in the bond. i.e. the balance of accumulated error without any correlation4- Poor Forecast – when both R2 and Signal Tracking are out of the bond (Total Mess).

This matrix manipulate the four decision regions to develop the right and best picture of the accuracy of forecasting. And to enhance the process of decision making for airline data analysis especially traffic forecasting, that maps the overall forecasting accuracy of US Carriers.

Fair

Unrelated

Mislead

Poor

CAMA Magazine | issue 18 | March, 2013

Mohammed S. AwadResearch Scholar Aviation Management

25study

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

ForecastingEvaluation

data available

model estimation period ex post forecasts

Y1 Yn YN

ex post forecasts

Case Study: Traffic Forecasting of Major US carriers

Data Collections: Based on the data published in RITA website, concerning traffic passengers of US major carriers, for the period of three years data base on a monthly bases, started from Aug- 2009 to July 2012 except Jetblue Airways which input reported from Jan – 2009 to Dec - 2011. While unavailable data for Atlas Air.. Forecasting Model: The basic data span is 36 months (Input) with 12 months forecasting, the fair bond restricted by the preset design values of R2 and Signal Tracking. 12 US carriers are addressed. The forecasting process has two stages, Evaluation, and Forecasting. In the evaluation stage we try to analysis the input data, and align the practical data with a mathematical model, we use state of art forecasting program to fit data. Two control factors have a great impact on the model, First displacement factor (Displacement Issue), this factor acts to shift the whole data from it running bath to a new one but keeping the trend and direction of the analysis. While the second factor is Directional factor, definitely if we manipulate this factor and try to use many trail values (positive and negative value), the model will position itself accordingly as a clock about the origin.As in the below airlines forecasting graphs.

Forecasting of US Carries – Graphs:8

CAMA Magazine | issue 18 | March, 2013

Analysis:About (12) US carries are analysis-ed, based on a RITA input data, the analysis are varied from Fair Category to Poor one.

The blue line represent the actual Input Data, for 36 input points (Months) while the red line represents the tracked and forecast points, tracked are 36 points for the purpose of monitoring and evaluation, while the forecast points for purpose of planning and setting goals and targets of these airlines for 2013 and 2014 respectively.

All airlines shows a seasonality patterns except united airline, it suddenly shocked to double level at the beginning of 2012, and may explain by a merger with other airline, and that why there will be a negative trends if we use the data of 2011 for united airline alone.

But for other US carriers the picture is completely different, Now it is easy to set goals and targets for each month (2013 and 2014) and consequently developed a KPI system to enhance the airline performance program.

Really it is a normal arguments

US carriers are out of the bond i.e. Delta Airlines, US Air and FRONTIER Airlines, (POOR region) while the remaining US carriers are positioning them self in a out-bond signal tracking values in spite of the higher values of R2. This lead us to (MISLEAD region). To solve this issue we have to repeat the analysis and adjust the signal Tracking when there is a room for change R2 in the bond at acceptable level.

2- Final (Adjusted):Since most of US carriers are in MISLEAD region, as Signals Tracking (S. T.) are out-bond, we have to adjust S. T. and repeat the analysis to direct these US carriers to FAIR region. Some of US carriers we cannot do anything for them, as R2 and S. T. are out of the limits. While other get improved (as FRONTIER Airlines) which moved from POOR to FAIR region. In terms of Forecasting of 2013, There is a significant improve in figures when we adjusted S. T. unless it will MISLEAD us if we rely on a First Trail.

for airline analyst to say “yes it is a summer season we have to adjust our fare and increase the frequency to serve this demand or to change the aircraft type to cover these demand”, but to what level.

These graphs shows clearly how airline to react, when the should response and what level they have to do that, off course the error will be there, but it will an acceptable error.

Its also define its trends either it is positive or negative one, and how to align a minor negative trend to be a positive one in an acceptable preset boundary.

Results: The result can be explore in two steps;

1- First Trail: Based on first analysis, three

First Trail Final (Adjusted)

Summary:This paper address the relation between R2 and S. T. , we target US carries traffic data from RITA for 3 year data base. Really we will Mislead if we rely on the classical monitoring approach to assign R2 as the only factor for Goodness of Fit to the data, really it is clear with the value of R2 in the analysis and undesirables level of S. T. but if we reflect S. T. to be adjusted in the required bond. Then the picture will be different as shown in the last columns ( Final Adjusted), and that will have a significant impact on 2013 forecasting figures (please compare the results).

