(Georgia Institute of Technology, 2007-02-16)
Ferguson, Mark E.; Crystal, Carolyn Roberts; Higbie, Jon; Kapoor, Rohit
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.
(Georgia Institute of Technology, 2006-05-31)
Ferguson, Mark E.; Crystal, Carolyn Roberts; Higbie, Jon; Kapoor, Rohit
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 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.
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.