Search

The aviation industry has experienced substantial growth in a highly competitive environment, especially during recent years. One of the consequences that this scenario has caused is that, in order to remain competitive in the market, airlines have had to perform continuous innovations in their seat pricing strategies to optimise the profitability of their flights. The concept of Revenue Management involves strategically making decisions during the sales process, with the primary objective of maximising the total revenue that can be obtained. One of the key aspects that plays a fundamental role throughout this process lays on modelling future demand, as well as the various behaviours that potential customers may exhibit. This project primarily focuses on two pillars: the categorization of the different types of demand in the sale of airline tickets depending on the behaviour of customers and the forecast of such demand in the short term. Additionally, the entire project has been carried out using real data obtained from historical bookings. Throughout this work, various traditional methods of time series forecasting are studied and analysed and, with the obtained results, a validation process is conducted and a comparison of the performance of each of the forecasting methods is carried out to ultimately choose the most appropriate method in each case. Finally, the obtained results are used to develop a final forecast of the demand for a specific flight in a certain date that allows to optimise revenue by means of an allocation of fare classes for different seats on the same flight after determining the availability of each fare class that should be offered throughout the entire sales process., Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement Econòmic

In December 2023, public-transportation providers in Switzerland introduced Public-Transportation Credits (PTCs). PTCs are credits (or ''allowances'') that are greater in amount than their price and can be used to purchase any type of public-transportation tickets within a year. With the initial fixed payment, the subsequent use of the allowance and the eventual return to the standard fare, PTCs represent three-part tariff models. We explore the potential of PTCs to target particularly elastic segments of the demand curve, simultaneously allowing for increased consumption and higher revenue. To assess the revenue impact of the PTC empirically, we analyze a pilot study conducted by the Swiss public-transportation providers. In a randomized field experiment with 200,000 PTC invitees and 911 actual PTC buyers, we use the dispach of invitations as an instrumental variable. While observing substantial revenue increases, this result is insignificant due to the weak relationship between invitees and buyers. Therefore, we complement our analysis with a selection-on-observable approach, utilizing machine-learning techniques to match PTC buyers to customers in the control group. This way, we reveal a highly significant treatment effect, indicating a revenue enhancement of CHF 179.7 per PTC (approximately USD 200). Leveraging our comprehensive dataset and insights from a non-buyer survey, we predict a demand of around 200,000 units for the market-launch version of the PTC., + ID der Publikation: hslu_106720 + Art des Beitrages: Wissenschaftliche Medien + Sprache: Englisch + Letzte Aktualisierung: 2024-07-09 15:57:36

The aviation industry is a highly competitive industry, with airlines constantly adjusting their capacity in response to changes in demand and other market factors. To stay ahead of the competition, it is essential that airlines closely monitor their competitors’ capacity adjustments and respond appropriately. To meet this need, a pilot project was undertaken to create a tool developed in Power BI to monitor capacity adjustments, trends, and strategies of Air France’s main competitor airlines on a weekly basis. All these elements can benefit different departments, including revenue management, network planning or corporate strategy. Revenue management can adjust pricing and inventory strategies, network planning can identify new market opportunities or threats in order to adjust its routes, and corporate strategy can gain insights on mergers and acquisitions, partnerships and other strategic initiatives. All in all, it has the potential to improve the competitiveness of both the airline and the Air France-KLM group.

This paper investigates the application of dynamic pricing in the language travel industry as a strategy to enhance total revenue and capacity utilization, contrasting it with the current static pricing approach. Despite dynamic pricing’s success in other industries characterized by capacity-constrained perishable products, such as airlines and hotels, it has not yet been adopted by language travel companies due to a lack of industry-specific data and implementation uncertainties. The study aims to learn from these analogous industries, identify key factors for successful dynamic pricing implementation, and evaluate the language travel industry’s suitability for this approach. The research methodology involves stakeholder and expert interviews, a literature review on dynamic pricing in relevant industries, thematic analysis of interview data to develop a theoretical framework, and a quantitative analysis of sales data using methods like time series forecasting, clustering, singular value decomposition, variable pricing, price differentiation, and network models. Key findings suggest that language travel companies should initially implement simpler algorithms, such as static variable pricing and basic price differentiation models, and gradually transition to more complex price differentiation models and network models. Similarly, the implementation should initially focus on the most sold products and if suitable expand to more product types. Effective implementation requires a reasonable degree of demand predictability, clear customer segmentation, understanding price elasticity, conducting pricing experiments, significant investments in IT infrastructure, aligning business processes, and potential restructuring. Integrating human oversight with technological tools is essential for real-time management and maintaining positive customer perceptions through transparency about price fluctuations. The study concludes that dynamic pricing is largely suitable for the langu, Denna artikel undersöker tillämpningen av dynamisk prissättning i språkreseindustrin som en strategi för att öka total intäkt och kapacitetsutnyttjande, i kontrast till den nuvarande statiska prissättningsmetoden. Trots att dynamisk prissättning har haft framgång i andra branscher med kapacitets-begränsade, förgängliga produkter såsom flyg- och hotellindustrin, har den ännu inte antagits av språkresebolag på grund av brist på branschspecifik data och osäkerheter kring implementeringen. Studien syftar till att dra lärdomar från dessa liknande industrier, identifiera nyckelfaktorer för en framgångsrik implementering av dynamisk prissättning, och utvärdera språkreseindustrins lämplighet för denna strategic. Forskningsmetodiken inkluderar intervjuer med intressenter och experter, en litteraturöversikt om dynamisk prissättning i relevanta industrier, tematisk analys av intervjudata för att utveckla ett teoretiskt ramverk, och en kvantitativ analys av försäljningsdata med metoder som tidsserieprognoser, klustring, singulärvärdesuppdelning, variabel prissättning, prisskillnadsmodeller och nätverksmodeller. Resultaten tyder på att språkresebolag initialt bör implementera enklare algoritmer, såsom statisk variabel prissättning och grundläggande prisskillnadsmodeller, och gradvis övergå till mer komplexa prisskillnadsmodeller och nätverksmodeller. Initialt bör fokus för implementeringen ligga på de mest sålda produkterna, och sedan expandera till fler produkter om det är lämpligt. För en effektiv implementering krävs möjligheter för att förutsäga efterfrågan, tydlig kundsegmentering, förståelse för priselasticitet, genomförande av prissättningsexperiment, betydande investeringar i IT-infrastruktur, anpassning av affärsprocesser och eventuell omstrukturering. Att integrera mänsklig övervakning med teknologiska verktyg är avgörande för realtidsstyrning och att bibehålla positiva kunduppfattningar genom transparens kring prisfluktuationer. Studien drar slutsatsen att dynamisk pr