Finally Fair – Poor Forecasting Matrix it is a unique method that reflects the impacts of R2 and Signal Tracking by manipulating the mathematical factor (Displacement and Directional) to align for best scenario of actual data (US carries data).■

26 study

CAMA Magazine | issue 18 | March, 2013

Relative Signal Tracking Rel

ativ

e R

2

Fair

Unrelated

Mislead

Poor

Introduction: Generally most of airlines companies in third world countries found too hard to evaluate commercial aircraft’s, due to the complexity of aviation industry and its optimizations programs especially linear program which implemented in the network of the airline also the high techniques that possess by aircraft manufacture and modern sales approach that deliver by their sale forces and their pretend of manufacturing the best commercial aircraft’s – products in the world either Airbus Industries or Boeing Group, as both aircraft’s of these parties have an excellent characteristics that cannot be differentiated and consequently most of small airlines refer to third party to evaluate their fleets, which normally a consultant company specialize in aviation as Sabre Solutions that implemented statistical tools and analysis in their approaches of evaluation as Value Analysis, Cost per Trip vs Cost per Seat Matrix, and Profit vs Market Opportunity Matrix. While the issue of selecting the right aircraft is a main effective parameter for successful airline, as each market has its own

characteristics that reflects/ impact by positive or negative signals on airline network study for the aircraft specification that have different business model as corporate airline, private and taxi air, legacy airline, low cost airlines, and other Mega air carriers and accordingly the aircraft’s are diversified and differentiated to be short range and long range, some has small

capacity while the others have larger one. Finally the new technology play a major role to reduce the manufacturing cost for aircraft which reflected by studying the technical specifications of the aircraft and their relations with their price and values in market which is addressing by a so-called Value analysis for A320 and B737-NG families.

Aircraft Performance FactorsEach aircraft characterize by its own performance factors especially the technical one that we can develop the evaluation studies as:

1- Aircraft Configuration2- Aircraft Speed 3- Mean Takeoff Weight4- Maximum Payload Range(Kilometers).5- Maximum Payload Range(Kilograms).

As it shown in table one, the relation between the aircraft price and other technical characteristics (Relative Values – multiplication of these characteristics after filtration) are addressed in this study.

Head To Head Analysis

A320 Family VS B737NG (Value Analysis)

Mohammed S. AwadResearch Scholar Aviation Management

Rela

tive V

alue

Aircraft Price

Attractive

Disappointment

Figure No. (1)

28 study

CAMA Magazine | issue 18 | March, 2013

Summary:Value Analysis define clearly the effective and favorite technical factors of aircraft’s and its relation to its prices by splitting the area of the graph for two main regions according to the aircraft sampling used and its related category so the first region – Attractive – which characterize by a lower price and high effective factors while the second region – Disappointment – which characters by a high price and less effective factors. That obviously clear by the comparison between A320 and B737-800. Also the analysis can be improve by giving more certain different weights as aircraft capacity, speed of aircraft, and MTOW. All these weight factors will impacts the final results to enhance the decision to purchase the right aircraft.■

115

113

114

112

111

110

10917.1

R2 = 0.9546

A 320 - 200

A 320

B 737 - 800

B 737 - 700B 737 - 400

B 737 - 600

B 737 - 300

B 737 - 500

17.2 17.3 17.4 17.5 17.6 17.7

Better Value

LesserValue

Value Analysis for B737 N G and A320

LN (Aircraft Price)

LN (R

elativ

e Valu

e)

130

125

120

115

110

10515.5 16 16.5 17 17.5 18 18.5 19 19.5

Value Analysis for Boeing & Airbus Fleet

LN (Aircraft Price)

LN (R

elativ

e Valu

e)

B 737 - 200 B 737 - 500

Coefficient Of

Correlation

Accepted

Rejected No

Yes

R > 80%

AircraftFactor

AircraftPrice

Value Analysis:One of the most effective methods to select and evaluate the aircraft’s is Value Analysis, where it power is to link the technical characteristics of the aircraft with it price and consequently develop Basic Line Reference (line fit – using regression analysis) that split the area into two parts i.e (Attractive Region and Disappointment Region)

=As indicated in figure (1).As the Aircraft Characteristics by speed,

capacity, MTOW, Capability range in terms or distance and weight should have a direct relations with its price. But not with fuel consumption as it will reflects a negative impact on the analysis, as the differentiation should be with a less fuel consumption and not with the high one. And that relation usually address by Coefficient of Correlation for Aircraft Characteristics and its price according to the following sequence:

Coefficient Of Correlation:The importance of this coefficient is in defining the effective factors that have impact on the value analysis. Simply it is a mathematical formula that explicit the relation between two

set of data in terms of percentage (%) as this will define how far are related. While 80 % and above the ideal level to accept that data to included in the analysis. In the context of the statistical analysis normally we use R - coefficient of determination (squaring of R) to raise the fairness of the analysis and avoid negative values.

