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dc.contributor.authorThompson, Gary
dc.date.accessioned2020-09-12T21:06:46Z
dc.date.available2020-09-12T21:06:46Z
dc.date.issued2011-08-01
dc.identifier.other7734257
dc.identifier.urihttps://hdl.handle.net/1813/71975
dc.description.abstractThis article extends the analysis of table mixes for restaurants, on the principle that table mix helps drive revenue. The article presents the results of a simulation experiment of more than forty-six thousand restaurant contexts to evaluate the accuracy of “naïve table mix” calculations. An earlier study presented a simple method of calculating a recommended mix of tables in a restaurant, and another found that those calculations provided table mixes yielding within about 1 percent of the revenue provided by the optimal table mix. While the earlier simulation studies assumed that the space required by a table was directly proportional to the number of seats it contained, that is not always the case. Thus, this article presents space-oriented versions of the existing naïve table mix methods. The article also presents the results of a test of two other forms of the naïve table mix calculations, which include party revenue (or contribution). The simplest of the naïve table mix models proved to be the best, on balance. It yielded table mixes that generated more than 98 percent of the revenue achievable by near-optimal table mixes. As a caveat, however, in more than 5 percent of the simulated scenarios, the best naïve table mix model yielded less than 95 percent of the revenue achievable by a near-optimal table mix. These findings suggest that the naïve table mix calculations be used with caution.
dc.language.isoen_US
dc.rightsRequired Publisher Statement: © Cornell University. Reprinted with permission. All rights reserved.
dc.subjectrestaurants
dc.subjectrevenue management
dc.subjectoperations
dc.subjectsimulation
dc.titleInaccuracy of the “Naïve Table Mix” Calculations
dc.typearticle
dc.description.legacydownloadsThompson6_Inaccuracy_naive_table.pdf: 147 downloads, before Aug. 1, 2020.
local.authorAffiliationThompson, Gary: gmt1@cornell.edu Cornell University School of Hotel Administration


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