Delft University of Technology
FACULTY MECHANICAL, MARITIME AND MATERIALS ENGINEERING
Department Marine and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl
This report consists of 12 pages and 2 appendices. It may only be reproduced literally and as a whole. For commercial purposes only with written authorization of Delft University of Technology. Requests for consult are only taken into consideration under the condition that the applicant denies all legal rights on liabilities concerning the contents of the advice.
Specialization: Transport Engineering and Logistics
Report number: 2017.TEL.8101
Title:
Operation performance distinction
of Full Service Airlines and Low
Cost Airlines with the principal
component analysis
Author:
J.D. Hockers
Title (in Dutch) Operationale prestaties onderscheiding tussen volledig dienstverlenende vluchtmaatschappijen en lage kosten vluchtmaatschappijen met behulp van de principale-componentenanalyse.
Assignment: Research Confidential: no
Supervisor: Dr. W.W.A. Beelaerts van Blokland
Delft University of Technology
FACULTY OF MECHANICAL, MARITIME AND MATERIALS ENGINEERING
Department of Marine and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl
Student: J.D. Hockers Assignment type: Research assignment Supervisor (TUD): Dr. W.W.A. Beelaerts
van Blokland
Creditpoints (EC): 15 Specialization: TEL
Report number: 2017.TEL.8101 Confidential: no
Subject: Operation performance distinction of Full Service Airlines and Low Cost Airlines with the principal component analysis.
The airline industry is very competitive with Full Service Airlines (FSA) and Low Cost Airlines (LCA). The different airlines try to be competitive with different cost structures, revenue generation, operational strategy, type of operation and value leverage. For great competitiveness it is of great value for the airlines to have good ratings and therefore a high ranking position compared to other airlines. However, this ranking is mainly based on qualitative data and not on operation performances. Prior research has been done by van Melick (2013) to develop an Operations Performance Analysis Model (OPAM). Different airlines were compared to each other based on operation performances. A dimension reduction technique: Principal Component Analysis (PCA) is used to look for differences between airlines based on operation performances. From this analysis, a not expected possible relationship between the number of 'Principal Components' and the type of airline and their stability of value flow was observed.
The assignment is to update the data from the research done by van Melick and research the number of principal components for each airline. Than trying to make a distinction between the different types of airlines and the number of principal components based on the ‘quick’ return from a recession for the airlines.
The report should comply with the guidelines of the section. Details can be found on the website. The supervisor,
Operation performance distinction of Full Service
Airlines and Low Cost Airlines with the principal
component analysis
Jeroen D. HockersTransport Engineering and Logistics
Faculty of Mechanical, Maritime and Materials Engineering (3mE) Delft University of Technology
The Netherlands
Abstract
In a highly competitive airline market, different types of airlines are operating. The so called Low Cost Airlines are mainly seen as worse performing airlines compared to the Full Service Airlines, but the question is if this is a fair statement. Therefore, different types of airlines from different regions are compared based on different variables. A dimension reduction technique, the so called principal component analysis is used to reduce the number of variables into principal components. A possible relation between the number of principal components and the type of airline was observed. In order to see if it is possible to make a clear distinction between the Full Service and Low Service airlines by the number of principal components, the ’quick’ return from a recession for each airline is investigated. From this, there was no clear relation found between the number of PC’s and the quick return, but other interesting observations were made. Such as, the recession has great influence at the downturn rate for the different type of airlines.
Keywords: Full Service Airline, Low Cost Airline, Principal Component Analysis, Operation Performance
1. Introduction
The airline industry is very competitive with Full Service Airlines (FSA) and Low Cost Airlines (LCA). The different airlines try to be competitive with different cost structures, revenue generation, operational strategy, type of operation and value leverage. For great competitiveness it is of great value for the airlines to have good ratings and therefore a high ranking position compared to other airlines. However, this ranking is mainly based on qualitative data and not on operation performances. E.g. criteria as passenger experience is based on feelings and don’t show how an airline really performs on an operational level. In general, LCA’s ’perform’ worse than FSA’s but this is based on providing services and passenger experiences. The question is if this is a fair and good
way to compare airlines, because LCA’s have a different business model than FSA’s. What if the airlines are compared based on operation performances and will the LCA’s outperform the FSA’s then?
van Melick [1] developed an Operations Performance Analysis Model (OPAM) to assess the stability flow and to show the importance of stability of value flow. Different airlines were compared to each other based on operation performances. For his research he made use of a dimension reduction technique: Principal Component Analysis (PCA), to look for differences between airlines based on operation performances. From this analysis, a not expected possible relationship between the number of ’Principal Components’ and the type of airline and their stability of value flow was observed. From this possible relationship, the main research question for this paper followed:
Can the number of PC’s be used to make a distinction between Full Service Airlines and Basic Service Airlines based on the ’quick’ return from a recession?
