Can demand forecast accuracy be linked to airline revenue
01 Aug 2019-Journal of Revenue and Pricing Management (Palgrave Macmillan UK)-Vol. 18, Iss: 4, pp 291-305
TL;DR: In this paper, a conditional demand forecast error metric is proposed to compare demand forecasts to historical bookings conditional on the set of fare classes that were open at the time of booking.
Abstract: Since accurate demand forecasts are a key input to any airline revenue management system, it is reasonable to assume that an improvement in demand forecast accuracy would lead to increased revenues. However, this relationship has often been called into question. Past work has not conclusively proven that more accurate demand forecasts lead to higher revenue, causing researchers and practitioners to debate whether the concept of demand forecast accuracy itself is “myth or reality.” In this paper, we demonstrate that it is possible to consistently link demand forecast accuracy to airline revenue. After discussing why traditional demand forecast error metrics have struggled to demonstrate this relationship, we evaluate a novel conditional demand forecast error metric which compares demand forecasts to historical bookings conditional on the set of fare classes that were open at the time of booking. We prove under some mild assumptions that minimizing conditional demand forecast error will maximize revenue under any fare structure and customer choice behavior. These theoretical findings are supported by simulations in both a simple, single-leg model and in a complex multiple-airline network in the Passenger Origin–Destination Simulator. We find that price elasticity parameter bias of ± 10% can reduce revenues by up to about 1%, while price elasticity parameter bias of ± 20% can reduce revenues by up to 4%. We close by discussing the implications of the findings for revenue management practitioners.
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TL;DR: In this paper, the authors compared the accuracy of a set of 22 machine learning models for short-term hotel demand forecasting for lead times up to 14 days ahead and compared with methods ranging from se...
Abstract: This paper compares the accuracy of a set of 22 methods for short-term hotel demand forecasting for lead times up to 14 days ahead. Machine learning models are compared with methods ranging from se...
11 citations
TL;DR: In this article, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand.
Abstract: The unconstrained demand forecast for car rentals has become a difficult problem for revenue management due to the need to cope with a variety of rental vehicles, the strong subjective desires and requests of customers, and the high probability of upgrading and downgrading circumstances. The unconstrained demand forecast mainly includes repairing of constrained historical demand and forecasting of future demand. In this work, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand. In addition, the linear Holt–Winters model and the nonlinear backpropagation neural network are combined to predict future demand and avoid excessive errors caused by a single method. In a case study, we take advantage of a stated preference and a revealed preference survey and use the variable precision rough set to obtain factors and weights that affect customer choices. In this case study and based on a numerical example, three forecasting methods are compared to determine the car rental demand of the next time cycle. The comparison with real demand verifies the feasibility and effectiveness of the hybrid forecasting model with a resulting average error of only 3.06%.
2 citations
Posted Content•
TL;DR: In this article, the authors propose a two-step methodology to solve the periodic demand estimation problem in large-scale tactical planning problems, where the first step is to solve a multilevel mathematical programming formulation whose solution is a periodic demand estimate that minimizes fixed costs and variable costs incurred by adapting the tactical plan at an operational level.
Abstract: Crucial to freight carriers is the tactical planning of the service network. The aim is to obtain a cyclic plan over a given tactical planning horizon that satisfies predicted demand at a minimum cost. A central input to the planning process is the periodic demand, that is, the demand expected to repeat in every period in the planning horizon. We focus on large-scale tactical planning problems that require deterministic models for computational tractability. The problem of estimating periodic demand in this setting broadly present in practice has hitherto been overlooked in the literature. We address this gap by formally introducing the periodic demand estimation problem and propose a two-step methodology: Based on time series forecasts obtained in the first step, we propose, in the second step, to solve a multilevel mathematical programming formulation whose solution is a periodic demand estimate that minimizes fixed costs, and variable costs incurred by adapting the tactical plan at an operational level. We report results in an extensive empirical study of a real large-scale application from the Canadian National Railway Company. We compare our periodic demand estimates to the approach commonly used in practice which simply consists in using the mean of the time series forecasts. The results clearly show the importance of the periodic demand estimation problem. Indeed, the planning costs exhibit an important variation over different periodic demand estimates, and using an estimate different from the mean forecast can lead to substantial cost reductions. For example, the costs associated with the period demand estimates based on forecasts were comparable to, or even better than those obtained using the mean of actual demand.
