Airline Profitability Research Articles

Environmental and social welfare effects of aviation liberalization: Evidence from Central Asia and China market

We investigate the effects of international aviation liberalization between China and Central Asia on environment and social welfare. Based on monthly international air traffic data between Central Asia and China, we estimate a random-coefficients discrete choice model and perform a counterfactual analysis to quantify the effects of improved air connectivity, mainly considering that the ratio of direct and connecting aviation services between Central Asia and Beijing is the same as those between Central Asia and Russia, between Central Asia and other former Soviet Union countries and between Central Asia and other Asian countries. The main findings are as follows. First, improving air connectivity intensifies competition in the market, resulting in a decrease in the passenger-weighted average airfares between Central Asia and Beijing by 0.86–5.15 US dollars (USD) in 2012 and an increase in the total number of passengers by 2930–20,790. Second, improving air connectivity increases total carbon emissions (CO2) between Central Asia and Beijing by 0.78–6.11 million kilograms (kg) in 2012 but also drives down per capita CO2 emissions in this market by 0.78–2.38 kg. Third, while increasing total environmental externality costs, improving air connectivity significantly enhances consumer welfare and airline profits, resulting in a net social benefit of 14.02–44.12 million USD between Central Asia and Beijing in 2012. Finally, several policy implications are discussed.

Carrot or stick? Environmental and welfare implications of sustainable aviation fuel policies

Sustainable aviation fuel (SAF) serves as a critical short-term measure for reducing aviation's carbon footprint. Two main policy tools, subsidy and quota, have been developed to support its usage. We build an economic model to compare the environmental and welfare impacts of these two policies. First, we find that if an airline uses the same portion of SAF under both policies, the subsidy approach results in reduced SAF costs, augmented airline output, and heightened emissions. Second, the subsidy policy is better than the quota policy in terms of consumer surplus, airline profits, SAF blender profit, and social welfare, if the traditional aviation fuel is sufficiently inexpensive and the emission regulation under the subsidy policy is adequately stringent. Finally, to demonstrate the empirical relevance of our theoretical framework, we have employed empirically-validated parameters for model calibration. Sensitivity analysis indicates that SAF production costs and market potential are pivotal factors influencing governmental policy formulation. With the reduction in SAF production expenses and the growth of the aviation sector, the government is positioned to adopt a more aggressive policy stance, characterized by higher SAF quotas and increased subsidies, to stimulate the uptake of SAF by airlines.

Multiple-Allocation Hub-and-Spoke Network Design With Maximizing Airline Profit Utility in Air Transportation Network

Multiple-Allocation Hub-and-Spoke Network Design With Maximizing Airline Profit Utility in Air Transportation Network

Empirical Analysis of Fleet Commonality Strategy’s Impact on the Operating Performance of the Indian Aviation Sector

Fleet commonality and standardization strategies are recognized in the global airline industry for their operational advantages and potential to ensure sustainability in a highly competitive environment. This study focuses on the Indian airline sector, aiming to verify the pivotal role of cost efficiency, revenue management, fleet size, and characteristics in determining airline profitability, as well as the influence of fleet size and standardization on operating costs. To achieve this, we developed an empirical model to evaluate both operating profitability and cost efficiency provided by fleet commonality. To measure fleet commonality, we employed a fleet standardization index, providing a quantitative assessment of the level of fleet commonality across different airlines. Utilizing real-time data collected extensively from the Directorate General of Civil Aviation (DGCA) in India, we conducted a detailed analysis involving eight prominent airlines for 10 years spanning from 2012 to 2022. Our regression models indicate a strong positive association between fleet commonality and operating performance, underscoring the significance of fleet standardization strategies in enhancing airline profitability. This study not only contributes empirical evidence to the existing literature but also provides valuable insights for airline managers seeking to understand the benefits of fleet commonality in the context of the Indian aviation industry. This research is anticipated to aid airline executives in making informed decisions about fleet management strategies to improve their operational and financial performance in an increasingly competitive market.

