How SAS gained 30% forecast accuracy thanks to innovative revenue management tools

November 23, 2021

Last updated: November 23, 2021

2 min read

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Erik Winter

Team Leader Revenue Management Systems, SAS

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Challenges and changes in the wake of global disruptions have always impacted the airline industry. But these demand shocks have also helped the industry to evolve and leverage technology to optimize for the future.

As airlines address dramatic changes in passenger demand and scheduling, an adaptable Revenue Management System (RMS) and forecasting tool is essential for effective business recovery. However, customer booking and cancellations patterns, as well as their willingness-to-pay have drastically changed from pre-pandemic levels. This has placed a dent in the RMS demand forecasting model, leaving gaps in the historical database.

To overcome this challenge, Amadeus has developed a new demand forecast system called Active Forecast Adjustment (AFA), working with SAS, Scandinavian Airlines, as the pilot airline. And the results were just what we needed at this point in time – thanks to AFA, we gained a 30% forecast accuracy from COVID-19 baseline.

Fortunately, Amadeus’ AFA already uses live sales data from non-departed flights, and as such, adjusts demand levels automatically as airlines resume operations. But in the current situation, which holds significant uncertainty about future customer demand, we needed a forecast to capture the most up-to-date demand patterns based on as little as a few months’ worth of live sales data. The new and innovative AFA model can quickly help airlines adapt to changes in the market.

To our benefit, we’ve been using Amadeus Revenue Management technology for many years and during the pandemic, we were able to quickly adapt to new circumstances. If we didn’t have AFA, the forecast would be off. In fact, during implementation, COVID-19 travel bans were issued around the world and we couldn’t fall back on the old system. Hence, Amadeus created this new forecast model to capture the most up-to-date demand patterns. Unlike the previous model, AFA automatically adjusts forecasts across all departure dates based on the latest live sales data. This was critical for us to quickly adapt and evolve our commercial strategies as well.

AFA has enabled us to be more flexible and agile in monitoring changing market developments and the variance between planned scenarios versus real passenger behavior. In the long term, we estimate numbers to even out to a 10-15% increase in forecast accuracy, which is around 1% increase in total revenue. With positive results already coming in, we expect more once bookings become more stable over time. Moreover, the increased automation we shall gain in the long run will allow us to manage resources to create impact elsewhere in the organization.

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