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Can demand forecast accuracy be linked to airline revenue?

Author

Listed:
  • Thomas Fiig

    (Amadeus IT Group)

  • Larry R. Weatherford

    (University of Wyoming)

  • Michael D. Wittman

    (Amadeus IT Group)

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.

Suggested Citation

  • Thomas Fiig & Larry R. Weatherford & Michael D. Wittman, 2019. "Can demand forecast accuracy be linked to airline revenue?," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(4), pages 291-305, August.
  • Handle: RePEc:pal:jorapm:v:18:y:2019:i:4:d:10.1057_s41272-018-00174-2
    DOI: 10.1057/s41272-018-00174-2
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    References listed on IDEAS

    as
    1. Weatherford, Larry R. & Kimes, Sheryl E., 2003. "A comparison of forecasting methods for hotel revenue management," International Journal of Forecasting, Elsevier, vol. 19(3), pages 401-415.
    2. Peter P Belobaba, 2016. "Optimization models in RM systems: Optimality versus revenue gains," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 229-235, July.
    3. L R Weatherford & P P Belobaba, 2002. "Revenue impacts of fare input and demand forecast accuracy in airline yield management," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(8), pages 811-821, August.
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    Cited by:

    1. Giovanni Gatti Pinheiro & Thomas Fiig & Michael D. Wittman & Michael Defoin-Platel & Riccardo D. Jadanza, 2022. "Demand change detection in airline revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 581-595, December.
    2. Resul Aydemir & Mehmet Melih Değirmenci & Abdullah Bilgin, 2023. "Estimation of passenger sell-up rates in airline revenue management by considering the effect of fare class availability," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(6), pages 501-513, December.
    3. Greta Laage & Emma Frejinger & Andrea Lodi & Guillaume Rabusseau, 2021. "Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?," Papers 2101.10249, arXiv.org, revised Jun 2021.

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