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

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/13600

Title: A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.3)
Author: Ferguson, Mark E. ; Crystal, Carolyn Roberts ; Higbie, Jon ; Kapoor, Rohit
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.
Type: Working Paper
URI: http://hdl.handle.net/1853/13600
Date: 2007-02-16
Publisher: Georgia Institute of Technology
Subject: Revenue management
Truncated demand
Forecasting
Unconstrained demand

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