In December 2023, public-transportation providers in Switzerland introduced Public-Transportation Credits (PTCs). PTCs are credits (or ''allowances'') that are greater in amount than their price and can be used to purchase any type of public-transportation tickets within a year. With the initial fixed payment, the subsequent use of the allowance and the eventual return to the standard fare, PTCs represent three-part tariff models. We explore the potential of PTCs to target particularly elastic segments of the demand curve, simultaneously allowing for increased consumption and higher revenue. To assess the revenue impact of the PTC empirically, we analyze a pilot study conducted by the Swiss public-transportation providers. In a randomized field experiment with 200,000 PTC invitees and 911 actual PTC buyers, we use the dispach of invitations as an instrumental variable. While observing substantial revenue increases, this result is insignificant due to the weak relationship between invitees and buyers. Therefore, we complement our analysis with a selection-on-observable approach, utilizing machine-learning techniques to match PTC buyers to customers in the control group. This way, we reveal a highly significant treatment effect, indicating a revenue enhancement of CHF 179.7 per PTC (approximately USD 200). Leveraging our comprehensive dataset and insights from a non-buyer survey, we predict a demand of around 200,000 units for the market-launch version of the PTC., + ID der Publikation: hslu_106720 + Art des Beitrages: Wissenschaftliche Medien + Sprache: Englisch + Letzte Aktualisierung: 2024-07-09 15:57:36

This paper investigates the application of dynamic pricing in the language travel industry as a strategy to enhance total revenue and capacity utilization, contrasting it with the current static pricing approach. Despite dynamic pricing’s success in other industries characterized by capacity-constrained perishable products, such as airlines and hotels, it has not yet been adopted by language travel companies due to a lack of industry-specific data and implementation uncertainties. The study aims to learn from these analogous industries, identify key factors for successful dynamic pricing implementation, and evaluate the language travel industry’s suitability for this approach. The research methodology involves stakeholder and expert interviews, a literature review on dynamic pricing in relevant industries, thematic analysis of interview data to develop a theoretical framework, and a quantitative analysis of sales data using methods like time series forecasting, clustering, singular value decomposition, variable pricing, price differentiation, and network models. Key findings suggest that language travel companies should initially implement simpler algorithms, such as static variable pricing and basic price differentiation models, and gradually transition to more complex price differentiation models and network models. Similarly, the implementation should initially focus on the most sold products and if suitable expand to more product types. Effective implementation requires a reasonable degree of demand predictability, clear customer segmentation, understanding price elasticity, conducting pricing experiments, significant investments in IT infrastructure, aligning business processes, and potential restructuring. Integrating human oversight with technological tools is essential for real-time management and maintaining positive customer perceptions through transparency about price fluctuations. The study concludes that dynamic pricing is largely suitable for the langu, Denna artikel undersöker tillämpningen av dynamisk prissättning i språkreseindustrin som en strategi för att öka total intäkt och kapacitetsutnyttjande, i kontrast till den nuvarande statiska prissättningsmetoden. Trots att dynamisk prissättning har haft framgång i andra branscher med kapacitets-begränsade, förgängliga produkter såsom flyg- och hotellindustrin, har den ännu inte antagits av språkresebolag på grund av brist på branschspecifik data och osäkerheter kring implementeringen. Studien syftar till att dra lärdomar från dessa liknande industrier, identifiera nyckelfaktorer för en framgångsrik implementering av dynamisk prissättning, och utvärdera språkreseindustrins lämplighet för denna strategic. Forskningsmetodiken inkluderar intervjuer med intressenter och experter, en litteraturöversikt om dynamisk prissättning i relevanta industrier, tematisk analys av intervjudata för att utveckla ett teoretiskt ramverk, och en kvantitativ analys av försäljningsdata med metoder som tidsserieprognoser, klustring, singulärvärdesuppdelning, variabel prissättning, prisskillnadsmodeller och nätverksmodeller. Resultaten tyder på att språkresebolag initialt bör implementera enklare algoritmer, såsom statisk variabel prissättning och grundläggande prisskillnadsmodeller, och gradvis övergå till mer komplexa prisskillnadsmodeller och nätverksmodeller. Initialt bör fokus för implementeringen ligga på de mest sålda produkterna, och sedan expandera till fler produkter om det är lämpligt. För en effektiv implementering krävs möjligheter för att förutsäga efterfrågan, tydlig kundsegmentering, förståelse för priselasticitet, genomförande av prissättningsexperiment, betydande investeringar i IT-infrastruktur, anpassning av affärsprocesser och eventuell omstrukturering. Att integrera mänsklig övervakning med teknologiska verktyg är avgörande för realtidsstyrning och att bibehålla positiva kunduppfattningar genom transparens kring prisfluktuationer. Studien drar slutsatsen att dynamisk pr

Imagine that you are the manager of a service system from which you are trying to make a profit. You have the problem of estimating system parameters and using the information to determine the prices of the service that you provide. We begin by introducing a pricing model for a monopoly retailer selling perishable goods to strategic customers. Customers arrive according to a Poisson process, and upon arrival, they decide whether to join the system, purchase non-fresh goods or balk based on a reward-cost structure. We model the interaction between customers and the retailer as a two-population game and derive Nash equilibrium behaviors of the retailer and customers. Our approach enables optimal values of both fresh goods and non-fresh goods to be identified in a short time. Then we consider a system where customers are inhomogeneous with varying valuations of goods. At their arrival times, customers are informed about the state of the current system. Customers might arrive and then immediately balk. Those balking customers are not observed by the system manager. The aim of the system manager is to estimate the customers’ valuation distribution as well as the arrival rate of customers including balking customers. We model the arrival process of customers by a Markovian Arrival Process and derive the joint distribution of the inter-arrival times of customers. We develop MLE estimators for the parameter of the customers’ valuation distribution and the corresponding arrival rate. We consider a variation of the problem of estimating customers’ valuation distribution and the arrival rate of customers where goods are permitted to be perishable. The MLE estimator performance is demonstrated through simulations. The sensitivity analysis of the perishing rate can help to understand how the performance of the system is affected by changes in this parameter. Finally, we consider several extensions to existing models and propose some new ideas and models, which, while we have not

