Title:
A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.3)
A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.3)
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. 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.
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Date Issued
2007-02-16
Extent
237384 bytes
Resource Type
Text
Resource Subtype
Working Paper