Scenario analysis of the impact of ANSP mergers on traffic distribution and congestion

Economic efficiency gains are one potential advantage that Air Navigation Service Provider (ANSP) mergers might bring to the European Air Traffic Management system. An ANSP merger might also affect the use of airspace and consequently the level of congestion. In this paper, we analyze possible effects of an ANSP merger on traffic distribution i.e., route choices of an airline, congestion levels in different parts of the airspace, and consequences for airline's costs. We analyze four scenarios for an OD-pair market with a monopoly airline and two neighbouring ANSPs. For each scenario, we consider both cases: two ANSPs operate as competing providers and set different unit rates for the airline, and a merged ANSP operates as a monopoly service provider that sets a uniform unit rate for the airline. In all scenarios, we assume that the ANSPs are regulated based on a full cost recovery scheme. The numerical examples show that the different unit rates create an incentive for the airline to take detours in order to avoid the airspace with the higher en-route charge, resulting in additional fuel burn and emissions. Meanwhile, the uniform unit rate incentivizes the airline to choose the shorter route more often, leading to higher congestion levels on the shorter route and higher total congestion cost for the airline. However, in our numerical example, the decrease in the total distance-related cost outweighs the increase in the total congestion cost, leading to higher airline profits. We conclude that an ANSP merger that leads to a uniform unit rate can bring positive effects to airlines even if its potential efficiency gains are not taken into account.

Assessing The Impact of Airlines' Strategic Decisions in Fleet Planning on Profitability by Implementing Panel Data Analysis

This study investigates the impact on airline profitability of different types of aircraft that airlines choose as a strategic decision for their flight operations. Dataset were gathered from MIT Airline Data Project for ten airlines operating in the USA for five-year period between 2015-2019. Three different panel data models- Pooled, Fixed Effects, and Random Effects- were employed to examine the effects of aircraft types (small narrow-body, large narrow-body, and wide-body) on profitability. Plm package of R language was used to create panel data models. As a conclusion, Fixed Effects Panel Data Model proved to be the most successful in explaining profit variation in all dataset. Variables determining airline profits change according to the airline specifications and are not time-dependent.

An Empirical Research on The Factors Affecting Profitability in Air Transportation

This study aims to analyse the impact of traditional airline companies' internal and external environmental factors on profitability. In total, 17 airlines operating in different regions were included in the study. Passenger load factor, cargo ton-km and the number of destinations were chosen as internal factors affecting the profitability of airlines. GDP, oil prices and exchange rate figures were external environmental factors affecting profitability. Panel ARDL analysis method was used in the study. In the model of the study, all variables were analysed together without division. All selected variables have been observed to affect airline profitability, and the effect of external environmental factors on profitability is more significant.

Competitive integrated airline schedule design and fleet assignment

Airline profits are significantly influenced by competitive timetables that match passenger demand to fleet resources. In this research, we develop an integrated, mixed-integer optimization model to derive a comprehensive flight schedule, fleet assignment, and average airfares jointly. Passenger choice behavior is incorporated through a prospect theory-adjusted, nested, multinomial logit model which estimates market share. The competitive fleet assignment and schedule design (CFSD) formulation is embedded in a differentiated Bertrand game, such that each transport operator optimizes their best response function connected through the market share model. To solve the integrated optimization problem efficiently, a hybrid algorithm is developed that combines stabilized column generation with a large neighborhood search algorithm.The game-theoretic framework is applied to a case study in China involving legacy airlines, a low-cost carrier, and a high-speed rail (HSR) operator. The equilibrium outcomes suggest that the low-cost carrier is likely to stimulate demand and improve the level of service through secondary airports. The further development of high-speed rail service is expected to intensify competition among airlines and reduce the overall fare level. The low-cost carriers and HSR are able to significantly increase overall consumer surplus, providing insights into the potential for low-cost service in the Chinese aviation markets and the entrance of the public service obligation program at minimum cost.

Flight rescheduling of an airline under the ground delay program considering delay propagation in multiple airports

The purpose of this study is to reschedule flights for an airline's profit to correspond to the airport’s changed capacity. In the event of a ground delay program (GDP), the number of flights the airport can accommodate is reduced. We formulated a mixed-integer linear programming (MILP) model to reschedule flights. The MILP models were divided into two versions to handle the uncertainty of the future. In scenarios in which the GDP is changed again, an optimal model obtains solutions for each scenario. The stochastic model solution obtains a minimizing expectation cost of all scenarios. All flights are connected to both the origin and destination airports, and one aircraft may be used for more than one flight. Therefore, we considered delay propagation not only within the same airport but from other airports by extending the setup to include several airports at once. Because the objective of this study is to minimize the operation cost of airline, we also considered costs associated with airline resources such as aircrafts and crews. Related experiments were conducted including comparison between two suggested versions.

