Demand Forecasting Using Smoothed Demand Curves in Hospitality

Forecasting demand is one of the fundamental components of a successful revenue management system in hospitality. The industry requires understandable models that contribute to adaptability by a revenue management department to make data-driven decisions. Data analysis proves an essential role for the time until the check-in date, which differs per day of week. This paper aims to provide a new model, which is inspired by cubic smoothing splines, resulting in smooth demand curves per rate class over time until the check-in date. This model regulates the error between data points and a smooth curve, and therefore able to capture natural guest behavior. The result is obtained by solving a linear programming model, which enables the incorporation of industry knowledge in the form of constrains. The performance is expressed in Weighted Absolute Percentage Error (WAPE) and revenue. This model claims to generate 13.3% more revenue for 2019.