Title:
A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.3)

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 2007-03-07T19:50:22Z
dc.date.available 2007-03-07T19:50:22Z
dc.date.issued 2007-02-16
dc.description.abstract A successful revenue management system requires accurate demand forecasts for each customer segment. The 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 unconstraining methods against our proposed method by testing them on intentionally constrained simulated data. Results show our proposed method outperform other methods in two out of three data sets. We also test the revenue impact of our proposed method, EM, and "no unconstraining" 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 237384 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/13600
dc.language.iso en_US en
dc.publisher Georgia Institute of Technology en
dc.relation.replaces http://hdl.handle.net/1853/10765
dc.subject Revenue management en
dc.subject Truncated demand en
dc.subject Forecasting en
dc.subject Unconstrained demand en
dc.title A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.3) 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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