Abstract
Demand shocks—unobservable, sudden changes in customer behavior—are a common source of forecast error in airline revenue management systems. The COVID-19 pandemic has been one example of a highly impactful macro-level shock that significantly affected demand patterns and required manual intervention from airline analysts. Smaller, micro-level shocks also frequently occur due to special events or changes in competition. Despite their importance, shock detection methods employed by airlines today are often quite rudimentary in practice. In this paper, we develop a science-based shock detection framework based on statistical hypothesis testing which enables fast detection of demand shocks. Under simplifying assumptions, we show how the properties of the shock detector can be expressed in analytical closed form and demonstrate that this expression is remarkably accurate even in more complex environments. Simulations are used to show how the shock detector can successfully be used to identify positive and negative shocks in both demand volume and willingness-to-pay. Finally, we discuss how the shock detector could be integrated into an airline revenue management system to allow for practical use by airline analysts.