Purpose: This paper aims to investigate the links between daily review sentiment and the hotel performance measures of occupancy rate (OR), average daily rate (ADR) and revenue per available room (RevPAR). Design/methodology/approach: The authors conducted review sentiment analyses in three moments (−1, −7 and −14 days) before arrival time using a data set of budget hotel performance and online reviews. The aim was to identify the effect of review sentiment in the budget hotel market on the three performance metrics. Findings: Daily sentiment positively affects ADR and negatively affects OR and RevPAR, but only up to a certain threshold, after which the trend reverses. Prices increase with the level of sentiment, and high prices lead to low OR and RevPAR only when the sentiment scores are low. When they are high, they are associated with low rates, which lead to high OR and RevPAR. Research limitations/implications: Daily review sentiment can be viewed as a valuable “barometer” indicating a hotel’s daily operational effectiveness. Daily sentiment can thus allow hotel managers to adjust their dynamic pricing strategies more accurately. Originality/value: This study identifies daily sentiment as an alternative predictor of hotel performance. In addition to the roles of valence and volume in the decision-making process, the authors found that daily review sentiment can be an “in-the-moment” factor with a high impact, encouraging consumers to complete their transactions. This study suggests that aggregated measures such as the total number of reviews and overall ratings of the hotel should not be the sole consideration in reputation management.

Problem definition: We study the adherence to the recommendations of a decision support system (DSS) for clearance markdowns at Zara, the Spanish fast fashion retailer. Our focus is on behavioral drivers of the decision to deviate from the recommendation, and the magnitude of the deviation when it occurs. Academic/practical relevance: A major obstacle in the implementation of prescriptive analytics is users’ lack of trust in the tool, which leads to status quo bias. Understanding the behavioral aspects of managers’ usage of these tools, as well as the specific biases that affect managers in revenue management contexts, is paramount for a successful rollout. Methodology: We use data collected by Zara during seven clearance sales campaigns to analyze the drivers of managers’ adherence to the DSS. Results: Adherence to the DSS’s recommendations was higher, and deviations were smaller, when the products were predicted to run out before the end of the campaign, consistent with the fact that inventory and sales were more salient to managers than revenue. When there was a higher number of prices to set, managers of Zara’s own stores were more likely to deviate from the DSS’s recommendations, whereas franchise managers did the opposite and showed a weak tendency to adhere more often instead. Two interventions aimed at shifting salience from inventory and sales to revenue helped increase adherence and overall revenue. Managerial implications: Our findings provide insights on how to increase voluntary adherence that can be used in any context in which a company wants an analytical tool to be adopted organically by its users. We also shed light on two common biases that can affect managers in a revenue management context, namely salience of inventory and sales, and cognitive workload. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1166 .

The success of revenue management models depends to a large extent on the quality of historical data used to forecast future bookings. Several theoretical models and best practices of handing historical data have been developed over the years, that all rely on assumptions about underlying distribution and seasonality in the historical data. In this paper, we describe a novel method that compares the fingerprints of the departure to optimise and selects historical departures without making assumptions on data distribution or seasonality. By evaluating the method at the departure level and using the Nemenyi rank test, we show the method's application in the ferry transportation business and discuss its advantages.

The success of revenue management models depends to a large extent on the quality of historical data used to forecast future bookings. Several theoretical models and best practices of handing historical data have been developed over the years, that all rely on assumptions about underlying distribution and seasonality in the historical data. In this paper, we describe a novel method that compares the fingerprints of the departure to optimise and selects historical departures without making assumptions on data distribution or seasonality. By evaluating the method at the departure level and using the Nemenyi rank test, we show the method's application in the ferry transportation business and discuss its advantages.

The success of revenue management models depends to a large extent on the quality of historical data used to forecast future bookings. Several theoretical models and best practices of handing historical data have been developed over the years, that all rely on assumptions about underlying distribution and seasonality in the historical data. In this paper, we describe a novel method that compares the fingerprints of the departure to optimise and selects historical departures without making assumptions on data distribution or seasonality. By evaluating the method at the departure level and using the Nemenyi rank test, we show the method's application in the ferry transportation business and discuss its advantages.

The success of revenue management models depends to a large extent on the quality of historical data used to forecast future bookings. Several theoretical models and best practices of handing historical data have been developed over the years, that all rely on assumptions about underlying distribution and seasonality in the historical data. In this paper, we describe a novel method that compares the fingerprints of the departure to optimise and selects historical departures without making assumptions on data distribution or seasonality. By evaluating the method at the departure level and using the Nemenyi rank test, we show the method's application in the ferry transportation business and discuss its advantages.

The success of revenue management models depends to a large extent on the quality of historical data used to forecast future bookings. Several theoretical models and best practices of handing historical data have been developed over the years, that all rely on assumptions about underlying distribution and seasonality in the historical data. In this paper, we describe a novel method that compares the fingerprints of the departure to optimise and selects historical departures without making assumptions on data distribution or seasonality. By evaluating the method at the departure level and using the Nemenyi rank test, we show the method's application in the ferry transportation business and discuss its advantages.

We study the optimal selling scheme when a seller sells a product/service with a positive consumption externality, and customers are uncertain about the product's/service's value. Because early adopters learn this value, we consider the customers' intrinsic signaling incentives and positive feedback effects. Allowing the seller to choose the sequence of customers' decisions (as an endogenous hierarchy) implies that signaling takes place in multiple rounds. To tackle this hierarchical signaling problem, we develop a novel dynamic-programming approach that yields analytical expressions of the equilibrium outcomes. The profit-maximizing structure turns out to be a chain in which the seller sells to the customers one by one. The sequential selling scheme is not only profitable for the seller but also beneficial to the society. The profit-maximizing pricing exhibits a cream-skimming property-decreasing prices along the sequence. The lack of seller's commitment is detrimental to the social welfare; nonetheless, the sequential selling still boosts up the seller's profit. When customers are allowed to freely disclose their quantity decisions to others, they willingly comply with the seller's plan of information transmissions. Despite the inherent value uncertainty, the optimal usage of information transmissions requires deliberate restricted access to information among customers. Our result also suggests some congruence between the profit and welfare maximization.

The success of revenue management models depends to a large extent on the quality of historical data used to forecast future bookings. Several theoretical models and best practices of handing historical data have been developed over the years, that all rely on assumptions about underlying distribution and seasonality in the historical data. In this paper, we describe a novel method that compares the fingerprints of the departure to optimise and selects historical departures without making assumptions on data distribution or seasonality. By evaluating the method at the departure level and using the Nemenyi rank test, we show the method’s application in the ferry transportation business and discuss its advantages.

