A hotel room is a perishable good. If it is vacant for one night, the revenue is lost forever.-Monica Alvarez was
Question;A hotel room is a perishable good. If it is vacant for one night, the revenue is lost forever.Monica Alvarez was commenting on the issue of capacity utilization in the hotel business. Onthe other hand, the customer is king with us. We go to great pains to avoid telling a customerwith a reservation at the front desk that we dont have a room for him in the hotel.As Manager of Revenue Managment at Intercontinentals hotels, Monica faced this tradeoff constantly. To complicate the matter, customers often booked reservations and then failed toshow, or cancelled reservations just before their expected arrival. In addition, some guests stayedover in the hotel extra days beyond their original reservation and others checked out early. A keyaspect of dealing with the capacity-management problem was having a good forecast of howmany rooms would be needed on any future date. It was Monicas responsibility to prepare aforecast on Thursday afternoon of the number of rooms that would be occupied each day of thenext week (Saturday through Friday). This forecast was used by almost every department withinthe hotel for a variety of purposes, now she needed the forecast for a decision in her owndepartment.Times Square HotelThe Times Square Hotel was a large downtown business hotel with 1220 rooms andabundant meeting space for groups and conventions. It had been built and was operated byIntercontinental Hotels, a company that operated more than 4600 hotels and resorts in more than90 countries worldwide and was expanding rapidly into other lodging segments. Management ofThe Times Square reported regularly to Corporate headquarters on occupancy and revenueperformance.Hotel managers were rewarded for their ability to meet targets for occupancy andrevenue. Monica could not remember a time when the targets went down, but she had seen themgo up in the two years since she took the job as manager. The hotel managers were continuouslycomparing forecasts of performance against these targets. In addition to overseeing thereservations office with ten reservationists, Monica prepared the week-ahead forecast andpresented it on Thursday afternoon to other department managers in the hotel. The forecast wasused to schedule, for example, daily work assignments for housekeeping personnel, the clerks atthe front desk, restaurant personnel, and others. It also played a role in purchasing and revenueand cost planning.OverbookingAt the moment, however, Monica needed her forecast to know how to treat anopportunity that was developing for next Saturday. It was Thursday, July 17, and Monicasforecasts were due by midafternoon for Saturday, July 19 through Friday, July 25. Although 1195rooms were reserved already for Saturday, Monica had just received a request from a tourcompany for as many as 250 more rooms for that night. The tour company would take anynumber of rooms less than 250 that Monica would provide, but no more than 250. NormallyMonica would be ecstatic about such a request: selling out the house for a business hotel on aSaturday would be a real coup. The request, in its entirety, put reservations above the capacity ofthe hotel, however. True, a reservation on the books Thursday was not the same as a head in thebed on Saturday, especially when weekend nights produced a lot of no-show reservations.Chances are good we still wouldnt have a full house on Saturday, Monica thought out loud.But if everybody came and someone was denied a room due to overbooking, I would certainlyhear about it!Monica considered the trade-off between a vacant room and denying a customer a room.The contribution margin from a room was about $180, since the low variable costs aroseprimarily from cleaning the room and check-in/check-out. On the other side, if a guest with areservation was denied a room at The Times Square, the front desk would find a comparableroom somewhere in the city, transport the guest there, and provide some gratuity, such as a fruitbasket, in consideration for the inconvenience. If the customer were an IHG Gold cardholder (afrequent guest staying more than 30 nights a year in the hotel), he or she would receive $100cash plus the next stay at Intercontinental free. Monica wasnt sure how to put a cost figure on adenied room, in her judgment, it should be valued, goodwill and all, at about twice thecontribution figure.ForecastingMonica focused on getting a good forecast for Saturday, July 19, and making a decisionon whether to accept the additional reservations for that day. She had historical data on demandfor rooms in the hotel, Exhibit 1 shows demand for the first 3 weeks for dates starting withSaturday, April 19. (Ten additional weeks [weeks 4-13] are contained in Intercontinental.xlsx andthus Saturday, July 19, was the beginning of week 14 in this database.)Exhibit 1Historical Demand and Bookings DataDemand figures (column C) included the number of turned-down requests for a reservation ona night when the hotel had stopped taking reservations because of capacity plus the number ofrooms actually occupied that night. Also included in Exhibit 1 is the number of rooms booked(column D) as of the Thursday morning of the week prior to each date. (Note that this Thursdayprecedes a date by a number of days that depends on the date's day of week. It is two days aheadof a Saturday date, seven days ahead of a Thursday, eight days ahead of a Friday. Also note thaton a Thursday morning, actual demand is known for Wednesday night, but not for Thursdaynight.)Monica had calculated pickup ratios for each date where actual demand was known inExhibit 1 (column E). Between a Thursday one week ahead and any date, new reservations wereadded, reservations were canceled, some reservations were extended to more nights, some wereshortened, and some resulted in no-shows. The net effect was a final demand that might be largerthan Thursday bookings (a pickup ratio greater than 1.0) or smaller than Thursday bookings (apickup ratio less than 1.0). Monica looked at her forecasting task as one of predicting the pickupratio. With a good forecast of pickup ratio, she could simply multiply by Thursday bookings toobtain a forecast of demand.From her earliest experience in a hotel, Monica was aware that the day of the week(DOW) made a lot of difference in demand for rooms, her recent experience in reservationssuggested that it was key in forecasting pickup ratios. Downtown business hotels like hers tendedto be busiest in the middle of the workweek (Tuesday, Wednesday, Thursday) and light on theweekends. Using the data in her spreadsheet, she had calculated a DOW index for the pickupratio during each day of the week, which is shown in column F of Exhibit 1. Thus, for example,the average pickup ratio for Saturday is about 86.5% of the average pickup ratio for all days ofthe week. Her plan was to adjust the data for this DOW effect by dividing each pickup ratio bythis factor. This adjustment would take out the DOW effect, and put the pickup ratios on thesame footing. Then she could use the stream of adjusted pickup ratios to forecast Saturday'sadjusted pickup ratio. To do this, she needed to think about how to level out the peaks andvalleys of demand, which she knew from experience couldn't be forecasted. Once she had thisforecast of adjusted pickup ratio, then she could multiply it by the Saturday DOW index to getback to an unadjusted pickup ratio. "Let's get on with it," she said to herself. "I need to get ananswer back on that request for 250 reservations."Questions1. Verify the Day-of-Week indices in Column F of Exhibit 1. If you dont agree with Monica,give your new indices.2. What forecasting procedure (try at least 3) would you recommend for the adjusted pickupratio? Report the same error measure (e.g., MAD, MAPE, or MSE) for each method tried.3. What is your forecast for Saturday, July 19? What will you do about the current request for upto 250 rooms for Saturday?
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