Analytical framework for airline revenue management and network planning

Abstract

Traditional airline revenue management (RM) systems assume a stable schedule with a given capacity on each cabin and departure. The capacity is a key input to subsequent steps of optimization for determining inventory controls that would maximize revenues throughout the booking horizon, based on the established price points, demand to come, time left and seats left until departure. However, as an airline or its competitors make strategic changes, the effectiveness and robustness of such controls is impacted. For instance, capacity disturbances that occur very often because of equipment delivery delays, fleet assignment re-optimization because of inaccurate demand forecasts, technical failures or other operational issues all affect the effectiveness of RM decisions.The airline industry has widely adopted a set of standard metrics in evaluating the performance of a given network. Indicators such as Revenue per Available Seat-Kilometres/Miles, Cost per Available Seat-Kilometres/Miles, Load Factor and Yield support the management by usually offering a time-series view of the airline’s performance. However, identifying the main contributors of a performance drop, or which markets may offer opportunities for revenue improvement, is a more difficult task. To support the airline’s strategic decision making, a network performance evaluation framework is proposed, to be jointly used by RM and Network Planning & Scheduling. The recommendations that this framework would produce should definitely not replace the day-to-day RM and Scheduling decisions, but will guide the senior management in identifying potential strategic changes in terms of pricing, fleet assignment or route planning. Afterwards, more granular analysis can be done on the areas identified by using this framework, which would follow the standard tactical processes of an airline. In this article we will seek ways of improving both capacity deployment and RM strategies, while ensuring pricing consistency and a more robust RM under regular capacity disturbances and even under unexpected changes in capacity.