Purpose: The purpose of this study is to examine the Relationship between Revenue Management Practices and Financial Performance in Star-Rated Hotels in Sri Lanka. Method: A quantitative research design was employed, utilizing a cross-sectional approach to collect data at a specific point in time. The sample was selected using stratified random sampling to ensure representation across hotels. Data was gathered through structured questionnaires and supplemented with secondary data from financial reports and industry publications. Results and Conclusions: The study revealed that 40.9% of surveyed hotels had fully implemented revenue management procedures, emphasizing their strategic importance. Significant positive correlations were found between revenue management practices and financial performance indicators, including ADR, occupancy rate, RevPAR, GOPPAR, and return on investment. Effective revenue management practices were shown to enhance competitive advantage and market differentiation. Regression analyses further highlighted the substantial impact of "Revenue per Available Room" on return on investment, underlining the importance of optimizing this metric. The research underscores the strategic importance of revenue management practices in the hospitality industry and their positive influence on financial performance. Research Implications: Hoteliers should consider these findings when formulating strategies to remain competitive and financially viable. Effective revenue management contributes to enhancing competitive advantage and market positioning. Originality/Value: Study provides insights into the pivotal role of revenue management practices in shaping the financial performance of star-rated hotels in Sri Lanka. Ethical considerations were a cornerstone of this study, ensuring the well-being and rights of participants and setting a high standard for future research in the field.

While most businesses actively adopt a data-driven approach for revenue management decisions, understanding how air travelers perceive and behave differently to pricing strategies is essential for yielding optimal financial outcomes. This study analyzes the loss aversion and diminishing sensitivity mechanisms of prospect theory in economy and business cabin classes. With rich longitudinal airfares, regression models and revenue data (15,868 observations from the top-10 aviation routes in the world) from 2014 to 2019, this study finds that lower-(higher-)than-expected airfares have a positive (negative) impact on revenue. When the effect of loss-coded and gain-coded tickets were compared, the extent to which passengers avoided losses (vs. welcomed gains) had a greater impact on revenue, supporting that loss aversion applies to the airline revenue, especially for business passengers. This study contributes to the further refinement of prospect theory by showing that the loss aversion and diminishing sensitivity mechanisms manifest differently in each cabin class.

In Revenue Management (RM) systems, information censoring and the interaction between the forecasting and optimization stages, increases the costs and complexity of performance analysis using historical data. An affordable and suitable alternative is using simulations, but appropriate behavioral models must be considered. In the following document, we discuss and test the implementation of a dynamic air transport market simulator, designed to analyze RM systems. The simulator replicates the behavior of passengers that book seats offered in multiple flights by different airlines. We use discrete choice models to replicate the demand behavior, accounting for preferences and decision rule heterogeneity, and including a temporal evolution of the preference throughout the selling horizon. To replicate the supply behavior, a number of airlines modify the price and quantity of different fare classes offered in each flight, using a variety of RM forecasting, un-constraining, and optimization techniques. The simulator allows analysts to study the economic benefit of RM systems under predefined assumptions in an artificial and controlled environment. This increases the benefits obtained by the correct selection of context-appropriate RM systems and the likelihood of successfully implementing new and complex systems. We test and showcase the simulator performance, studying the entrance of a new airline in a competitive context. We generate, implement and evaluate different RM strategies in response to the introduction of new competition, and discuss the results, highlighting the interpretability and accuracy of the proposed framework., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Aerospace Transport & Operations

The purpose of this article is to prove the need to perceive revenues as a manageable variable by applying an active approach to companies’ management during the COVIDization of the economy. In this regard, the article illustrates the role of revenues as a factor and as a result. Revenues underlie the formation of all major financial indicators. They are key measures of a company’s growth and performance. This dual nature of revenues is revealed in the context of economics, accounting and financial management. Their importance determines the need to adopt an active approach to revenue management of the economy during COVID-19 pandemic or other similar future occurrences. From a theoretical point of view, the active approach has been justified in the context of the classical strategic management methods emphasising the leading role of marketing. The main research hypothesis tested is that in the conditions of a sharp change in the economy during COVIDization, the importance of the active approach of operating revenue management as the main variable (KPI), cannot be understated.

The significant contribution of raw materials inbound logistics to the reduction of the procurement cost have motivated several recent studies, particularly in the forestry sector. New routing and truck scheduling techniques have been proposed to plan in advance the arrival of the trucks to the mill’s gates. Yet, little attention has been given to the trucks rece ption, unloading and internal movements until exiting the facilities. Consequently, congestion and queuing of the trucks at the mill gate often occur, increasing the duration and costs of the transportation services. This paper introduces the Raw Materials Reception Problem (RMRP) and presents an innovative solution approach anchored on Revenue Management principles often used in for example airline and hotel booking systems. The solution builds on segmenting carriers/trucks and their dynamic assignment to the time slots available at the unloading docks, awarding on -time arrivals. Direct unload at the production lines is further preferred. It avoids, whenever is possible, the passage in the intermediate stockyard, therefore leading to a reduction of the mate rial handling cost. Results of the application of this solution approach to the wood supply of a Portuguese pulp mill are presented. The case study encompasses 120 pulpwood daily deliveries on 6 possible unloading docks inside the mill. The proposed approa ch led to a reduction of 55% on the daily raw material reception cost when compared to the FIFO approach used today. The maximum waiting time for each truck was also reduced, particularly for the trucks on higher priority segments. This approach was implem ented in the Wood Delivery System prototype and then used for conducting a trade -off analysis that provided valuable information to support decision -making in respect to the design of the reception process.

The censored demand estimation problem has been well studied over the past 20 years. Theseminal paper is Talluri and Van Ryzin 2004, which models demand as the product of an unobserved arrival rate and purchase probability, and uses Expectation Maximization (EM) for estimation. In this work, I apply their model to the case of a small hotel with a single product, and adapt the model to use a time-varying arrival rate. Modeling the arrival rate as a step function, the choice of where the steps fall is a crucial hyperparameter, so I propose a heuristic algorithm, the Step Variance Minimization Heuristic (SVMH), to specify the size and location of the steps in the arrival rate function. On both real and simulated hotel data, SVMH is superior in terms of both in-sample and out-of-sample mean squared error when compared to (a) a constant arrival rate and (b) evenly-spaced steps of the same num- ber. To improve the convergence time of EM, I introduce a variant algorithm, Regularized Expectation Maximization (REM), that uses 81% fewer iterations than EM, on average. I show that REM is a Generalized Expectation Maximization (GEM) algorithm, and thus has similar theoretical properties to EM. I also conducted multiple numerical studies that show that models estimated using REM perform similarly in-sample and out-of-sample to their EM counterparts, and produce nearly identical revenue curves. Thus, the convergence improvements offered by REM do not come at a cost to good parameter estimation.

