Measuring and monitoring granular forecast performance

Forecasting demand is a key component of all Revenue Management (RM) processes. Measuring the performance of forecasting is not only crucial for effective inventory controls but it also provides an insight into the changing market dynamics, customer behaviour patterns and so on, which further play an important role in an airline’s strategy. During the segment‐based RM era, the most detailed level of forecasting required was ‘FlightLeg‐Fareclass‐DepartureDate‐DCP’. Forecast values at this level used to be large positive integer values. The advent of Origin–Destination (O&D) RM necessitated the detailed ‘O&D‐Itinerary‐PointOfSale‐FareClass‐DepartureDate’ level forecasts, which generally tend to be fractions less than 1, therefore are granular in nature. The actual bookings, however, arrive in integer values greater or equal to 1. Thus conventional measures such as Mean Percentage Error, Mean Absolute Percentage Error, Mean Squared Error and so on tend to become inappropriate to measure forecast performance. This article aims to briefly highlight the perceived shortcomings of existing methods in measuring and monitoring the above‐mentioned granular forecasts, outline an innovative method and discuss its suitability.