1 citations
TL;DR: In this article , the authors developed a science-based shock detection framework based on statistical hypothesis testing, which enables fast detection of demand shocks and can be expressed in analytical closed form and demonstrate that this expression is remarkably accurate even in complex environments.
Abstract: Demand shocks—unobservable, sudden changes in customer behavior—are a common source of forecast error in airline revenue management systems. The COVID-19 pandemic has been one example of a highly impactful macro-level shock that significantly affected demand patterns and required manual intervention from airline analysts. Smaller, micro-level shocks also frequently occur due to special events or changes in competition. Despite their importance, shock detection methods employed by airlines today are often quite rudimentary in practice. In this paper, we develop a science-based shock detection framework based on statistical hypothesis testing which enables fast detection of demand shocks. Under simplifying assumptions, we show how the properties of the shock detector can be expressed in analytical closed form and demonstrate that this expression is remarkably accurate even in more complex environments. Simulations are used to show how the shock detector can successfully be used to identify positive and negative shocks in both demand volume and willingness-to-pay. Finally, we discuss how the shock detector could be integrated into an airline revenue management system to allow for practical use by airline analysts.
1 citations
TL;DR: In this article , a reinforcement learning agent is used to find novel pricing policies that balance revenue maximization and demand model quality in a surprisingly effective way, generating more revenue over the long run than current practices.
Abstract: The accurate estimation of how future demand will react to prices is central to the optimization of pricing decisions. The systems responsible for demand prediction and pricing optimization are called revenue management (RM) systems, and, in the airline industry, they play an important role in the company’s profitability. As airlines’ current pricing decisions impact future knowledge of the demand behavior, the RM systems may have to compromise immediate revenue by efficiently performing price experiments with the expectation that the information gained about the demand behavior will lead to better future pricing decisions. This earning while learning (EWL) problem has captured the attention of both the industry and academia in recent years, resulting in many proposed solutions based on heuristic optimization. We take a different approach that does not depend on human-designed heuristics. We present the EWL problem to a reinforcement learning agent, and the agent’s goal is to maximize long-term revenue without explicitly considering the optimal way to perform price experimentation. The agent discovers through experience that “myopic” revenue-maximizing policies may lead to a decrease in the demand model quality (which it relies on to take decisions). We show that the agent finds novel pricing policies that balance revenue maximization and demand model quality in a surprisingly effective way, generating more revenue over the long run than current practices.
1 citations
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Abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
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TL;DR: In this article, the authors used data from Choice Hotels and Marriott Hotels to test a variety of forecasting methods and to determine the most accurate method to forecast the arrivals of tourists.
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TL;DR: In this article, the authors developed a theory for optimizing revenue through seat inventory control that can be applied in a variety of airline fare structures, including those with less restricted and fully undifferentiated fare products that have become more common in the recent past.
Abstract: This paper develops a theory for optimizing revenue through seat inventory control that can be applied in a variety of airline fare structures, including those with less restricted and fully undifferentiated fare products that have become more common in the recent past We describe an approach to transform the fares and the demand of a general discrete choice model to an equivalent independent demand model The transformation and resulting fare adjustment approach is valid for both static and dynamic optimization and extends to network revenue management applications This transformation allows the continued use of the optimization algorithms and seat inventory control mechanisms of traditional revenue management systems, developed more than two decades ago under the assumption of independent demands for fare classes
114 citations
TL;DR: In this paper, the authors examined six different methods of unconstraining bookings to demand and showed that the expectation maximisation and projection detruncation methods are the most robust and that, as the percentage of data constrained increases to 60-80 per cent, their estimate of the unconstrained mean increases by 20-80 percent over the naive unconstraints methods, which leads to less bias and more accurate forecasts.
Abstract: Accurate forecasts of passenger demand are the heart of a successful revenue management system. The forecasts are usually based on historical booking data. These bookings do not reflect historical demand in all cases because booking requests can be rejected due to capacity constraints or booking control limits. This paper examines six different methods of unconstraining bookings to demand. Simulation analysis of many different scenarios of historical booking data are used with different percentages of the data being constrained, using simulated data, to show that the expectation maximisation and projection detruncation methods are the most robust and that, as the percentage of data constrained increases to 60–80 per cent, their estimate of the unconstrained mean increases by 20–80 per cent over the naive unconstraining methods, which leads to less bias and more accurate forecasts. Finally, by means of actual booking data from a major US airline, it is shown that upgrading the unconstraining process can lead to revenue gains of 2–12 per cent.
92 citations