Most research regarding revenue management in manufacturing has considered only a short-term planning horizon, assuming supply and production data exogenously given. Motivated by the case of the Canadian softwood lumber industry, this paper offers additionally a medium-term visibility for firms with limited capacity and faced with seasonal markets. We propose a demand management process for Make-To-Stock environments, integrating sales and operations planning (S&OP) and order promising based on revenue management concepts. Given heterogeneous customers, divergent product structure and multiple sourcing locations in a multi-period context, we first define a multi-level decision framework in order to support medium-term, short-term and real-time sales decisions in a way to maximize profits and to enhance the service level offered to high-priority customers. We further propose a mathematical formulation integrating an S&OP network model in the Canadian softwood lumber industry and an order promising model using nested booking limits. This new formulation allows reviewing previous order promising decisions while respecting sales commitments. A rolling horizon simulation is used to evaluate the performance of the proposed process in various demand scenarios and provides evidence that better performances can be achieved compared to common demand management practices by integrating S&OP and revenue management concepts.

Objective: This paper aims to develop a basic understanding of a demand management process integrating sales and operations planning (S&OP) and order promising in a Make-To-Stock environment and to compare different demand management policies. Contribution: Typical researches about demand management processes analyze few system specifications or vary few potential factors one at time. Yet, we can get additional insights by employing design of experiments (DOE). Methodology: For making promises, we compare a First-Come First-Served approach to an approach using nested booking limits and giving advantage to profitable customers and attractive periods. Considering various sequences of order arrival, we generate Kriging metamodels that best describe the nonlinear relationships between the simulation responses and system factors for Canadian softwood lumber firms. We employ a Latin hypercube design to take into account different environmental scenarios. Results: Our analysis reveals the potential to improve the performance of the demand management process if we know high-priority customers needs before fulfilling less-priority orders and if we use nested booking limits concept.

Objective This research aims to develop a basic understanding of a demand management process integrating sales and operations planning (S&OP) and order promising in a Make-To-Stock environment and to compare different demand management policies with limited capacity. Contribution Typical researches about demand management processes analyze few system specifications or vary few potential factors one at a time. Yet, additional insights can be obtained by employing a space-filling design and Kriging metamodeling for analysis. Methodology We compare two configurations of the integrated demand management process. While the First-Come First-Served concept is used at the order promising level for the first configuration, the second configuration uses nested booking limits and gives advantage to profitable customers and attractive periods. Considering various order arrival sequences, we generate Kriging metamodels that best describe the nonlinear relationships between four environmental factors (demand intensity, demand forecast error, customer heterogeneity and coefficient of variation) and three performance measures (yearly profit margin, yearly sales and high-priority fill rate) for Canadian softwood lumber firms. Since our simulation experiments are time-consuming, we employ a Latin hypercube design to efficiently take into account different market situations. Results Our analysis reveals the potential to improve the performance of the demand management process if we know high-priority customers needs before fulfilling low-priority orders and if we use nested booking limits concept.

The significant contribution of raw materials inbound logistics to the reduction of the procurement cost have motivated several recent studies, particularly in the forestry sector. New routing and truck scheduling techniques have been proposed to plan in advance the arrival of the trucks to the mill’s gates. Yet, little attention has been given to the trucks rece ption, unloading and internal movements until exiting the facilities. Consequently, congestion and queuing of the trucks at the mill gate often occur, increasing the duration and costs of the transportation services. This paper introduces the Raw Materials Reception Problem (RMRP) and presents an innovative solution approach anchored on Revenue Management principles often used in for example airline and hotel booking systems. The solution builds on segmenting carriers/trucks and their dynamic assignment to the time slots available at the unloading docks, awarding on -time arrivals. Direct unload at the production lines is further preferred. It avoids, whenever is possible, the passage in the intermediate stockyard, therefore leading to a reduction of the mate rial handling cost. Results of the application of this solution approach to the wood supply of a Portuguese pulp mill are presented. The case study encompasses 120 pulpwood daily deliveries on 6 possible unloading docks inside the mill. The proposed approa ch led to a reduction of 55% on the daily raw material reception cost when compared to the FIFO approach used today. The maximum waiting time for each truck was also reduced, particularly for the trucks on higher priority segments. This approach was implem ented in the Wood Delivery System prototype and then used for conducting a trade -off analysis that provided valuable information to support decision -making in respect to the design of the reception process.

At present, innovation together with technology are resources that generate great benefits to the service industry. However, nowadays there are still accommodation establishments that opt ​​for procedures without looking at new horizons that can positively influence your Business Growth. For this reason, this intervention project is based on the study bibliography of the theory of Revenue Management (RM) within the hotel sector, as an indispensable technique for the management of both internal and external processes. external to a lodging establishment. Similarly, they were taken into has a series of characteristics that hotels must meet for their correct application and in this way achieve the objective of executing a successful study. the RM is complex and takes into account different variables and indicators that aim to focus on the analysis of its four fundamental pillars: demand, supply, price and competence in a certain time, from the results the shortcomings and a new approach aimed at helping in the decision-making process is initiated. of decisions. In addition, the work proposes strategies to be implemented in hotels Santa Lucía and Forum with the aim of contributing to their development in the management of their income and services, for the creation of the strategies studies of field in order to collect both qualitative and quantitative data, so that it is possible to know and analyze the situation of the hotels. Similarly, through from the calculation of the RM ratios it was possible to measure the performance of the establishments and based on the results obtained suggest real and achievable strategies that are aimed at maximizing revenue. Keywords: Revenue Management, Hotels, Management, Strategies, Cuenca, Hotel Santa Lucia, Hotel Forum

Forecasting demand is one of the fundamental components of a successful revenue management system in hospitality. The industry requires understandable models that contribute to adaptability by a revenue management department to make data-driven decisions. Data analysis proves an essential role for the time until the check-in date, which differs per day of week. This paper aims to provide a new model, which is inspired by cubic smoothing splines, resulting in smooth demand curves per rate class over time until the check-in date. This model regulates the error between data points and a smooth curve, and therefore able to capture natural guest behavior. The result is obtained by solving a linear programming model, which enables the incorporation of industry knowledge in the form of constrains. The performance is expressed in Weighted Absolute Percentage Error (WAPE) and revenue. This model claims to generate 13.3% more revenue for 2019.

