hotel-inventory-management

You optimize hotel room inventory and dynamic pricing by using Python libraries like PuLP, SciPy, and Scikit-learn to model demand forecasting, overbooking strategies, and channel mix allocation. This balances occupancy and average daily rate to maximize revenue.

Calculating overbooking limits involves determining the optimal number of rooms to overbook to maximize expected profit. This approach balances potential walk costs against lost revenue from empty rooms during peak demand periods.

Yes, you can use Scikit-learn for demand forecasting in hotels. The models predict future demand by analyzing historical data, local events, competitor activity, and economic indicators to adjust room rates effectively.

You allocate rooms across distribution channels by optimizing the channel mix across Direct, OTAs, and GDS. This maximizes net revenue by accounting for varying distribution costs and commissions associated with each booking channel.

Yes, group booking analysis helps decide whether to accept group bookings by evaluating their impact. It analyzes how group reservations affect transient room availability and overall revenue to ensure maximum profitability.

To adjust hotel room rates based on competitor pricing, you need demand forecasts, competitor price data, and price elasticity metrics. These inputs drive dynamic pricing models that optimize room rates for upcoming dates.