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Better Unconstraining of Airline Demand Data for Improved Forecast Accuracy and Greater Revenues. AGIFORS--RM Study Group Berlin, April 2002 Larry R. Weatherford, PhD University of Wyoming Stefan P ölt, PhD Lufthansa German Airlines. Outline of Presentation. I. Introduction
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AGIFORS--RM Study Group
Berlin, April 2002
Larry R. Weatherford, PhDUniversity of Wyoming
Stefan Pölt, PhD
Lufthansa German Airlines
II. Review of Common Unconstraining Methods
III. Comparison of Unconstraining Performance
IV. Comparison of Revenue Performance
• One of the major factors that affects forecast accuracy is the inability to observe the true (unconstrained) demand
• Had several presentations lately (Pölt, 2000; Weatherford, 2000; Zeni, 2001) that discussed different unconstraining methods
• What’snew here? Want to quantify the revenue impact of using the more sophisticated unconstraining methods
Going to look at 6 commonly used methods:
A. Naïve 1—use all data (open and closed)
B. Naïve 2—use only “open” data
C. Naïve 3—replace “closed” with larger of actual,
avg. of “open”
D. Booking Profile
E. Projection Detruncation
F. Expectation Maximization
Intuitively, it makes sense that more statistically sound procedures
like the “Projection Detruncation” and “Expectation Maximization”
methods should do a better job than the “Naïve”
methods at estimating the true unconstrained demand, but the
question is how much better and is it worth the effort?
Of course, one of the real problems in performing this analysis is that
if one uses real airline data, we never know what the true
unconstrained demand is and therefore are not able to accurately
compare all 6 methods
unconstrained demand” and also randomly generated “booking
limits” that determine whether or not we observe the true
unconstrained demand or some constrained value.
Then, we can make an honest evaluation of how much better one
method does than another and how close it came to the true
unconstrained demand (because we secretly know what that is).
We’ll look at 2 different data sets (1000 observations each):
1. Simulated #1, unconstrained mean = 20, % unconstrained
varies from 0% to 98%
2. Simulated #2, unconstrained mean = 4, % unconstrained
varies from 0 to 98%
So, we know EM & PD are the most robust unconstraining methods
…but how much revenue impact does it make when we integrate
such an improved unconstrainer in our overall RM system??
Let’s test it using real airline (major US) data—2 representative
markets (business, leisure)
Process: historical bookings data available, unconstrain data,
generate forecasts of demand, establish optimal booking limits,
interface bkg limits with random arrivals, calculate actual revenue,
[ Repeat for 1100 weeks of departures ]
Leg Optimization generated by EMSRb
Forecasts simply used average of past unconstrained obs.
# of reading days/dcps = 10
# of fare classes = 5 (see next slide)
Tested at multiple demand/capacity ratios (0.9 to 1.5)
The type of unconstrainer you’re using can make a BIG revenue
Business: 1.3 – 2.9% on high demand legs
Leisure: 2 – 12% on high demand legs