In this paper, an optimisation method is introduced that accounts for cancellations. We do so by estimating the opportunity cost of a booking between the time of booking and the expected time of cancellation. The formulation involves an estimate of the value of the state of the system at the time of cancellation (which is in the future), found through novel heuristics we introduce. The fare that is used to determine whether a product is available for sale, is adjusted by the risk the airline faces. We introduce an example which shows that there may be cases where it is optimal to reject a higher-priced product if the risk of cancellation is high, while accepting a lower-priced product. Simulations show increases in revenues against a traditional formulations that does not explicitly models cancellations. We show our method is robust against choice of heuristic, misjudgement of cancellation probability and forecasting errors.

In the airline industry revenue and inventory is managed tightly, with the intent to sell each seat at the right price to the right customer at the right time. This does not always go to plan and seats remain unsold, or demand can be greater than what is available. The question then becomes how can capacity and revenue be managed more efficiently? How can capacity and revenue be maximized to ensure that seat spoilage is minimized. This thesis takes a look at this problem and aims to solve this problem by dynamically allocating capacity as well as becoming paired with revenue management processes. By being able to move capacity around to different flights, it is believed that revenue can be maximized and that seat spoilage can be minimized, creating a more effective use of a perishable product. The goal of this thesis is to show that increases are possible, even if by small margins. This thesis looks to use real-world data to help support its findings along with a random distribution to pair alongside a Monte Carlo simulation to help ensure accuracy. In this data driven methodological approach revenue management and a capacity swapping algorithm are integrated. The revenue management module creates a series of seat inventory allocations as well as demand forecasting. This information is then fed into a simulation which will then simulate the entire booking life- cycle of a fight. Here each flight will build its passenger load and will swap aircraft with other aircraft of varying capacities when the algorithm has determined it is necessary and would create a positive increase in revenue and passenger counts. To assist in this approach a further random forest model is developed to help make informed decisions. These changes are further evaluated by the algorithm to ensure that every change that was made was beneficial and if so will accept the swap and continue to build loads as this new flight. Different case studies were developed that introduced different timing sce, Aerospace Engineering

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges.

In Revenue Management (RM) systems, information censoring and the interaction between the forecasting and optimization stages, increases the costs and complexity of performance analysis using historical data. An affordable and suitable alternative is using simulations, but appropriate behavioral models must be considered. In the following document, we discuss and test the implementation of a dynamic air transport market simulator, designed to analyze RM systems. The simulator replicates the behavior of passengers that book seats offered in multiple flights by different airlines. We use discrete choice models to replicate the demand behavior, accounting for preferences and decision rule heterogeneity, and including a temporal evolution of the preference throughout the selling horizon. To replicate the supply behavior, a number of airlines modify the price and quantity of different fare classes offered in each flight, using a variety of RM forecasting, un-constraining, and optimization techniques. The simulator allows analysts to study the economic benefit of RM systems under predefined assumptions in an artificial and controlled environment. This increases the benefits obtained by the correct selection of context-appropriate RM systems and the likelihood of successfully implementing new and complex systems. We test and showcase the simulator performance, studying the entrance of a new airline in a competitive context. We generate, implement and evaluate different RM strategies in response to the introduction of new competition, and discuss the results, highlighting the interpretability and accuracy of the proposed framework., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Air Transport & Operations

Immer mehr produzierende Unternehmen beginnen die Produktion erst mit der Annahme eines Auftrags und nutzen somit das Make-to-Order Prinzip. Durch die limitierte Produktionskapazität kann jedoch nicht jeder Auftrag angenommen werden. Bei der Auftragsannahme werden die in der Regel konkurrierenden Ziele, den Profit zu maximieren und insbesondere den wichtigsten Kunden einen guten Service zu bieten, verfolgt. In dieser Dissertation werden unter Berücksichtigung beider Ziele verschiedene Revenue Management Ansätze für die Auftragsannahme entwickelt und analysiert. Es kann gezeigt werden, dass die entwickelten Revenue Management Strategien erfolgreich funktionieren und in der Lage sind, einen guten Kompromiss zwischen den konkurrierenden Zielen herzustellen., More and more manufacturing companies start production only when an order has been accepted and hence, make use of the make-to-order principle. However, due to limited production capacity, not every order can be accepted. The order acceptance should pursue the usually competing objectives of maximizing profit and, in particular, providing good service to the most important customers. In this dissertation, different revenue management approaches for the order acceptance are developed and analyzed considering both objectives. It can be shown that the developed revenue management strategies work successfully and are able to establish a good trade-off between the competing objectives.

At present, innovation together with technology are resources that generate great benefits to the service industry. However, nowadays there are still accommodation establishments that opt ​​for procedures without looking at new horizons that can positively influence your Business Growth. For this reason, this intervention project is based on the study bibliography of the theory of Revenue Management (RM) within the hotel sector, as an indispensable technique for the management of both internal and external processes. external to a lodging establishment. Similarly, they were taken into has a series of characteristics that hotels must meet for their correct application and in this way achieve the objective of executing a successful study. the RM is complex and takes into account different variables and indicators that aim to focus on the analysis of its four fundamental pillars: demand, supply, price and competence in a certain time, from the results the shortcomings and a new approach aimed at helping in the decision-making process is initiated. of decisions. In addition, the work proposes strategies to be implemented in hotels Santa Lucía and Forum with the aim of contributing to their development in the management of their income and services, for the creation of the strategies studies of field in order to collect both qualitative and quantitative data, so that it is possible to know and analyze the situation of the hotels. Similarly, through from the calculation of the RM ratios it was possible to measure the performance of the establishments and based on the results obtained suggest real and achievable strategies that are aimed at maximizing revenue. Keywords: Revenue Management, Hotels, Management, Strategies, Cuenca, Hotel Santa Lucia, Hotel Forum