The role of airport costs in the post-9/11 recovery of San Francisco International Airport

This paper examines whether or to what extent San Francisco International Airport’s (SFO’s) purportedly high costs relative to other airports suppressed SFO’s traffic demand and prolonged the recovery of passenger traffic in the period after the 11th September, 2001 terrorist attacks. A comparative analysis of the historical data shows that SFO’s direct airport costs per enplanement (CPE) were higher than most other major airports in the post-9/11 period by a considerable margin, but that airport costs likely were not a significant factor in SFO’s prolonged recovery. Airport costs likely did not affect airline profitability sufficiently to influence route and scheduling decisions, and it is unlikely that passenger demand was dampened by the pass-through of higher costs to airfares. This research suggests at best a tenuous relationship between airport rates and passenger activity, and at SFO the evidence does not suggest that an extremely high CPE relative to peer airports affected the pace of its slow return to pre-9/11 traffic levels. Rather, the slow return of traffic to SFO and its neighbour, Norman Y. Mineta San Jose International Airport (SJC), compared to the robust growth at Oakland International Airport (OAK) can be explained by traditional economic means. It also suggests that in a multi-airport region where each airport is somewhat specialised, an individual airport’s over- or underperformance relative to its neighbours is more likely to be a function of economic geography than of any airport’s rate-setting practices. If this conclusion holds true for other US airports, it emphasises the ability of airports to create capital plans and set rates and charges to make long-term investments in organic air traffic growth, ensuring that new entrant airlines have sufficient facilities to begin service and that existing airlines have sufficient capacity to grow.

THE EFFECT OF LONG-TERM DEBT FINANCING ON PROFITABILITY OF COMMERCIAL AIRLINES IN KENYA

<p>The objective of this study was to assess the effect of long-term debt financing on the profitability of commercial airlines in Kenya. Pecking order theory directed the study. A cross-sectional research design was adopted in the study. The census sampling technique was. Secondary data was collected from audited financial statements. Panel data was analyzed using descriptive and inferential statistics. Descriptive statistics consisted of minimum values, maximum values, mean and standard deviation and inferential statistics consisted of correlational analysis, Hausman test for fixed and random effects and random effects models. Hausman test indicated that the random effect model was appropriate for the study. The study results showed that long-term debt financing has a negative and statistically significant effect on the profitability of commercial airlines in Kenya. This is supported by a regression coefficient of –0.2318 and a p-value of 0.038. The study recommends that executives of commercial airlines should aim to have a long-term debt that is manageable by the company and ensure that the long-term debt load is compatible with a favorable long-term debt ratio for the company to function without worrying about defaulting on loans.</p><p> </p><p><strong>JEL:</strong> R40; L20; G20</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0045/a.php" alt="Hit counter" /></p>

Sequence optimization of departure flights considering ferry scheduling

Due to uncertain factors such as weather, large-scale flight delays will occur on the ground at the airport. A reasonable and effective flight sequence can make the airport resume regular operation as soon as possible. However, limited shuttle bus resources will limit the execution of the flight sequence plan. In order to provide decision support for airport authorities, we establish an integrated optimization model for flight sequences that consider shuttle bus resources. The objective function comprehensively considers the operational losses, airline profit losses, and loss of passenger time value. Then we design a multi-chromosome genetic algorithm to solve the model according to the characteristics of the integration problem. In addition, a simulation experiment is conducted based on the data from Yunnan Kunming Changshui International Airport. The simulation experiment results show that compared with the current first-come-first-served (FCFS) strategy, the algorithm reduces the overall loss caused by delays by 7.03%. The results of the simulation example verify the effectiveness of the proposed model and algorithm.

Support vector regression model for flight demand forecasting

Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits. The traditional flight demand forecasting models generally only take day of the week (DOW) and the current data collection point (DCP) adds up bookings as the model input and uses linear regression, exponential smoothing, pick-up as well as other models to predict the final bookings of flights. These models can be regarded as time series flight demand forecasting models based on the interval between the current date and departure date. They fail to consider the early bookings change features in the specific flight pre-sale period, and have weak generalization ability, at last, they will lead to poor adaptability to the random changes of flight bookings. The support vector regression (SVR) model, which is derived from machine learning, has strong adaptability to nonlinear random changes of data and can adaptively learn the random disturbances of flight bookings. In this paper, flight bookings are automatically divided into peak, medium, and off (PMO) according to the season attribute. The SVR model is trained by using the vector composed of historical flight bookings and adding up bookings of DCP in the early stage of the flight pre-sale period. Compared with the traditional models, the priori information of flight is increased. We collect 2 years of domestic route bookings data of an airline in China before COVID-19 as the training and testing datasets, and divide these data into three categories: tourism, business, and general, the numerical results show that the SVR model significantly improves the forecasting accuracy and reduces RMSE compared with the traditional models. Therefore, this study provides a better choice for flight demand forecasting.