The main research question is divided into multiple sub-questions:
1. What is the number of Principal components for the different airlines? 2. How long does it take to recover from a recession for each airline?
This research will use the research of van Melick as a foundation to investigate the questions. The data is updated till 2015, because the annual reports of the airlines for 2016 are not available yet.
2. Literature review
From literature, two main airline business models are recognized: the Low Cost Airline (LCA) and the Full Service Airline (FSA). Differences between the airlines are mainly not clear for the average customer/passenger. Simply put, the passenger wants to go from destination A to destination B with or without (extra) services for a value they think it is worth. So the choice for making use of a FSA or LCA doesn’t depend on the business model of the airline, but just if the airline will fulfill their needs and therefore end up with one of the two type of airline [2]. For better insight in the differences between the different types of airline, first in section 2.1 will the FSA be discussed and then in section 2.2 the LCA, further on the main differences will be described in section 2.3. Also to answer subquestion 2 it is needed to know what a recession means and how it can be identified,. Therefore, in section 2.4 some insights of a recession are outlined.
2.1. Full Service Airline
A FSA focus its operations on a Hub and therefore the rise of LCA’s occurred. FCA’s make use of feeder routes to increase their load factor; this is usually done by using a short distance feeder network. According to (Levine 2009): Full Service Airlines use the hub and spoke system to create factories to manufacture route density. However, a hub and spoke network has compared to a point to point network used by LCA’s, much
heavier overheads. This is because of the FSA’s high cost structures of the past and it is hard to decrease these costs, due to long term contracts which remain in force. The costs of a full service airline can be divided in mainly 3 categories:
1. Overhead costs 2. Operating costs 3. Wages.
Wages, as one of the main categories, is very difficult to reduce the cost, due to old labor conditions and unions usually don’t give in. In periods of downturn, FSA’s can reduce cost by increasing the load factor due to reducing the amount of long haul flights. Decreasing of the long haul flights can be achieved by grounding aircraft. Another way of reducing the costs is by forming alliances to share capacity and overhead.[3]
2.2. Low Cost Airline
A LCA have a cost structure which make it possible to accept low fares and providing flights in less high frequency routes; the LCA focuses on cost minimization. Although this strategy of cost minimization (e.g. By a higher aircraft utilization and seat density) is not suitable for long-haul operations. With the introduction of the LCA, therefore low fares, an often younger and more sensitive to fare levels customer/passenger appeared [4]. For low fares, these customers accept limited services and connection through sec-ondary airports. Advantages for the LCA’s is the secsec-ondary airports give the LCA’s shorter turnaround times, slot availability, low charges, less taxi time and an absence of congestion. LCA’s achieve cost reductions by outsourcing different thing such as cater-ing, maintenance and handling and this result in a higher crew and aircraft productivity. The LCA doesn’t make use of a hub feeder network to obtain high long-haul load factor, instead they achieve a high load factor by changing the frequency of operations. More specifically, by making use of the origin and destination market, a LCA provide low frequency high load connections.[5]
2.3. Differences between FSA and LCA
In the beginning of the nineties, FSA’s had no competition from LCA’s, but this changed when the regulations changed of the airline industry in Europe and the United States. The prices for tickets dropped and the hubs became bigger, which resulted in less domi-nance on the market for the FSA’s.[5] The short-haul European market changed rapidly with the introduction of the characterized Low Cost Airlines (e.g. Easyjet and Ryanair). The LCA was first seen as a type of airline without any service and not reliable. But the customer/passengers changed with the introduction of the LCA’s, as air travel became more accessible for the average people due to lower prices, instead of the expensive com-munity as it was. The new type’ of customer/passenger preferred other things such as no frills, proximity of travel opportunities and mainly cheap fares.[6] According to Berritella et al [6] the difference between the two types of airline are led to the differences in re-liance on critical mass, feeder routes, cost structure and no frills airlines competition. So, the main difference between a LCA and FSA is, the strategy of a LCA focuses on cost minimization. van Melick questioned if this statement is really true by researching
if a LCA have a more stable value in comparison with a FSA. According to van Melick: ”There is a fundamental difference between LCA’s and FSA’s, based on their ability to adapt to natural shocks and establish a long term stable value flow.”
