Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures

Victor Pimentel; Aysajan Eziz; Tim Baker

doi:doi:10.11648/j.mcs.20210601.12

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Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures

Victor Pimentel, Aysajan Eziz, Tim Baker

Published in Mathematics and Computer Science (Volume 6, Issue 1)

Received: 6 January 2021 Accepted: 20 January 2021 Published: 28 January 2021

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Abstract

Research in hotel revenue management system design has not paid much attention to the demand forecasting side of the system. And the research that has examined forecasting has tended to focus on the comparison of specific forecaster methodologies, as opposed to prioritizing how a total system should be parameterized: how far in the future should projections be, how much data to use to update each specific parameter, which measure of forecast error to use, and how long to freeze each parameter/forecast before updating. This paper fills this prioritization void by utilizing a full-functionality hotel reservation system simulation validated by the revenue management staff of a major hotel chain as the basis for running screening experiments on an exhaustive set of forecaster parameters with regards to their impact on bottom-line system performance (average nightly net revenue, where net revenue refers to total room rate receipts minus an overbooking per person penalty that estimates the discounted lost sales of future revenues). A screening experiment is run for each general type of operating environment (demand intensity, degree of market segment differentiation) that a property might face. We find that only two parameters are significant: the final combined forecast horizon length and how long that final forecast is frozen before updating. We find that these two factors interact in a negative fashion to influence net revenue.

Published in	Mathematics and Computer Science (Volume 6, Issue 1)
DOI	10.11648/j.mcs.20210601.12
Page(s)	8-15
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Forecasting, Simulation, Capacity Analysis, Statistics

References

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[5]	Duffy (2015). Personal communication with Marriott’s corporate revenue management group (June 15, Bethesda, MD, USA). Kathleen Duffy is the contact.
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[7]	Weatherford, L., Kimes, S. (2003). A comparison of forecasting methods for hotel revenue management. International Journal of Forecasting, 19 (3), 401-415.
[8]	Weatherford, L. & Belobaba, P. (2002). Revenue impacts of fare input and demand forecast accuracy in airline yield management. Journal of the Operational Research Society, 53 (8), 811-821.
[9]	Weatherford, L, Kimes, S., Scott, D. (2001). Forecasting for hotel revenue management. Cornell Hotel and Restaurant Administration Quarterly, 42 (4), 53-64.
[10]	Azadeh, S., Marcotte, P., Savard, G. (2014). A taxonomy of demand uncensoring methods is revenue management. Journal of Revenue and Pricing Management, 13, 1-17.
[11]	van Ryzin, G., Vulcano, G. (2017). Technical note—an expectation-maximization method to estimate a rank-based choice model of demand. Operations Research, 65 (2), 396-407.
[12]	Yuksel, S. (2007). An integrated forecasting approach to hotel demand. Mathematical and Computer Modeling, 46 (7), 1063-1070.
[13]	Huang, Y., Ge, Y., Zhang, X., Xu, Y. (2013). Overbooking for parallel flights with transference. International Journal of Production Economics, 144 (2), 582-589.
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[17]	Pimentel, V., Aizezikali, A., Baker T. (2016). The Impact of Simultaneous Overbooking-Allocation on Explicit Price Setting Methods in Hotel Revenue Management. Working paper, Washington State University, Pullman, WA.
[18]	Baker, T., Collier, D. (2003). The benefits of optimizing prices to manage demand in hotel revenue management systems. Production and Operations Management, 12 (4), 502-518.
[19]	QuikSigma (2011). Software for Six Sigma project management. Promontory Management Group (Layton, UT, USA).

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Victor Pimentel, Aysajan Eziz, Tim Baker. (2021). Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures. Mathematics and Computer Science, 6(1), 8-15. https://doi.org/10.11648/j.mcs.20210601.12

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ACS Style

Victor Pimentel; Aysajan Eziz; Tim Baker. Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures. Math. Comput. Sci. 2021, 6(1), 8-15. doi: 10.11648/j.mcs.20210601.12

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AMA Style

Victor Pimentel, Aysajan Eziz, Tim Baker. Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures. Math Comput Sci. 2021;6(1):8-15. doi: 10.11648/j.mcs.20210601.12

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@article{10.11648/j.mcs.20210601.12,
  author = {Victor Pimentel and Aysajan Eziz and Tim Baker},
  title = {Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures},
  journal = {Mathematics and Computer Science},
  volume = {6},
  number = {1},
  pages = {8-15},
  doi = {10.11648/j.mcs.20210601.12},
  url = {https://doi.org/10.11648/j.mcs.20210601.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20210601.12},
  abstract = {Research in hotel revenue management system design has not paid much attention to the demand forecasting side of the system. And the research that has examined forecasting has tended to focus on the comparison of specific forecaster methodologies, as opposed to prioritizing how a total system should be parameterized: how far in the future should projections be, how much data to use to update each specific parameter, which measure of forecast error to use, and how long to freeze each parameter/forecast before updating. This paper fills this prioritization void by utilizing a full-functionality hotel reservation system simulation validated by the revenue management staff of a major hotel chain as the basis for running screening experiments on an exhaustive set of forecaster parameters with regards to their impact on bottom-line system performance (average nightly net revenue, where net revenue refers to total room rate receipts minus an overbooking per person penalty that estimates the discounted lost sales of future revenues). A screening experiment is run for each general type of operating environment (demand intensity, degree of market segment differentiation) that a property might face. We find that only two parameters are significant: the final combined forecast horizon length and how long that final forecast is frozen before updating. We find that these two factors interact in a negative fashion to influence net revenue.},
 year = {2021}
}

