Unconstraining methods for revenue management systems under small demand

Kourentzes, Nikolaos and Li, Dong and Strauss, Arne (2019) Unconstraining methods for revenue management systems under small demand. Journal of Revenue and Pricing Management, 18 (1). pp. 27-41. ISSN 1476-6930

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Sales 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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Revenue and Pricing Management
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1057/s41272-017-0117-x
Uncontrolled Keywords:
?? demand unconstrainingforecasting small demand revenue management financeeconomics and econometricsbusiness and international managementstrategy and management ??
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Deposited On:
06 Oct 2017 19:38
Last Modified:
10 May 2024 01:34