In periods of downturn the FSA’s reduce the capacity of long-haul flights and this result in reducing the variable costs, such as fuel, maintenance and wages. However, this reduction in capacity does not change the fixed costs, which is the main problem. LCA’s tries to keep the utilization of aircraft high during periods of downturn, so the opposite of FSA’s. They do this by varying routes, the frequency of flights and therefore can take over a percentage of the passengers that use a FSA.[6]
2.4. Recession
There are multiple definitions for a recession, but mostly the definition for a recession is when there is a period of general decline (contraction) in the Gross Domestic Product (GDP) for two consecutive quarters [7]. However, according to the National Bureau of Economic Research (NBER) the definition of a recession is:
”A recession is a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales.”[8]
The NBER is the private non-profit that announces when recessions start and stop. It is in the United States the source for measuring the stages of the business cycle. Table 1 shows the last three recessions according to the NBER. The recession periods are starting from a peak and from a trough the recession period is over. Hence the last recession started from December 2007 till June 2009 and this is know as the Great recession.
Table 1: Business Cycle Expansions and Contractions (most recent ones).[8] Business
cycles reference dates Duration in months
Peak Trough Contraction Expansion Cycle
Quarterly dates are
in parentheses Peak to Trough
Previous trough to this peak Trough from Previous Trough Peak from Previous Peak
July 1990(III) March 1991(I) 8 92 100 108
March 2001(I) November 2001 (IV) 8 120 128 128
December 2007 (IV) June 2009 (II) 18 73 91 81
3. Methodology
Since this research use the work of van Melick as the foundation of the research, the methodology for this research will follow the framework done by van Melick. van Melick determined different variables that for mostly are present for LCA’s and FSA’s. Those variables are yearly evaluated and depicted in annual reports. In order to reduce the number of variables a dimension reduction technique was used by van Melick. This dimension reduction technique is the so called, Principal Component Analysis (PCA) and a commonly used technique for complex problems. The principle of this technique will be outlined in section 3.1 and as well the used data in section 3.2. Furthermore the
methodology for assessing the recovery time of an airline from a recession is discussed in section 3.3.
3.1. Principal Component Analysis
Principal component analysis (PCA) is a multivariate technique that analyzes a data table. In this table are several inter-correlated quantitative dependent variables described as observations.[9]
The goals of PCA are to:
• extract the most important data/information from the table;
• reduce the size of the data set by focusing only on the important information; • simplify the description of the data set; and
• analyze the variables and the structure of the observations.
PCA computes so called principal components, which are new variables that are obtained by linear combinations of the original variables of the data set. The first principal component (PC) explains the largest possible variance of the data (observations) and the second PC the next largest variance, but under the constraint that it is orthogonal to the first component; the other PC’s are computed in the same way. The new variables have new values for the observations and are called factor scores, which can be projected onto the principal components.
In order to have a viable answer from the principal component analysis, it is necessary to have consistent data. A PCA is sensitive to trending variables and different units, also with missing values the PCA cannot be performed. If the PCA is performed without detrending the data first, the result of the PCA will describe the trend of the data. It is also necessary to standardize the data, because there are a lot of different units between the variables (e.g. number of passengers and operational costs expressed in dollars). The first principal component found after the PCA, describe the first largest variance in the data set, so the data needs to be scaled accordingly by standardizing the data.
(a) Passengers for JetBlue Airways. (b) Net Income for Air France - KLM. Figure 1: Trending and shock variable plots.
