Unobscuring Algorithm for Airline Revenue Optimization
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Solution Overview
Problem
The airline industry faces challenges in maximizing revenue due to passengers booking seats at lower prices than they are willing to pay, leading to foregone revenue, and existing demand forecasting methods are inaccurate and fail to account for interdependence between fare classes.
Innovation Solution
A method and system that utilize an unobscuring algorithm to determine monetary stimulation values and dilution penalties for fare classes, allowing airlines to adjust fares and optimize revenue by considering the willingness to pay of passengers and interclass demand relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If demand forecasting methods assume independence between fare classes, then forecasting complexity is reduced, but forecasting accuracy deteriorates due to failure to account for interdependence between fare classes
Solution Approach 1:
The patent introduces an unobscuring algorithm as an intermediary computational process that processes booking data and fare class information to generate corrected demand forecasts. This algorithm acts as a mediator between raw booking data and final forecasts, accounting for interclass demand relationships and buy-down behavior without requiring complex manual analysis.
Solution Approach 2:
The patent replaces traditional mechanical forecasting methods (manual analysis, simple statistical models) with an automated computational algorithm that processes booking data, calculates unobscured demand, and generates forecasts. This substitution of mechanical processes with automated computing systems resolves the contradiction by handling complexity computationally rather than procedurally.
2Ease of operation
If passengers are allowed to book at lower fare classes, then ease of booking is improved, but revenue loss increases due to passengers paying less than their willingness to pay
Solution Approach 1:
The patent implements dynamic fare class management where the availability and pricing of fare classes are adjusted in real-time based on current booking patterns, demand forecasts, and revenue optimization criteria. This dynamic approach allows the system to respond to changing conditions, opening or closing fare classes strategically to maximize revenue while maintaining booking flexibility.
Solution Approach 2:
The patent changes key parameters including fare prices, fare class availability, and booking restrictions based on computed demand forecasts and revenue optimization algorithms. By dynamically adjusting these parameters rather than maintaining fixed pricing structures, the system can capture higher willingness-to-pay from passengers while still providing booking options at lower fares when appropriate.
3Loss of energy
If fare classes are closed to protect revenue, then revenue preservation is improved, but seat utilization deteriorates due to empty seats that could have been sold
Solution Approach 1:
The patent performs preliminary demand forecasting and revenue analysis before making fare class closure decisions. By computing unobscured demand and evaluating the potential impact of closing fare classes in advance, the system can make informed decisions about when to close or open fare classes, balancing revenue protection with seat utilization optimization.
Solution Approach 2:
The patent implements a feedback mechanism where actual booking data and revenue outcomes are continuously monitored and fed back into the demand forecasting model. This feedback loop allows the system to learn from past decisions, refine its predictions of buy-down behavior and interclass demand relationships, and improve future fare class management decisions to better balance revenue preservation and seat utilization.
Data Source
AI summary
The system uses an unobscuring algorithm to determine dilution values and stimulation values. The system includes analysis methods and tools suitable for use in connection with yield management systems, inventory control systems, revenue management systems, and the like.