The Analysis: By implementing all the previous steps and study most of the characteristics of World Fleet of Boeing Groups and Airbus Industries as it it shown in figure (2), the figures shows Three Groups but unfortunately the coefficient of determination is to poor to accept as it is i.e 12.5%, since we includes all the world fleet and not targeted a certain family, and if we refer again to the the three groups, we will note that the last group contains A320 and B737 families.

So by re-analysis the last group data ( targeting the last group ), we will find a relation between Aircraft relative factors and Aircraft price that may reach to 95% and accordingly we can apply minimum least square analysis to find the best line fit to create a Reference Line as it is indicate by figure no. ( 3 ), Witch split the area into two parts of these aircraft’s Airbus and Boeing.

Consequently we can define which the best aircraft and according to the figure no. (3) we will find that A320 own more favorite relative value than B737-800 and with less purchasing value than B737-800 while if we compare A320 with B737-700 we will find that the price of B737-700 is less than A320 but don’t reach to technical relative values of A320, so by referring to the prices of both aircraft’s, we will find the B737-700 does not reflect a good position of A320 due the difference in prices which may support to re-position the aircraft for a good level.

Flow Chart (1)

29study

CAMA Magazine | issue 18 | March, 2013

Figure No. (2)

Figure No. (3)

32

Prepared by: Mohammed Salem AwadResearch Scholar – Aviation Management Forecasting by Objective

Case Study TALLINN Airport

Airports Forecasting

To carry a traffic forecast for an airport is uneasy task, As most of the statistician rely on coefficient of determination R2 to ensure the fairness of the analysis. In this article we will try to create many scenarios that will reflects, what the top management thinks, are they interesting to rely R2 (Classical approach), are they trying to minimize the errors by setting Signal Tracking to Zero, or trying to merge long range trend forecast to be targeted (accumulated) for the seasonality model, or to ask to follow the most update and recent input data (recent years). Really all the four scenarios are addressed, in case study of TALLINN Airport.

Case Study – TALLINN Airport: Tallinn Airport (Estonian: Lennart Meri Tallinna lennujaam) (IATA: TLL, ICAO: EETN) or Lennart Meri it formerly Ülemiste Airport, is the largest airport in Estonia and home base of the national airline Estonian Air. Tallinn Airport is open to both domestic and international flights. It is located approximately 4 km from the centre of Tallinn on the eastern shore of Lake Ülemiste. As Tallinn is located nearest to Asia Pacific of all EU capitals, this gives Tallinn Airport a major geographical advantage for establishing long-haul flights between these two regions.Two set of data are examined to develop a Trend and Seasonality Models.

A- Trend Model (15 years data set)Based on these data the a trend model is developed and with R2 = 86.1 and Signal Tracking = -1.84, the 2013 Forecasted is = 2,495,900.

B- Seasonality Model (36 months data set)Four Scenarios are developed as it is shown in the following table.

1- Maximize R2

2- Setting Signal Tracking = Zero 3- Setting Trend Target to 2,495,9004- Reflects the latest Input Data

Results: All scenarios shows high values of R2 (all closes value) while Signal Tracking shows a large divergence with respect to the bond values and the best selection decision is scenario no. 3 (why) as it is almost cover three pre-constrained parameters in spite of slight divergence of S. T. of the bond and also it reflects the lowest one in the results.Forecasted by Trend Target for 2013 = 2,495,900 PaxR2 = 92.22%Signal Tracking: 6.662

“The only true wisdom is in knowing you know nothing.” Socrates

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

-

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

y = 443370e0.108x

R2 = 0.861

2,49

5,90

0

TRENDANALYSIS

No. Scenarios Objective ?

Coefficient of Determination (R2)

Signal Tracking (S. T.)