To check if there are trending variables, the different variables are examined by looking at their plots, such as in Figure 1a and 1b. The figures clearly show the difference between a trending variable and a shock variable. The number of passenger keeps increasing (trend) and the net income clearly shows ”shocks” in the results. To detrend the variables, the percentage change of the variables has been used:
Xnew− Xold
Xold
∗ 100. (1)
After the data detrending and standardizing, the principal component analysis can be performed. The first principal component is a linear combination of the standardized variables (Xi); this will describe the largest variance of the data. This first PC is
calcu-lated by maximizing:
var(z) = u0Ru (2)
where z is the vector of the elements, u the vector to maximize with the restriction of u0u = 1 and R is the correlation matrix (X0X). To maximize equation 2, the next equation (eq 3) is set to zero. This results in finding the Langrange multiplier λ.
dL
dU = 2Ru − 2λu (3)
By solving this derivative (eq 3), the eigenvector problem Ru = λu is given. Here, u is the eigenvector and λ the eigenvalue. Now by taking the linear combination of x and the eigenvector ui, the PC score (zi) can be found. The Variance Accounted For (VAF) is
the percentage of a PC that accounts for the total variance of the data set. This can be calculated by:
V AF = λi Σλi
(4)
So the i-th eigenvalue λi is divided by the sum of the individual eigenvalues, Σλi. After
obtaining the principal components and the VAF, it is needed to select the number of PC’s that represents the most important ones. There are mainly two rules known for selecting the number of PC’s, which are the ’Elbow’ and ’Kaiser’ rule. For using the Elbow rule, a so called scree plot is used. Examples of scree plots are shown in Figure 2. With the Elbow rule is looked for an ’elbow’ in the result of the scree plot. It’s where the scree plot goes from ’steep’ to ’flat’ and the number of components are chosen till this elbow. So for Figure 2a it means to choose for 3 principal components as the elbow is clear to see at the third component. When there is no clear sign of an elbow in the result, the Kaiser rule may apply. With the Kaiser rule, the principal components that have an eigenvalue higher than 1 are chosen; this means for Figure A.5a that there are 5 PC’s.
(a) Scree plot for British Airways. (b) Scree plot for Air France - KLM. Figure 2: Scree plots from PCA.
3.2. Data
As this research is a successor of the research of van Melick, the same data is used and updated to the latest annual results from the airlines. However, not for all the different airlines is new data collected, because in this research will be looked at a smaller number of different airlines. Some airlines have been merged with another airline in the recent years and therefore new annual results are not collectible. Also the amount of LCC’s and FSA’s are chosen to be the same as well the number of airlines per region; European Union (EU) airlines and United States (US) airlines. Table 2 shows the different airlines per region and type of airline.
Table 2: Analysed airlines with their sample size in years.
FSA Code Sample size Country
Alaska Airlines ASA 1994-2015 USA Delta Air Lines DAL 1991-2015 USA United Airlines UAL 1996-2015 USA Air France-KLM AFR 2005-2015 France
British Airways BAW 1996-2015 United Kingdom Deutsche Lufthansa DLH 2002-2015 Germany LCA
Allegiant Air AAY 2002-2015 USA
JetBlue Airways JBU 2000-2015 USA Southwest Airlines SWA 1994-2015 USA
easyJet EZY 2004-2015 United Kingdom
Ryanair RYR 2000-2015 Ireland
Vueling Airlines VLG 2006-2015 Spain
The annual results are obtained for the US airlines from the 10-K filing forms. These forms show the yearly results in a standardized format. For the EU airlines, the annual report for each airline is used. A downside of using the annual reports is that it can be non-consistent over the different years, which can make it difficult to obtain the new yearly results of a variable. The variables used for the principal component analysis are shown in Table 3.
Table 3: Variables used for the principal component analysis
Variable Unit Variable Unit
Passengers: [millions] Total Expenses: [$/millions]
ASM: [millions] Salary Expense: [$/millions]
RPM: [millions] Commissions Expense: [$/millions]
Load-Factor: [%] Fuel Expense: [$/millions]
Operating Revenue/ASM: [$/cents] Rentals and Landing Fees: [$/millions] Operating Cost/ASM: [$/cents] Aircraft Rent: [$/millions] Gallons of fuel used: [G/millions] Purchased Services: [$/millions] Average Cost/Gallon: [$] Depreciation/Amortization: [$/millions] Fuel Cost % of Expense: [%] Aircraft Maintenance: [$/millions]
Total Employees: [-] Food and Beverages: [$/millions]
Total Aircraft: [-] Special Charges: [$/millions]
Leased Aircraft: [-] Other Expense: [$/millions]
Owned Aircraft: [-] Operating Income: [$/millions]
Average Aircraft Age: [Years] Net Income: [$/millions] Operating Revenue: [$/millions] Operating Equipment: [$/millions] Passenger Revenue: [$/millions] Owned Equipment: [$/millions] Cargo/Ancillary Revenue: [$/millions] Leased Equipment: [$/millions] Other Revenue: [$/millions]
3.3. Return from recession
In order to answer subquestion 2: (How long does it take to recover from a recession for each airline? ), there is looked at how long it takes for an airline to recover from an recession to its initial state from before the recession. From the literature review it was found that normally a recession is visibly in some variables such as the real income. Therefore, to check the recovery period for each airline, there is looked at the operating income. For comparison between the recovery times between each airline, the Great recession is used as the downturn period. This recession started in December 2007 until June 2009 (Table 1) and is chosen because this recession had occurred for each airline in the dataset.
