Demand change detection in airline revenue management

Giovanni Gatti Pinheiro, Thomas Fiig, Michael D. Wittman (), Michael Defoin-Platel and Riccardo D. Jadanza
Additional contact information
Giovanni Gatti Pinheiro: Amadeus S.A.S.
Thomas Fiig: Amadeus IT Group
Michael D. Wittman: Amadeus IT Group
Michael Defoin-Platel: Amadeus S.A.S.
Riccardo D. Jadanza: Enerbrain SRL

Journal of Revenue and Pricing Management, 2022, vol. 21, issue 6, No 2, 595 pages

Abstract: 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.

Keywords: Change point detection; Demand forecast error; Airline revenue management; Demand shock; Forecasting; Markov decision process (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1057/s41272-022-00385-8

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