Forecasting of 2013

Remarks

1 MaximizeR2

93.46% -33.31 2,907,888 -

2 Setting S. T. = zero

92.67% 0.0000004 2,568,499 -

3 15 Years Tren Target = 2,495,900

92,22% 6.662 2,495,900 Fair

4 Reflecting the Latest Input Data

93.34% -26.26 2,794,463 -

TALLINN AIRPORT (Seasonlity Model)Passengers Forecasting 2013

-

50,000

100,000

150,000

200,000

250,000

300,000

350,000

No

of P

asse

nger

s

TIME (Month)

ForecastActual

Forecasting by Trend Target R2 = 92.22 %S.T.= 6.6620

Pax900,495,2(F) = 2013

CAMA Magazine | issue 18 | March, 2013

33Airport forecAsting

Washington Dulles International Airport (IATA: IAD, ICAO: KIAD, FAA LID: IAD) is a public airport in Dulles, Virginia, 26 miles (41.6 km) west of downtown Washington, D.C. The airport serves the Baltimore-Washington-Northern Virginia metropolitan area centered on the District of Columbia. It is named after John Foster Dulles, Secretary of State under Dwight D. Eisenhower. The Dulles main terminal is a well-known landmark designed by Eero Saarinen. Operated by the Metropolitan Washington Airports Authority, Dulles Airport occupies 11,830 acres (47.9 km2) straddling the border of Fairfax County and Loudoun County, Virginia.

R2 = 96.1S.T.= 0.02013 (F) = 11,641,322 Pax

Detroit Metropolitan Wayne County Airport (IATA: DTW, ICAO: KDTW), usually called Detroit Metro Airport, Metro Airport locally, or simply DTW, is a major international airport in the United States covering 7,072-acre (11.050 sq mi; 2,862 ha) in Romulus, Michigan, a suburb of Detroit. It is Michigan’s busiest airport and one of the world’s largest air transportation hubs.The airport serves as Delta’s second busiest hub. Delta, along with SkyTeam partner Air France, occupy the McNamara Terminal.

R2 = 92.3 S.T.= -4 2013 (F) = 16,770,937 Pax

Forecasting of US Airports:Airport forecasting is an important issue in Aviation industry. It becomes an integral parts of transportation planning. It sets targets and goals for the airports, either for long term or medium term planning. The primary statistical methods used in airport aviation activity forecasting are market share approach, econometric modeling, and time series modeling.While we will use R and Signal Tracking Approach.

Detroit Airport (Seasonlity Model)Passengers Forecasting 2013

1,000,000

1,100,000

1,200,000

1,300,000

1,400,000

1,500,000

1,600,000

1,700,000

No of

Pas

seng

ers

TIME (Month)

Forecast

Actual

R2 = 92.3 %S.T.= - 42013(F) = 16,770,937 Pax

Washington Dulles Airport (Seasonlity Model)Passengers Forecasting 2013

500,000

600,000

700,000

800,000

900,000

1,000,000

1,100,000

1,200,000

No of

Pas

seng

ers

TIME (Month)

Forecast

Actual

R2 = 96.1%S.T.= 0.02013(F) = 11,641,322 Pax

CAMA Magazine | issue 18 | March, 2013

34

International Airport

(IATA: FLL, ICAO: KFLL, FAA LID: FLL) is an international commercial airport located in unincorporated Broward County, Florida, three miles (5 km) southwest of the central business district of Fort Lauderdale. It is also located near the city of Hollywood and is 21 miles (33.7 km) north of Miami.

R2= 96.8S.T.= 0.0 2013(F) = 12,080,874 Pax

Charlotte Douglas International Airport

(IATA: CLT, ICAO: KCLT, FAA LID: CLT) is a joint civil-military public international airport located in Charlotte, North Carolina. Established in 1935 as Charlotte Municipal Airport, in 1954 the airport was renamed Douglas Municipal Airport after former Charlotte mayor Ben Elbert Douglas, Sr. The airport gained its current name in 1982 and is currently US Airways’ largest hub, with service to 175 domestic and international destinations as of 2008. In 2009, it was the 9th busiest airport in the United States and in 2010, the 24th busiest airport in the world by passenger traffic.

R2= 85.6 S.T.= 02013(F) = 21,135,269 Pax

Los Angeles International Airport

(IATA: LAX, ICAO: KLAX, FAA LID: LAX) is the primary airport serving the Greater Los Angeles Area, the second-most populated metropolitan area in the United States. LAX is located in southwestern Los Angeles along the Pacific coast in the neighborhood of Westchester, 16 miles (26 km) from the downtown core and is the primary airport of Los Angeles World Airports (LAWA), an agency of the Los Angeles city government formerly known as the Department of Airports.

