A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.1)

dc.contributor.author Ferguson, Mark E.
dc.contributor.author Crystal, Carolyn Roberts
dc.contributor.author Higbie, Jon
dc.contributor.author Kapoor, Rohit
dc.date.accessioned 2005-12-07T21:41:49Z
dc.date.available 2005-12-07T21:41:49Z
dc.date.issued 2005-12-07
dc.description.abstract A successful revenue management system requires accurate demand forecasts for each customer segment. These forecasts are used to set booking limits for lower value customers to ensure an adequate supply for higher value customers. The very use of booking limits, however, constrains the historical demand data needed for an accurate forecast. Ignoring this interaction leads to substantial penalties in a firm's potential revenues. We review existing unconstraining methods and propose a new method that includes some attractive properties not found in the existing methods. We evaluate several of the common methods used to unconstrain historical demand data against our proposed method by testing them on intentionally constrained simulated data. Results show our proposed method along with the Expectation Maximization (EM) method perform the best. We also test the revenue impact of our proposed method, EM, and “no unconstraining” on actual booking data from a hotel/casino. We show that performance varies with the initial starting protection limits and a lack of unconstraining leads to significant revenue losses. en
dc.format.extent 271370 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/7398
dc.language.iso en_US en
dc.relation.isreplacedby http://hdl.handle.net/1853/10765
dc.subject Forecasting en
dc.subject Revenue management en
dc.subject Truncated demand en
dc.subject Unconstrained demand en
dc.title A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.1) en
dc.type Text
dc.type.genre Working Paper
dspace.entity.type Publication
local.contributor.corporatename Scheller College of Business
relation.isOrgUnitOfPublication a2f83831-ae41-4d65-82ff-c8bf95db4ffb
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