At present, innovation together with technology are resources that generate great benefits to the service industry. However, nowadays there are still accommodation establishments that opt ​​for procedures without looking at new horizons that can positively influence your Business Growth. For this reason, this intervention project is based on the study bibliography of the theory of Revenue Management (RM) within the hotel sector, as an indispensable technique for the management of both internal and external processes. external to a lodging establishment. Similarly, they were taken into has a series of characteristics that hotels must meet for their correct application and in this way achieve the objective of executing a successful study. the RM is complex and takes into account different variables and indicators that aim to focus on the analysis of its four fundamental pillars: demand, supply, price and competence in a certain time, from the results the shortcomings and a new approach aimed at helping in the decision-making process is initiated. of decisions. In addition, the work proposes strategies to be implemented in hotels Santa Lucía and Forum with the aim of contributing to their development in the management of their income and services, for the creation of the strategies studies of field in order to collect both qualitative and quantitative data, so that it is possible to know and analyze the situation of the hotels. Similarly, through from the calculation of the RM ratios it was possible to measure the performance of the establishments and based on the results obtained suggest real and achievable strategies that are aimed at maximizing revenue. Keywords: Revenue Management, Hotels, Management, Strategies, Cuenca, Hotel Santa Lucia, Hotel Forum

Revenue management has been an excellent application of convex and non-convex optimization, which requires data analysis, problem formulation, model design, evaluation and testing afterwards. Customer choice modelling plays an important role in revenue management, for its broad perspective in retail industry. With the advance of technology, the data that can be utilized for data analysis, thus developing a feasible customer choice model is drawing attention from many companies. In this paper, the main topic is a real application of customer choice modeling to a new form of product. We’ll start the work from defining the problem, and then brief the preliminary work done around the data. And then, we’ll select a model and take approaches to analyze the data. Followed will be the evaluation and validation of the models. At last, we’ll conclude the model performance, develop a solution to the original revenue management problem, and propose possible future work for the project. Key words: data analysis, customers choice modelling, pricing strategy, logistic regression.

With the development of information technology, Internet service platforms become more and more important. Recommendation systems, among many others, play a vital role in generating revenue and competition for these platforms. This thesis investigates the revenue management problems in two recommendation systems. In the first part, we develop a variant of the multinomial logit model with impatient customers who make choice decisions in multiple stages. In each stage, if the maximum utility of viewed products is larger than a minimum acceptable one, the customer purchases right away. Otherwise, the customer stays till the last stage within her patience level and leaves without purchasing. We propose efficient algorithms for pure assortment/pricing, joint assortment and pricing problems. In the second part, we propose one innovative model for the live-streaming e-commerce. In reality, both Amazon and Alibaba have embraced live-streaming as a novel marketing tool. We use a mechanism design framework to demonstrate how the live-streaming platform can leverage information provision to improve its revenue compared to traditional position auctions. We show that the revenue loss from neglecting information provision can be arbitrarily large. The optimal mechanism problem is NP-hard and we develop several heuristics with constant performance bounds. We attribute the effectiveness of information provision to its discriminatory role in serving heterogeneous advertisers.

With the development of information technology, Internet service platforms become more and more important. Recommendation systems, among many others, play a vital role in generating revenue and competition for these platforms. This thesis investigates the revenue management problems in two recommendation systems. In the first part, we develop a variant of the multinomial logit model with impatient customers who make choice decisions in multiple stages. In each stage, if the maximum utility of viewed products is larger than a minimum acceptable one, the customer purchases right away. Otherwise, the customer stays till the last stage within her patience level and leaves without purchasing. We propose efficient algorithms for pure assortment/pricing, joint assortment and pricing problems. In the second part, we propose one innovative model for the live-streaming e-commerce. In reality, both Amazon and Alibaba have embraced live-streaming as a novel marketing tool. We use a mechanism design framework to demonstrate how the live-streaming platform can leverage information provision to improve its revenue compared to traditional position auctions. We show that the revenue loss from neglecting information provision can be arbitrarily large. The optimal mechanism problem is NP-hard and we develop several heuristics with constant performance bounds. We attribute the effectiveness of information provision to its discriminatory role in serving heterogeneous advertisers.

Revenue management has been an excellent application of convex and non-convex optimization, which requires data analysis, problem formulation, model design, evaluation and testing afterwards. Customer choice modelling plays an important role in revenue management, for its broad perspective in retail industry. With the advance of technology, the data that can be utilized for data analysis, thus developing a feasible customer choice model is drawing attention from many companies. In this paper, the main topic is a real application of customer choice modeling to a new form of product. We’ll start the work from defining the problem, and then brief the preliminary work done around the data. And then, we’ll select a model and take approaches to analyze the data. Followed will be the evaluation and validation of the models. At last, we’ll conclude the model performance, develop a solution to the original revenue management problem, and propose possible future work for the project. Key words: data analysis, customers choice modelling, pricing strategy, logistic regression.

La evolución del turismo en el mundo, así como en Cuba, ha traído consigo la puesta en marcha de diversas herramientas de optimización de ingresos entre las que destaca el Revenue Management. Sus notables resultados en la industria aérea y hotelera constituyen un atractivo para su explotación en las agencias de viajes. La investigación que se presenta tiene como objetivo aplicar la herramienta de Revenue Management en la Agencia de Viajes Cubanacán Sucursal Matanzas, a partir del diseño de un procedimiento de Revenue Management para agencias de viajes en Cuba. El trabajo investigativo se sustenta en el empleo de técnicas y herramientas como, matriz DAFO, Brainstorming, matriz de posicionamiento, método de experto y Kendall, teniendo a la dialéctica materialista como método general de conocimiento y base metodológica. Con la aplicación del procedimiento propuesto se obtuvo como resultado la identificación de las principales amenazas, fortalezas, debilidades y oportunidades, determinación de competidores potenciales y cálculo del índice integral de Revenue Management, todo ello permitió conocer la salud financiera de la entidad. Sin dudas, constituye el cimiento sobre el cual se proyectarán importantes análisis desde la óptica del Revenue Management en esta y otras empresas del sector, constituyendo un punto de partida en el estudio de esta rama en agencias de viajes en Cuba.

We consider a network revenue management model with derivative products that share properties of flexible and callable products. A derivative product gives the seller the option to recall a sold unit and replace it with an alternative from a pre-specified set – this set can include substitute products and/or cash compensation. We model demand by a Nested Logit model where each derivative product and its associated specific product are included in the same nest because they are correlated. We propose a heuristic control policy and present computational experiments that show that it is near-optimal and significantly increase expected revenue relative to offering only specific products, even with a high penalty for reassignment and an early exercise constraint.

