Case Study: Ruby Hotel Room Forecasting A hotel room is a perishable good. If it is vacant for one night, the revenue is lost forever." Sarah Lee was commenting on the issue of capacity utilization in the hotel business. "On the other hand, the customer is king with us. We go to great pains to avoid telling a customer with a reservation at the front desk that we don't have a room for him in the hotel." As Manager of Revenue Management at Ruby's hotels, Sarah faced this trade-off constantly. To complicate the matter, customers often booked reservations and then failed to show, or cancelled reservations just before their expected arrival. In addition, some guests stayed over in the hotel extra days beyond their original reservation and others checked out early. A key aspect of dealing with the capacity of the room will based on the number of arrivals and departure of the day. However, some of the room were expected to be out of order or out of service due to maintenance work in the room. The hotel will have various forecasting data daily depending on the seasons or periods. As the revenue manager, Sarah must be able to decide their weekly or monthly room pricing in advance, so that all Front office staff will be aware with any promotion, changes, or increases of the room pricing. This will also assist other department to schedule and organize their staff accordingly. In order to occupy the empty vacant room and to maximize the revenue of the day, Sarah need to calculate the expected total number of rooms that is still available for sale. Those calculated available room, can be offered to last minute reservation guest who would like to check in on the same day or any walk-in guest who approach the front desk for a room. Question: 1. What are the major issues faced by Sarah as the hotel's revenue manager? Based on the above case study, why forecasting is important in hotel business? 2. 3. List down the forecasting data needed for Ruby's Hotel in order to calculate the available room and explain four (4) benefit of forecasting room availability. 4. Suggest how you can help Sarah to increase her hotel revenue from the forecasted data calculated below: a. Low Season -40% occupancy b. High Season -85% occupancy