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TY  - JOUR
T1  - Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures
AU  - Victor Pimentel
AU  - Aysajan Eziz
AU  - Tim Baker
Y1  - 2021/01/28
PY  - 2021
N1  - https://doi.org/10.11648/j.mcs.20210601.12
DO  - 10.11648/j.mcs.20210601.12
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
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PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20210601.12
AB  - Research in hotel revenue management system design has not paid much attention to the demand forecasting side of the system. And the research that has examined forecasting has tended to focus on the comparison of specific forecaster methodologies, as opposed to prioritizing how a total system should be parameterized: how far in the future should projections be, how much data to use to update each specific parameter, which measure of forecast error to use, and how long to freeze each parameter/forecast before updating. This paper fills this prioritization void by utilizing a full-functionality hotel reservation system simulation validated by the revenue management staff of a major hotel chain as the basis for running screening experiments on an exhaustive set of forecaster parameters with regards to their impact on bottom-line system performance (average nightly net revenue, where net revenue refers to total room rate receipts minus an overbooking per person penalty that estimates the discounted lost sales of future revenues). A screening experiment is run for each general type of operating environment (demand intensity, degree of market segment differentiation) that a property might face. We find that only two parameters are significant: the final combined forecast horizon length and how long that final forecast is frozen before updating. We find that these two factors interact in a negative fashion to influence net revenue.
VL  - 6
IS  - 1
ER  -

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Author Information

Victor Pimentel

Department of Management, College of Business, New Mexico State University, Las Cruces, the United States
Aysajan Eziz

Ivey Business School, University of Western Ontario, London, Canada
Tim Baker

Carson College of Business, Washington State University, Richland, United States

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Plain Text BibTeX RIS

APA Style

Victor Pimentel, Aysajan Eziz, Tim Baker. (2021). Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures. Mathematics and Computer Science, 6(1), 8-15. https://doi.org/10.11648/j.mcs.20210601.12

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ACS Style

Victor Pimentel; Aysajan Eziz; Tim Baker. Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures. Math. Comput. Sci. 2021, 6(1), 8-15. doi: 10.11648/j.mcs.20210601.12

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AMA Style

Victor Pimentel, Aysajan Eziz, Tim Baker. Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures. Math Comput Sci. 2021;6(1):8-15. doi: 10.11648/j.mcs.20210601.12

Copy | Download

@article{10.11648/j.mcs.20210601.12,
  author = {Victor Pimentel and Aysajan Eziz and Tim Baker},
  title = {Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures},
  journal = {Mathematics and Computer Science},
  volume = {6},
  number = {1},
  pages = {8-15},
  doi = {10.11648/j.mcs.20210601.12},
  url = {https://doi.org/10.11648/j.mcs.20210601.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20210601.12},
  abstract = {Research in hotel revenue management system design has not paid much attention to the demand forecasting side of the system. And the research that has examined forecasting has tended to focus on the comparison of specific forecaster methodologies, as opposed to prioritizing how a total system should be parameterized: how far in the future should projections be, how much data to use to update each specific parameter, which measure of forecast error to use, and how long to freeze each parameter/forecast before updating. This paper fills this prioritization void by utilizing a full-functionality hotel reservation system simulation validated by the revenue management staff of a major hotel chain as the basis for running screening experiments on an exhaustive set of forecaster parameters with regards to their impact on bottom-line system performance (average nightly net revenue, where net revenue refers to total room rate receipts minus an overbooking per person penalty that estimates the discounted lost sales of future revenues). A screening experiment is run for each general type of operating environment (demand intensity, degree of market segment differentiation) that a property might face. We find that only two parameters are significant: the final combined forecast horizon length and how long that final forecast is frozen before updating. We find that these two factors interact in a negative fashion to influence net revenue.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures
AU  - Victor Pimentel
AU  - Aysajan Eziz
AU  - Tim Baker
Y1  - 2021/01/28
PY  - 2021
N1  - https://doi.org/10.11648/j.mcs.20210601.12
DO  - 10.11648/j.mcs.20210601.12
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 8
EP  - 15
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20210601.12
AB  - Research in hotel revenue management system design has not paid much attention to the demand forecasting side of the system. And the research that has examined forecasting has tended to focus on the comparison of specific forecaster methodologies, as opposed to prioritizing how a total system should be parameterized: how far in the future should projections be, how much data to use to update each specific parameter, which measure of forecast error to use, and how long to freeze each parameter/forecast before updating. This paper fills this prioritization void by utilizing a full-functionality hotel reservation system simulation validated by the revenue management staff of a major hotel chain as the basis for running screening experiments on an exhaustive set of forecaster parameters with regards to their impact on bottom-line system performance (average nightly net revenue, where net revenue refers to total room rate receipts minus an overbooking per person penalty that estimates the discounted lost sales of future revenues). A screening experiment is run for each general type of operating environment (demand intensity, degree of market segment differentiation) that a property might face. We find that only two parameters are significant: the final combined forecast horizon length and how long that final forecast is frozen before updating. We find that these two factors interact in a negative fashion to influence net revenue.
VL  - 6
IS  - 1
ER  -

Copy | Download