Decomposing airline profit inefficiency in NDEA through the non-competitive Nerlovian profit inefficiency model

With airlines operating in an imperfectly competitive market environment, this study focuses on the important role of price variables. A network data envelopment analysis (NDEA)-based Nerlovian profit inefficiency model for a group of international airlines was proposed, in which the property of unstorable intermediate outputs in the air transport industry is considered. The profit inefficiency of airlines is then decomposed into two stages: cost inefficiency in the production stage and revenue inefficiency in the consumption stage. Both cost inefficiency and revenue inefficiency are further decomposed into three sub-inefficiencies: technical inefficiency, price inefficiency, and allocation inefficiency. The empirical results show that all airlines are profit inefficient and their profit inefficiency is due to different sub-inefficiency components. In terms of overall profit inefficiency, the revenue inefficiency at the consumption stage is the main cause, and price inefficiency is a major component of revenue inefficiency on average.

Methodological framework for a deeper understanding of airline profit cycles in the context of disruptive exogenous impacts

This paper combines the k-means clustering method in combination with PCA and the system dynamic modeling approach to derive a better insight into the behavior of airline profitability during the time span of 1995 until 2020. The model includes various explanatory variables that capture different aspects of airline economic and operational metrics, whose fluctuations may affect the airline profitability. By forecasting these exogenous variables, the system dynamic model is used to predict airline profitability through 2025 and answer the question of whether the US airline industry will return to its pre-COVID 19 pandemic state. The latter research question can be agreed with, as the effect of introducing a fourth dimension derived from Principal Component Analysis (PCA) to sufficiently cover the variation within the dataset during the years of COVID-19 pandemic diminishes towards the end of the forecast period. Furthermore, the key measures from PCA imply that under the assumption of continuous growth and a non-exogenous shock, future years will not cluster in past years. The six different clusters from 2019 to 2025 showed how the system stays in a certain state for a few years and then drifts further to a new state. There are only a few variables that change to transfer from one cluster to the next.

Passenger-Centric Integrated Airline Schedule and Aircraft Recovery

Airlines are known to compete for passengers, and airline profitability heavily depends on the ability to estimate passenger demand, which in turn depends on flight schedules, fares, and the number of seats available at each fare, across all airlines. Interestingly, such competitive interactions and passenger substitution effects may not be limited to the planning stages. Existing regulations in some countries and regions impose monetary compensations to passengers in case of disruptions, altering the way they perceive the utility of other travel alternatives after the disruption starts. These passenger rights regulations may act as catalysts of passengers’ response to recovered schedules. Ignoring such passenger response behavior under operational disruptions may lead airlines to develop subpar recovery schedules. We develop a passenger response model and embed it into a novel integrated optimization approach that recovers airline schedules, aircraft, and passenger itineraries while endogenizing the impacts of airlines' decisions on passenger compensation and passenger response. We also develop an original solution approach, involving exact linearization of the nonlinear passenger cost terms, combined with delayed constraint generation for ensuring aircraft maintenance feasibility and an acceleration technique that penalizes deviations from planned schedules. Computational results on real-world problem instances from two major European airlines are reported, for scenarios involving disruptions, such as delayed flights, airport closures, and unexpected grounding of aircraft. Our approach is found to be tractable and scalable, producing solutions that are superior to airline’s actual decisions and highly robust in the face of passenger response uncertainty. Of particular relevance to the practitioners, our simulation results highlight that accounting for passengers’ disruption response behaviors, even in a highly approximate manner, yields significant benefits to the airline compared with not accounting for them at all, which is the current state-of-the-art. Funding: This work was supported by the Agencia Estatal de Investigación [Grant PID2020-112967GB-C33], the Ministerio de Economía y Competitividad, Spain [Grant TRA2016-76914-C3-3-P], and the Ministerio de Ciencia, Innovación y Universidades, Spain [Grant CAS19/00036].

The Impact of Open Sky on Airlines Theoretical Study: Case of Tunisia

At the level of this article to understand the potential economic effects of an “open skies” agreement, in particular on competition, prices and airline profits. We will try in a first part to determine if consumers will benefit from price reductions following the entry into force of an Open Sky agreement, we will also try to see the effects of “open sky” agreements on competition.

