Combining hybrid forecasting and fare adjustment with various unconstraining methods to maximize revenue in a global network with four airlines

In their competitive environment, airlines are always searching for ways to maximize revenues. This article uses the sophisticated Passenger Origin‐Destination Simulator (PODS) simulator to examine the revenue impact of four implementations of hybrid forecasting (HF) and fare adjustment (FA) in combination with three different methods of unconstraining – Expectation Maximization (EM), Projection Detruncation (PD) and Booking Curve (BC). Owing to the competitive nature of PODS (four airlines competing for customers) and its allowance for customer choice, we are able to assess all the implications of these HF and FA implementations in combination with unconstraining, including the impact of spill, upgrades and recapture. We find that in the fully/semi‐restricted fare environment, under either EM or PD, the optimal combination is HF only, independent of demand level and that under BC unconstraining, the optimal combination is HF with FA equal to 0.5 (medium demand) or HF only (high demand) if using leg optimization. Under network optimization, it is best to use HF with FA equal to 0.5 for EM/PD unconstraining and HF only if using BC unconstraining, independent of demand level. When using realistic booking data from major global airlines to calibrate the largest PODS global network (U1), we show that becoming more aggressive with HF and FA can lead to revenue gains of 0.3–2.3 per cent.