4. Results
With the updated dataset up till the year 2016 the Principal Component Analysis is performed. After performing the analysis, all the different scree plots were made with
the eigenvalue’s of the PC’s. The largest variation between the number of found PC’s per airline are shown in Figure 3a and 3b.
(a) Scree plot for Allegiant Air. (b) Scree plot for Alaska Airlines. Figure 3: Scree plots from PCA with the smallest and largest amount of PC’s.
Based on the elbow rule, where is looked for the scree plot going from steep to flat, it can be seen that for Allegiant Air in Figure 3a are 2 PC’s. The first PC clearly shows the most variance accounted for (VAF; Equation 2) and from the third PC the scree plot becomes more flat. So for Allegiant Air it can be seen that 2 PC’s represent the most VAF. In Figure 3b the scree plot for Alaska Airlines shows much more PC’s above the criteria for the kaiser rule (eigenvalue more than 1). For Alaska Airlines the scree plot becomes flat from 6 PC’s, therefore 5 PC’s are chosen to represent the most VAF. For all the different airlines their scree plots can be found in Appendix A. The amount of principal components for each airline are depicted in Table 4 and it shows that most of the airlines has 3 or 4 PC’s.
From this results the same possible relation between the number of PC’s and the type of airline can be seen such in van Melick’s research. Besides the possible relation between the type of airline and the number of PC’s, another interesting observation can be made. Namely, the possible relation between the number of PC’s and the region where the airline is stationed. It looks from this dataset that for airlines in Europe fewer PC’s represent an airline than in the USA. Therefore, there is not only the possible relation between the number of PC’s and the type of airline, but also the region where the airline is stationed maybe can have an impact on the number of PC’s.
Next, the recovery time of the different airlines was investigated by looking at the initial value of the operating income and when this value was reached again after the recession began. The results are depicted in Table 5. From this it can be seen that most airlines recover within 3 years and some have more and even Air France-KLM has still not recovered to the initial operating income value. It also shows that Allegiant Air and Vueling Airlines didn’t had a downturn in operating income; they kept growing during the
Table 4: Results of PCA for each airline.
Airline Type Number of PC’s Region
Allegiant Air LCA 2 USA
British Airways FSA 3 EU
Deutsche Lufthansa FSA 3 EU
JetBlue Airways LCA 3 USA
Vueling Airlines LCA 3 EU
easyJet LCA 4 EU
Ryanair LCA 4 EU
Southwest Airlines LCA 4 USA
United Airlines FSA 4 USA
Air France-KLM FSA 5 EU
Alaska Airlines FSA 5 USA
Delta Air Lines FSA 5 USA
recession. During the recovery time analysis, another phenomena was observed. Namely that the operating income for all LCA’s didn’t became negative due to the recession, but for all FSA’s it did except for Deutsche Lufthansa. However, Lufthansa had a major downfall in operating income because of the recession and Vueling Airlines had negative operating income before the recession but their operating income kept increasing during this period. All Figures for the different operating income of the different airlines can be found in Appendix B.
Table 5: Recovery time from the great recession.
Airline Type Recovery Time
(in years) Region
Allegiant Air LCA 0 USA
Vueling Airlines LCA 0 EU
JetBlue Airways LCA 2 USA
Alaska Airlines FSA 2 USA
easyJet LCA 3 EU
Ryanair LCA 3 EU
Southwest Airlines LCA 3 USA
United Airlines FSA 3 USA
Delta Air Lines FSA 3 USA
British Airways FSA 5 EU
Deutsche Lufthansa FSA 8 EU
Air France-KLM FSA not recovered yet EU
5. Conclusions
To answer the main research question:
Can the number of PC’s be used to make a distinction between Full Service Airlines and Basic Service Airlines based on the ’quick’ return from a recession?
