Enhancing robustness to forecast errors in availability control for airline revenue management

Tiago Gonçalves () and Bernardo Almada-Lobo ()
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Tiago Gonçalves: INESC-TEC, Faculty of Engineering of the University of Porto
Bernardo Almada-Lobo: INESC-TEC, Faculty of Engineering of the University of Porto

Journal of Revenue and Pricing Management, 2024, vol. 23, issue 4, No 6, 346-354

Abstract: Abstract Traditional revenue management systems are built under the assumption of independent demand per fare. The fare adjustment theory is a methodology to adjust fares that allows for the continued use of optimization algorithms and seat inventory control methods, even with the shift toward dependent demand. Since accurate demand forecasts are a key input to this methodology, it is reasonable to assume that for a scenario with uncertainties it may deliver suboptimal performance. Particularly, during and after COVID-19, airlines faced striking challenges in demand forecasting. This study demonstrates, firstly, the theoretical dominance of the fare adjustment theory under perfect conditions. Secondly, it lacks robustness to forecast errors. A Monte Carlo simulation replicating a revenue management system under mild assumptions indicates that a forecast error of $$\pm 20\%$$ ± 20 % can potentially prompt a necessity to adjust the margin employed in the fare adjustment theory by $$-10\%$$ - 10 % . Moreover, a tree-based machine learning model highlights the forecast error as the predominant factor, with bias playing an even more pivotal role than variance. An out-of-sample study indicates that the predictive model steadily outperforms the fare adjustment theory.

Keywords: Revenue management; Fare adjustment theory; Forecast accuracy; Bid price; Dependent demand; Sell-up (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1057/s41272-024-00475-9

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