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Unconstraining methods for revenue management systems under small demand
journal contribution
posted on 2018-04-26, 12:55 authored by Nikolaos Kourentzes, Dong Li, Arne K. StraussSales data often only represents a part of the demand for a service product owing to constraints such as capacity or booking limits. Unconstraining methods are concerned with estimating the true demand from such constrained sales data. This paper addresses the frequently encountered situation of observing only a few sales events at the individual product level and proposes variants of small demand forecasting methods to be used for unconstraining. The usual procedure is to aggregate data; however, in that case we lose information on when restrictions were imposed or lifted within a given booking profile. Our proposed methods exploit this information and are able to approximate
convex, concave or homogeneous booking curves. Furthermore, they are numerically robust due to our proposed group-based parameter optimization. Empirical results on accuracy and revenue performance based on data from a major car rental company indicate revenue improvements over a
best practice benchmark by statistically significant 0.5%–1.4% in typical scenarios.
History
School
- Business and Economics
Department
- Business
Published in
Journal of Revenue and Pricing ManagementCitation
KOURENTZES, N., LI, D. and STRAUSS, A.K., 2018. Unconstraining methods for revenue management systems under small demand. Journal of Revenue and Pricing Management, 18 (1), pp.27–41.Publisher
Palgrave Macmillan © Macmillan Publishers LtdVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2018Notes
This is a post-peer-review, pre-copyedit version of an article published in Journal of Revenue and Pricing Management. The definitive publisher-authenticated version KOURENTZES, N., LI, D. and STRAUSS, A.K., 2018. Unconstraining methods for revenue management systems under small demand. Journal of Revenue and Pricing Management, 18 (1), pp.27–41 is available online at: https://doi.org/10.1057/s41272-017-0117-x.ISSN
1476-6930eISSN
1477-657XPublisher version
Language
- en