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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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better unconstraining of airline demand data for improved forecast accuracy and greater revenues

Better Unconstraining of Airline Demand Data for Improved Forecast Accuracy and Greater Revenues

AGIFORS--RM Study Group

Berlin, April 2002

Larry R. Weatherford, PhDUniversity of Wyoming

Stefan Pölt, PhD

Lufthansa German Airlines

outline of presentation
Outline of Presentation

I. Introduction

II. Review of Common Unconstraining Methods

III. Comparison of Unconstraining Performance

IV. Comparison of Revenue Performance

V. Conclusion

slide3

I. Introduction

• 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

slide4

II. Review of Common 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

slide5

III. Comparison of Unconstraining Performance

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

slide6

Leads us to use simulated data--randomly generated “true

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).

slide7

A. Simulated Data Sets

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%

slide14

IV. Comparison of Revenue Performance

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 ]

slide15

Details:

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)

slide21

V. Conclusion

The type of unconstrainer you’re using can make a BIG revenue

difference.

Business: 1.3 – 2.9% on high demand legs

Leisure: 2 – 12% on high demand legs

Questions?