Decision Support Models for Predicting and Explaining Airport Passenger Connectivity From Data

Predicting if passengers in a connecting flight will lose their connection is paramount for airline profitability. We present novel machine learning-based decision support models for the different stages of connection flight management, namely for strategic, pre-tactical, tactical and post-operations. We predict missed flight connections in an airline’s hub airport using historical data on flights and passengers, and analyse the factors that contribute additively to the predicted outcome for each decision horizon. Our data is high-dimensional, heterogeneous, imbalanced and noisy, and does not inform about passenger arrival/departure transit time. We employ probabilistic encoding of categorical classes, data balancing with Gaussian Mixture Models, and boosting. For all planning horizons, our models attain an area under the curve (AUC) of the receiver operating characteristic (ROC) higher than 0.93. SHAP value explanations of our models indicate that scheduled/perceived connection times contribute the most to the prediction, followed by passenger age and whether border controls are required.

PERFORMANCE OF AIRLINES: TOPSIS ANALYSIS

Purpose- Airline industry is very important for modern society and has a crucial role for globalization and business development by connecting regions, promoting global trade and tourism and hence enabling economic and social development. Over the last two decades, the troubled airlines were oftenly on news delebrating financial difficulties, layouts and distrupted sceduled flights. Covid-19 also has a crucial impact on the deterioration of the industry due to quarantinas and break downs for travel and business. This empricial research intends to analyse and rank the financial performance of the top 11 airlines in the Word for the periods of 2019-2020-2021 (Covid era) and also group and rank these airlines as US Airlines, European Airlines and Chinese Airlines. Methodology- This study employs TOPSIS method. 11 biggest airline companies are selected by the total revenue generated in 2021. A total of 18 financial indicators are used to measure the profitability, liquidity, financial structure and operational efficiency for the period of 2019-2021. Utilizing a TOPSIS analysis, the performance scores of these airline companies are calculated and a ranking is determined according to these scores. Findings- United Airlines, Delta Airlines and Southwest Airlines are the top performers in 2019. These airlines have a big drop in 2020 and their performance ranks are 8, 10 and 7, respectively. The best performer in 2020 is the Turkish Airlines. Also, United Airlines and Southwest Airlines show a fast recovery in 2021 by ranking 2 and 1, respectively. When performance ranking is applied for the groups of companies (namely; USA based, Europe based and China based companies), the USA based group is the best performer in 2019, the worst performer in 2020 and the second best performer in 2021. Meanwhile, China based group are the best performer in 2020 and 2021. Conclusion- This study shows that the Covid era has affected profitability and operational efficiency of airline companies significantly. As a result of the study, it is observed that China based airline companies managed the Covid era better than the USA based and European based airline companies. Keywords: TOPSIS, multi-criteria decision making, financial ratios, airline companies, performance ranking. JEL Codes: C02, D22, D53

Assessing the effect of proactive maintenance scheduling on maintenance costs and airline profitability

This study mainly aims to examine the perception of Turkish Airlines Technic staff about the effect of proactive maintenance scheduling (PMS) on maintenance costs (MCs) and airline profitability (AP). Another purpose of the study is to reveal if the perception of the staff changes according to the demographic features and experience of the staff. As far as it is known, there is no direct study addressing the effects of PMS on MCs and airline profitability. The study is considered to be a first in this context. In the study, Turkish Airlines-THY was chosen as the company that was handled. The fact that the company has a separate special maintenance & repair group (Turkish Airlines Technic-THYT) was effective in the selection of this company. Some additional qualifications (very rooted, with corporate governance, Turkey's top largest & full-service carrier, member of IATA and Star Alliance, and a listed company (in BIST) were also among other factors affecting the selection. Methodologically, it was used a quantitative survey with a sample of 133 THYT staff. The staff has been chosen among departments that take place actively/directly in maintenance. Besides questions that aim to measure the perception of THYT staff on the effects of PMS on MCs and AP, some demographic and experience-related questions were asked in order to find out if the perception of the staff changes according to their qualifications. The data were collected by online survey method and analyzed with SPSS version 22. Necessary tests together with Chi-square and Fisher’s tests were carried out to analyze the results. It was concluded that there is a strong correlation between PMS and the MCs. The study proved that PMS has a great decreasing effect on MCs and a great increasing effect on AP. The study also revealed that THYT personnel is like-minded and that PMS reduces MCs and increases AP as well.