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A hotel room is a perishable good. If it is vacant for one night, the revenue is lost forever.-Monica Alvarez was




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. 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 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 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-management problem was having a good forecast of how;many rooms would be needed on any future date. It was Monicas responsibility to prepare a;forecast on Thursday afternoon of the number of rooms that would be occupied each day of the;next week (Saturday through Friday). This forecast was used by almost every department within;the hotel for a variety of purposes, now she needed the forecast for a decision in her own;department.;Times Square Hotel;The Times Square Hotel was a large downtown business hotel with 1220 rooms and;abundant meeting space for groups and conventions. It had been built and was operated by;Intercontinental Hotels, a company that operated more than 4600 hotels and resorts in more than;90 countries worldwide and was expanding rapidly into other lodging segments. Management of;The Times Square reported regularly to Corporate headquarters on occupancy and revenue;performance.;Hotel managers were rewarded for their ability to meet targets for occupancy and;revenue. Monica could not remember a time when the targets went down, but she had seen them;go up in the two years since she took the job as manager. The hotel managers were continuously;comparing forecasts of performance against these targets. In addition to overseeing the;reservations office with ten reservationists, Monica prepared the week-ahead forecast and;presented it on Thursday afternoon to other department managers in the hotel. The forecast was;used to schedule, for example, daily work assignments for housekeeping personnel, the clerks at;the front desk, restaurant personnel, and others. It also played a role in purchasing and revenue;and cost planning.;Overbooking;At the moment, however, Monica needed her forecast to know how to treat an;opportunity that was developing for next Saturday. It was Thursday, July 17, and Monicas;forecasts were due by midafternoon for Saturday, July 19 through Friday, July 25. Although 1195;rooms were reserved already for Saturday, Monica had just received a request from a tour;company for as many as 250 more rooms for that night. The tour company would take any;number of rooms less than 250 that Monica would provide, but no more than 250. Normally;Monica would be ecstatic about such a request: selling out the house for a business hotel on a;Saturday would be a real coup. The request, in its entirety, put reservations above the capacity of;the hotel, however. True, a reservation on the books Thursday was not the same as a head in the;bed 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 certainly;hear 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 arose;primarily from cleaning the room and check-in/check-out. On the other side, if a guest with a;reservation was denied a room at The Times Square, the front desk would find a comparable;room somewhere in the city, transport the guest there, and provide some gratuity, such as a fruit;basket, in consideration for the inconvenience. If the customer were an IHG Gold cardholder (a;frequent guest staying more than 30 nights a year in the hotel), he or she would receive $100;cash plus the next stay at Intercontinental free. Monica wasnt sure how to put a cost figure on a;denied room, in her judgment, it should be valued, goodwill and all, at about twice the;contribution figure.;Forecasting;Monica focused on getting a good forecast for Saturday, July 19, and making a decision;on whether to accept the additional reservations for that day. She had historical data on demand;for rooms in the hotel, Exhibit 1 shows demand for the first 3 weeks for dates starting with;Saturday, April 19. (Ten additional weeks [weeks 4-13] are contained in Intercontinental.xlsx and;thus Saturday, July 19, was the beginning of week 14 in this database.);Exhibit 1;Historical Demand and Bookings Data;Demand figures (column C) included the number of turned-down requests for a reservation on;a night when the hotel had stopped taking reservations because of capacity plus the number of;rooms 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 Thursday;precedes a date by a number of days that depends on the date's day of week. It is two days ahead;of a Saturday date, seven days ahead of a Thursday, eight days ahead of a Friday. Also note that;on a Thursday morning, actual demand is known for Wednesday night, but not for Thursday;night.);Monica had calculated pickup ratios for each date where actual demand was known in;Exhibit 1 (column E). Between a Thursday one week ahead and any date, new reservations were;added, reservations were canceled, some reservations were extended to more nights, some were;shortened, and some resulted in no-shows. The net effect was a final demand that might be larger;than Thursday bookings (a pickup ratio greater than 1.0) or smaller than Thursday bookings (a;pickup ratio less than 1.0). Monica looked at her forecasting task as one of predicting the pickup;ratio. With a good forecast of pickup ratio, she could simply multiply by Thursday bookings to;obtain 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 reservations;suggested that it was key in forecasting pickup ratios. Downtown business hotels like hers tended;to be busiest in the middle of the workweek (Tuesday, Wednesday, Thursday) and light on the;weekends. Using the data in her spreadsheet, she had calculated a DOW index for the pickup;ratio 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 of;the week. Her plan was to adjust the data for this DOW effect by dividing each pickup ratio by;this factor. This adjustment would take out the DOW effect, and put the pickup ratios on the;same footing. Then she could use the stream of adjusted pickup ratios to forecast Saturday's;adjusted pickup ratio. To do this, she needed to think about how to level out the peaks and;valleys of demand, which she knew from experience couldn't be forecasted. Once she had this;forecast of adjusted pickup ratio, then she could multiply it by the Saturday DOW index to get;back to an unadjusted pickup ratio. "Let's get on with it," she said to herself. "I need to get an;answer back on that request for 250 reservations.;Questions;1. 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 pickup;ratio? 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 up;to 250 rooms for Saturday?


Paper#19729 | Written in 18-Jul-2015

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