Case Study 2: Intercontinental Hotel Room Forecasting

Case Study 2:
Intercontinental Hotel Room Forecasting

Ahotel 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 don’t have a room for him in the hotel.”
As Manager of Revenue Managment at In­tercontinental’s
hotels, Monica 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-management
problem was having a good forecast of how many rooms would be needed on any
future date. It was Monica’s 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 Monica’s 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 wouldn’t 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 wasn’t 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

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“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 toeach 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 don’t 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?