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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Unconstraining methods for revenue management systems under small demand
AU - Kourentzes, Nikolaos
AU - Li, Dong
AU - Strauss, Arne
N1 - The final publication is available at Springer via http://dx.doi.org/10.1057/s41272-017-0117-x
PY - 2019/2/14
Y1 - 2019/2/14
N2 - 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.
AB - 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.
KW - Demand unconstraining
KW - Forecasting
KW - Small demand
KW - Revenue management
U2 - 10.1057/s41272-017-0117-x
DO - 10.1057/s41272-017-0117-x
M3 - Journal article
VL - 18
SP - 27
EP - 41
JO - Journal of Revenue and Pricing Management
JF - Journal of Revenue and Pricing Management
SN - 1476-6930
IS - 1
ER -