R2= 97.7 S.T.= 42013(F) = 32,269,576 Pax

Fort Lauderdale–Hollywood Airport (Seasonlity Model)Passengers Forecasting 2013

Charlotte Douglas Airport (Seasonlity Model)Passengers Forecasting 2013

Los Angeles Airport (Seasonlity Model)Passengers Forecasting 2013

600,000

700,000

800,000

900,000

1,000,000

1,100,000

1,200,000

1,300,000

No of

Pas

seng

ers

TIME (Month)

Forecast

Actual

R2 = 96.8 %S.T.= 0.002013(F) = 12,080,874 Pax

1,800,000

2,000,000

2,200,000

2,400,000

2,600,000

2,800,000

3,000,000

3,200,000

No of

Pas

seng

ers

TIME (Month)

Forecast

Actual

R2 = 85.6 %S.T.= 0.02013(F) = 21,135,269 Pax

1,800,000

2,000,000

2,200,000

2,400,000

2,600,000

2,800,000

3,000,000

3,200,000

No of

Pas

seng

ers

TIME (Month)

Forecast

Actual

R2 = ̂ 7.7 %S.T.= 42013(F) = 32,269,576 Pax

Airport forecAsting

CAMA Magazine | issue 18 | March, 2013

Minneapolis–Saint Paul International Airport

(IATA: MSP, ICAO: KMSP, FAA LID: MSP) is a joint civil-military public use airport. Located in a portion of Hennepin County, Minnesota outside of any city or school district, within ten miles (16 km) of both downtown Minneapolis and downtown Saint Paul, it is the largest and busiest airport in the five-state upper Midwest region of Minnesota, Iowa, South Dakota, North Dakota, and Wisconsin.

R2= 95.3%S.T.= -3 2013(F) = 16,650,355 Pax

Chicago Midway International Airport

(IATA: MDW, ICAO: KMDW, FAA LID: MDW), is an airport in Chicago, Illinois, United States, located on the city’s southwest side, eight miles (13 km) from Chicago’s Loop.Dominated by low-cost carrier Southwest Airlines, Midway is the Dallas-based carrier’s largest focus city as of 2011. Both the Stevenson Expressway and Chicago Transit Authority’s Orange Line provide passengers access to downtown Chicago. Midway Airport is the second largest passenger airport in the Chicago metropolitan area, as well as the state of Illinois, after Chicago O’Hare International Airport.

R2= 96%S.T.= 4 2013(F) = 9,397,884 Pax

Dallas/Fort Worth International Airport

(IATA: DFW, ICAO: KDFW, FAA LID: DFW) is located between the cities of Dallas and Fort Worth, Texas, and is the busiest airport in the U.S. state of Texas. It generally serves the Dallas–Fort Worth metropolitan area.DFW is the fourth busiest airport in the world in terms of aircraft movements. In terms of passenger traffic, it is the eighth busiest airport in the world. It is the largest hub for American Airlines. DFW Airport is considered to be an Airport City.

R2= 97.3%S.T.= 0.76 2013 (F) = 28,183,463 Pax

35

Dallas Airport (Seasonlity Model)Passengers Forecasting 2013

Minneapolis–Saint Paul Airport (Seasonlity Model)Passengers Forecasting 2013

Chicago Midway Airport (Seasonlity Model)Passengers Forecasting 2013

1,600,000

1,800,000

2,000,000

2,200,000

2,400,000

2,600,000

2,800,000

No of

Pas

seng

ers

TIME (Month)

Forecast

Actual

R2 = ̂ 7.3 %S.T.= 0.752013(F) = 28,183,463 Pax

1,000,000

1,100,000

1,200,000

1,300,000

1,400,000

1,500,000

1,600,000

1,700,000

1,800,000

No of

Pas

seng

ers

TIME (Month)

Forecast

Actual

R2 = ̂ 5.˼ %S.T.= - 32013(F) = 16,650,355 Pax

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

No of

Pas

seng

ers

TIME (Month)

Forecast

Actual

R2 = 96.1%S.T.= 0.02013(F) = 9,397,884 Pax

Airport forecAsting

CAMA Magazine | issue 18 | March, 2013