Overbooking as a Means to Manage Restaurant No-Shows and Cancellations: A Novel Model Extension

Abstract

Newsvendor models have been developed to determine the optimal overbooking level of hotel rooms to manage no-shows and cancellations. This research note extends the newsvendor model to a restaurant context by taking into account the “stretched capacity” of restaurants to determine the optimal overbooking levels for restaurant seats. Data were collected from a restaurant in Taiwan to illustrate the model. The percentage of no-shows per day in this restaurant ranged from 11% to 16%. Utilizing its stretched capacity, the restaurant can overbook up to five more seats than the estimated number of no-shows. The extended model will be most suitable for restaurants that largely depend on reservations (rather than walk-ins), such as luxury or fine-dining restaurants. Directions for future research on restaurant overbooking are provided.

Keywords

cancellations no-shows luxury newsvendor overbooking stretched capacity

Get full access to this article

View all access options for this article.

References

Alexandrov

Lariviere

M. A.

(2012). Are reservations recommended? Manufacturing & Service Operations Management, 14(2), 218–230. https://doi.org/10.1287/msom.1110.0360

Gregorash

B. J.

(2016). Restaurant revenue management: Apply reservation management? Journal of Information Technology & Tourism, 16(4), 331–346. https://doi.org/10.1007/s40558-016-0065-0

Heo

C. Y.

Lee

Mattila

(2013). Restaurant revenue management: Do perceived capacity scarcity and price differences matter? International Journal of Hospitality Management, 35(December), 316–326. https://doi.org/10.1016/j.ijhm.2013.05.007

Hwang

(2008). Restaurant table management to reduce customer waiting times. Journal of Foodservice Business Research, 11(4), 334–351. https://doi.org/10.1080/15378020802519603

Hwang

Wen

(2009). The effect of perceived fairness toward hotel overbooking and compensation practices on customer loyalty. International Journal of Contemporary Hospitality Management, 21(6), 659–675. https://doi.org/10.1108/09596110910975945

Kimes

S. E.

(2004). Restaurant revenue management: Implementation at Chevys Arrowhead. Cornell Hotel and Restaurant Administration Quarterly, 45(1), 52–67. https://doi.org/10.1177/0010880403260107

Kimes

S. E.

(2011). Customer attitudes towards restaurant reservations policies. Journal of Revenue Pricing Management, 10(3), 244–260. https://doi.org/10.1057/rpm.2009.24

Kimes

S. E.

Robson

(2004). The impact of restaurant table characteristics on meal duration and spending. Cornell Hotel and Restaurant Administration Quarterly, 45(4), 333–346. https://doi.org/10.1177/0010880404270063

Kimes

S. E.

Thompson

(2004). Restaurant revenue management at Chevys: Determining the best table mix. Decision Sciences, 35(3), 371–391. https://doi.org/10.1111/j.0011-7315.2004.02531.x

10.

Kimes

S. E.

Wirtz

(2002). Perceived fairness of demand-based pricing for restaurants. Cornell Hotel and Restaurant Administration Quarterly, 43(1), 31–37. https://doi.org/10.1016/S0010-8804(02)80006-4

11.

Manning

(2020). What happens when diners don’t show up for their dinner reservation. https://blog.opentable.com/2020/restaurant-no-shows-explained/

12.

McGuire

Kimes

(2006). The perceived fairness of waitlist management techniques for restaurants. Cornell Hotel and Restaurant Administration Quarterly, 47(2), 121–134. https://doi.org/10.1177/0010880405284212

13.

Netessine

Shumsky

(2002). Introduction to the theory and practice of yield management. INFORMS Transactions on Education, 3(1), 34–44. https://doi.org/10.1287/ited.3.1.34

14.

Parsa

H. G.

Shuster

B. K.

Bujisic

(2020). New classification system for the U.S. restaurant industry: Application of utilitarian and hedonic continuum model. Cornell Hospitality Quarterly, 61(4), 379–400. https://doi.org/10.1177/1938965519899929

15.

Phumchusri

Maneesophon

(2014). Optimal overbooking decision for hotel rooms revenue management. Journal of Hospitality and Tourism Technology, 5(3), 261–277. https://doi.org/10.1108/JHTT-03-2014-0006

16.

Sierag

D. D.

Van der Rest

J. P.

Koole

Van der Mei

R. D.

Zwart

(2017). A call for exploratory data analysis in revenue management forecasting: A case study of a small and independent hotel in the Netherlands. International Journal of Revenue Management, 10(1), 28–51. https://doi.org/10.1504/IJRM.2017.084147

17.

Thompson

(2002). Optimizing a restaurant’s seating capacity: Use dedicated or combinable tables? Cornell Hotel and Restaurant Administration Quarterly, 43(3), 48–57. https://doi.org/10.1177/0010880402434005

18.

Thompson

(2003). Optimizing restaurant-table configuration: Specifying combinable tables. Cornell Hotel and Restaurant Administration Quarterly, 44(1), 53–60. https://doi.org/10.1016/S0010-8804(03)90046-2

19.

Thompson

(2010). Restaurant profit management: The evolution of restaurant revenue management. Cornell Hospitality Quarterly, 51(3), 308–322. https://doi.org/10.1177/1938965510368653

20.

Tse

T. S. M.

Poon

Y. T.

(2017). Modeling no-shows, cancellations, overbooking and walk-ins in restaurant revenue management. Journal of Foodservice Business Research, 20(2), 127–145. https://doi.org/10.1080/15378020.2016.1198626

21.

Vajpai

G. N.

(2018). Managing overbooking in hotels: A probabilistic model using Poisson distribution. International Journal of Advance Research, Ideas and Innovation in Technology, 4(2), 1375–1379. https://www.ijariit.com/manuscripts/v4i2/V4I2-1655.pdf

22.

Vidotto

Brown

Beck

(2007). Managing restaurant tables using constraints. Knowledge-Based Systems, 20(2), 160–169. https://doi.org/10.1016/j.knosys.2006.11.002

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.07 MB