Title:
A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.1)
A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.1)
Author(s)
Ferguson, Mark E.
Crystal, Carolyn Roberts
Higbie, Jon
Kapoor, Rohit
Crystal, Carolyn Roberts
Higbie, Jon
Kapoor, Rohit
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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.
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Date Issued
2005-12-07
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271370 bytes
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Text
Resource Subtype
Working Paper