With the development of information technology, Internet service platforms become more and more important. Recommendation systems, among many others, play a vital role in generating revenue and competition for these platforms. This thesis investigates the revenue management problems in two recommendation systems. In the first part, we develop a variant of the multinomial logit model with impatient customers who make choice decisions in multiple stages. In each stage, if the maximum utility of viewed products is larger than a minimum acceptable one, the customer purchases right away. Otherwise, the customer stays till the last stage within her patience level and leaves without purchasing. We propose efficient algorithms for pure assortment/pricing, joint assortment and pricing problems. In the second part, we propose one innovative model for the live-streaming e-commerce. In reality, both Amazon and Alibaba have embraced live-streaming as a novel marketing tool. We use a mechanism design framework to demonstrate how the live-streaming platform can leverage information provision to improve its revenue compared to traditional position auctions. We show that the revenue loss from neglecting information provision can be arbitrarily large. The optimal mechanism problem is NP-hard and we develop several heuristics with constant performance bounds. We attribute the effectiveness of information provision to its discriminatory role in serving heterogeneous advertisers.

Revenue management has been an excellent application of convex and non-convex optimization, which requires data analysis, problem formulation, model design, evaluation and testing afterwards. Customer choice modelling plays an important role in revenue management, for its broad perspective in retail industry. With the advance of technology, the data that can be utilized for data analysis, thus developing a feasible customer choice model is drawing attention from many companies. In this paper, the main topic is a real application of customer choice modeling to a new form of product. We’ll start the work from defining the problem, and then brief the preliminary work done around the data. And then, we’ll select a model and take approaches to analyze the data. Followed will be the evaluation and validation of the models. At last, we’ll conclude the model performance, develop a solution to the original revenue management problem, and propose possible future work for the project. Key words: data analysis, customers choice modelling, pricing strategy, logistic regression.

Financial sustainability of water and sewerage provision is undermined by consumers’ inability and reluctance to pay service fees. South African municipalities are providing water and sewerage services at high maintenance and operational costs, households and businesses that are unable and/or committing to pay their full accounts timeously. This study examines the provision of water and sewage service and presents strategic framework to manage revenue collection to achieve financial sustainability of water and sewerage provision. The study applied the interpretive research method to collect and analyzed data for improved understanding of service provision and revenue collection mantra. The main objective of this study was to investigate factors affecting financial sustainability of water and sewerage provision in Cape Town. In this study an interpretive research method was embraced and qualitative research design was used to collect qualitative data by means of interviewing the senior officials using semi structured interviews. Narrative methods of analysis were applied in analyzing data collected from randomly selected Cape Town officials including observations and secondary data. The results showed that although Cape Town is making efforts of generating revenue by means of enforcing water and sanitation services payments, there is still a percentage of residents that refuses to pay for services resulting in revenue loss. The other reason for loss of revenue was unaccounted water which was the focus of Water and Sanitation. The reasons advanced for unwillingness to pay for water and sanitation services by consumers were unemployment, poverty, unequal distribution of services and general ignorance.

This research analyzes the effects of advance booking and channel type on hotel rates. While this relationship has been addressed in the literature, most studies take a partial approach by focusing only on one distribution channel or one destination. This study fills this gap by analyzing the price dynamics for four channels and multiple destinations. The data set consists of 39,363 bookings for 1085 hotels over 27 consecutive months. We used two-stage least squares to solve potential endogeneity issues, and the results proved that distribution channel, hotel type and hotel size have an influence on the effect of advance booking on hotel rates. Critical managerial implications are discussed.

TravelBoom, a data-driven marketing solutions company, has compiled a list of helpful tips to create a successful Black Friday/Cyber Monday sales strategy for a hotel. The company provides compelling data, urging hotels to take the post-holiday shopping frenzy as an opportunity to capitalize on sales that can outshine any other sales day for the year.

This article discusses how Revenue managers have collaborated with operations teams during the pandemic. This was done to help revenue managers have a better grasp on what was feasible for the operations teams in real time. Additionally, this collaboration helped operations teams to start thinking more strategically about how to maximize the rooms available, labor, and other resources that are currently available. The article also offers “creative” solutions to managing through the pandemic season.

Структура магистерской диссертации включает в себя введение, три главы, заключение, список использованных источников и приложения. В первой главе рассмотрены теоретические основы, государственное регулирование систем управления доходностью предприятий гражданской авиации. Во второй главе проведен анализ текущего состояния систем управления доходностью, проведен анализ управления доходностью авиакомпании на уровне рейсов. В третьей главе разработан комплекс мероприятий по повышению доходности авиакомпании и дано их экономическое обоснование., The structure of the master's thesis includes an introduction, three chapters, conclusion, list of references and appendices. The first chapter discusses the theoretical foundations, state regulation of civil aviation profitability management systems. The second chapter analyzes the current state of profitability management systems and analyzes the airline’s profitability management at the flight level. In the third chapter, a set of measures to increase the profitability of the airline is developed and their economic rationale is given.

The online market enables hotels to enhance their visibility and drive up their revenue. This study analysed both the average room price and price count (i.e. the sum of the number of prices that hotels offer) on Booking.com for a period of 300 days prior to check-in, with data classified by official hotel category. Hotels’ number of rooms, day of the week, room type, room capacity (i.e. maximum number of guests per room) and length of stay were also tracked. This research was based on a stratified sample of hotels gathered by using random sampling and proportional allocation, as well as defining the strata by hotel categories. The dataset included 1,353,751 records. The results reveal that channel management activities are an important area of hotels’ operations, which generate a considerable workload in terms of the time devoted to updating data and other related tasks. However, hotels’ participation in online channels does not always match their importance in the market as measured by their relative number of rooms. Most of the variables under study have a significant positive impact on prices, except for hotels’ number of rooms, which failed to follow any discernible pattern of influence.

Este trabajo se centra en el estudio de la oferta de precios a través de canales online (Booking.com) en un destino turístico. Los hoteles se han seleccionado mediante muestreo aleatorio simple estratificado con afijación proporcional. Se trabaja con un conjunto de datos de grandes dimensiones (big data, con un total de 1.353.751 registros). Se considera una antelación de estancia de aproximadamente un año, lo que supone una gran novedad respecto a estudios previos. En el trabajo se presentan los valores de precios considerando los datos habitualmente publicados por los hoteles en estos canales, lo que permite ofrecer una panorámica de la operativa relativa a la oferta de precios a través de la distribución online a nivel de destino turístico.