Larry Weatherford

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

Accurate forecasts of passenger demand are the heart of a successful
Revenue Management system. The forecasts are usually based on historical booking data. These bookings do not reflect historical demand in all cases because booking requests can be rejected due to capacity constraints or booking control limits. In this presentation, we examine 6 different methods of unconstraining bookings to demand. We use simulation analysis of many different scenarios of historical booking data with different percentages of the data being constrained, using simulated data, to show that the Expectation Maximization and Projection Detruncation methods are the most robust and that as the percentage of data constrained increases to 60-80%, that their estimate of the unconstrained mean increases by 20-80% over the naïve unconstraining methods, which leads to less bias and more accurate forecasts. Finally, using actual booking data from a major US airline, we show that upgrading the unconstraining process can lead to revenue gains of
2-12%."