First the answers to the two subquestions are formed. Namely, for the first subquestion, the number of principal components differ from 2 to 5 and can be seen in Table 4. From these results the possible relation between the number of PC’s and the type of airline can be noticed. It seems that a LCA have less PC’s than a FSA, and this could mean that a LCA could have a better operation performance than FSA’s because LCA’s are less complex to manage (less PC’s) than a FSA. To determine if it possible to distinguish a LCA and a FSA from each other, based on the number of PC’s. There was looked at the recovery time for the airlines returning to its initial value before the recession. The recovery time looks not related to the number of PC’s found for each airline. Therefore, the answer to the main research question is that it not possible to make a distinction between FSA’s and BSA’s based on the ’quick’ return from a recession. However, this research has led to other insights such as the possible relation between the number of PC’s and the region where the airline is stationed and that during a recession the LCA’s could manage to have positive operating income whereas (almost all) the FSA’s could not.
6. Recommendations
After analyzing the results and the following conclusions. some recommendations for further research can be made. From the first observation that there is a possible relation between the number of PC’s and the region of the airline, the dataset should be expanded with more airlines from those regions. Also it is interesting to see if this possible relation also holds up when there are airlines of other regions added to the dataset. Another recommendation is to investigate the impact of a recession to the different airlines by looking at the rate of downturn (if any) and not looking at the recovery time. Lastly, further research should be done for trying to distinguish the LCA’s and FSA’s based on the number of PC’s, by looking at other variables for the different airlines.
References
[1] P. G. F. van Melick, How to assess stability of value flow: a case study of airline operations, Ph.D. thesis, Delft University of Technology, 2013.
[2] J. F. O’Connell, G. Williams, Passengers’ perceptions of low cost airlines and full service carriers: A case study involving Ryanair, Aer Lingus, Air Asia and Malaysia Airlines, Journal of Air Transport Management 11 (2005) 259–272.
[3] M. G. Lijesen, P. Nijkamp, P. Rietveld, Measuring competition in civil aviation, Journal of Air Transport Management 8 (2002) 189–197.
[4] P. Morrell, Can long-haul low-cost airlines be successful?, Research in Transportation Economics 24 (2009) 61–67.
[5] J. G. de Wit, J. Zuidberg, The growth limits of the low cost carrier model, Journal of Air Transport Management 21 (2012) 17–23.
[6] M. Berrittella, L. La Franca, P. Zito, An analytic hierarchy process for ranking operating costs of low cost and full service airlines, Journal of Air Transport Management 15 (2009) 249–255. [7] Business Dictionary, What is a recession? definition and meaning - BusinessDictionary.com,
http://www.businessdictionary.com/definition/recession.html, Accessed: 2017-01-18.
[8] NBER, The National Bureau of Economic Research, http://www.nber.org/, Accessed: 2017-01-18. [9] H. Abdi, L. J. Williams, Principal component analysis, Wiley Interdisciplinary Reviews:
Computa-tional Statistics 2 (2010) 433–459.
Appendix A. PCA results
(a) Scree plot for Alaska Airlines. (b) Scree plot for Allegiant Air.
(c) Scree plot for Delta Airlines. (d) Scree plot for jetBlue.
(e) Scree plot for Southwest Airlines. (f) Scree plot for United Airlines. Figure A.4: Scree plots from PCA for all airlines in the USA region.
(a) Scree plot for Air France-KLM. (b) Scree plot for British Airways
(c) Scree plot for Deutsche Lufthansa. (d) Scree plot for easyJet.
(e) Scree plot for Ryanair. (f) Scree plot for Vueling Airlines. Figure A.5: Scree plots from PCA for all airlines in the EU region.
Appendix B. Operating Income
(a) Operating Income for Alaska Airlines. (b) Operating Income for Allegiant Air.
(c) Operating Income for Delta Airlines. (d) Operating Income for jetBlue.
(e) Operating Income for Southwest Airlines. (f) Operating Income for United Airlines. Figure B.6: Operating Income for all airlines in the USA region.
(a) Operating Income for Air France-KLM. (b) Operating Income for British Airways
(c) Operating Income for Deutsche Lufthansa. (d) Operating Income for easyJet.
(e) Operating Income for Ryanair. (f) Operating Income for Vueling Airlines. Figure B.7: Operating Incomes for all airlines in the EU region.
