Chess game on an airplane wing symbolizing airline revenue management

Airline Revenue Management: Are 'Perfectly Tuned' Networks Really Untouchable?

Rhea Morgan in Business & Economy August 2025 • 4 min read.

"Explore the surprising limitations of intelligent aggressiveness in airline revenue management and what it means for the future of competition."

In the high-stakes world of airline revenue management, the quest for maximizing profits is relentless. For years, airlines have employed various 'intelligent aggressiveness' levers—forecast multipliers, aggressive unconstrainers, hybrid forecasting, and strategic fare adjustments—to gain a competitive edge. The underlying concept? To nimbly outmaneuver competitors in the battle for bookings.

But what happens when everyone's playing the same game, and playing it well? Imagine an airline industry where all competitors are 'perfectly' aggressive, each leveraging sophisticated revenue management techniques to their fullest extent. In such a hyper-competitive environment, can individual airlines still pull ahead using these established tactics?

A new study, using the Passenger Origin-Destination Simulator (PODS) network, investigates this very question. By simulating a large international network with multiple airlines competing for passengers, the research uncovers surprising limitations to the conventional wisdom of revenue optimization. Keep reading to discover the future and limitations of revenue management.

The Quest for Revenue: Do Traditional Methods Still Work?

Chess game on an airplane wing symbolizing airline revenue management

The study, leveraging the PODS consortium's research activities and the state-of-the-art PODS simulator, examined whether established revenue management techniques could still generate revenue increases for a single airline when all competitors are equally aggressive. The PODS simulator, widely recognized for its customer choice modeling capabilities, has been instrumental in numerous revenue management studies since the late 1990s.

In the simulation, a 63-day booking curve was divided into 16 time frames, each airline running its forecasting and optimization models at the close of each time frame to update inventory controls. This setup mirrors real-world airline practices, where time frames are longer at the beginning of the booking curve and shorten as the departure date approaches.

Hybrid Forecasting (HF): Segments demand between product-oriented customers (traditional RM techniques apply) and price-oriented customers (forecasts account for potential sell-up to higher fare classes).
Forecast Multipliers (FM): Adjust demand forecasts to increase or decrease seat protection for higher fare passengers based on booking trends.
Dynamic User Influence (UI): Simulates manual overrides of RM systems by analysts, increasing forecasts when flights book above average and decreasing them when booking below average.
Fare Adjustment (FA): Modifies fares based on the concept of marginal revenue, accounting for the dilutionary impact of lower-priced products.

The study used the PODS network U10, representing a large international network with 572 origin-destination markets served by four competing airlines, including a low-cost carrier. These airlines offer three basic products: a domestic, fully restricted fare structure; a domestic, semi-differentiated fare structure; and an international, restricted fare structure. Restrictions such as minimum stay requirements, change fees, and non-refundability are applied to lower fare classes to differentiate offerings. The load factor (LF) is 83.1 per cent.

Key Findings and Future Directions

The research reveals a crucial insight: the revenue gains typically attributed to intelligent aggressiveness are largely a result of airlines operating in imperfectly tuned networks with imperfect competitors. In essence, the advantage comes from being smarter than the competition, not from the inherent effectiveness of the tactics themselves.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1057/s41272-017-0098-9, Alternate LINK

Title: Do Hybrid Forecasting And Forecast Multipliers Still Work In A “Perfectly Tuned” Pods International Network With Four Competing Airlines?

Subject: Strategy and Management

Journal: Journal of Revenue and Pricing Management

Publisher: Springer Science and Business Media LLC

Authors: Larry Weatherford

Published: 2017-06-07

Everything You Need To Know

What is the primary focus of the study using the Passenger Origin-Destination Simulator (PODS) network?

The study, using the Passenger Origin-Destination Simulator (PODS) network, focuses on determining whether established revenue management techniques can still generate revenue increases for a single airline when all competitors are equally aggressive. The research leverages the PODS simulator to analyze how tactics like Hybrid Forecasting, Forecast Multipliers, Dynamic User Influence, and Fare Adjustment perform in a hyper-competitive environment where everyone is using similar strategies.

How does the PODS network U10 represent the airline industry and what are the key characteristics used in the simulations?

The PODS network U10 represents a large international network with 572 origin-destination markets served by four competing airlines, including a low-cost carrier. The simulations include three basic product offerings: a domestic, fully restricted fare structure; a domestic, semi-differentiated fare structure; and an international, restricted fare structure. The study also incorporates restrictions like minimum stay requirements, change fees, and non-refundability applied to lower fare classes, and it operates with a load factor (LF) of 83.1 percent.

What are the main revenue management techniques examined in the study and how do they function?

The study investigates four key revenue management techniques. Hybrid Forecasting (HF) segments demand between product-oriented and price-oriented customers. Forecast Multipliers (FM) adjust demand forecasts to influence seat protection. Dynamic User Influence (UI) simulates analyst overrides to increase or decrease forecasts based on booking trends. Fare Adjustment (FA) modifies fares based on marginal revenue, considering the impact of lower-priced products.

What are the limitations of 'intelligent aggressiveness' in airline revenue management, as revealed by the study?

The research indicates that the revenue gains from intelligent aggressiveness are largely due to operating in networks with less efficient competitors. The advantage comes from outsmarting the competition, not the inherent effectiveness of the tactics themselves. In a scenario where all airlines are equally optimized, the benefits of these techniques diminish, highlighting their limitations in a perfectly competitive environment.

Why is the PODS simulator a critical tool for this research, and what are its capabilities?

The PODS simulator is crucial because of its customer choice modeling capabilities, which have been instrumental in numerous revenue management studies. It allows researchers to simulate a large international network with multiple airlines competing for passengers, providing a realistic environment to test and evaluate the effectiveness of different revenue management strategies, such as Hybrid Forecasting, Forecast Multipliers, Dynamic User Influence, and Fare Adjustment, under various competitive scenarios.