Pearson New International Edition International pcl tp indd 1



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Professional Front Office Management Pearson New International Edition by Robert Woods, Jack D. Ninemeier, David K. Hayes, Michele A. Austin (z-lib.org)

MANAGING FORECAST DATA
FIGURE 4
Stayover report for the Altoona Hotel—January 1–14 (total rooms 

300).
Date
Expected stayovers
Actual early departures
Early departure (%)
Jan. 1 (Sun.)
10
1
10%
Jan. 2 (Mon.)
100
10
10%
Jan. 3 (Tues.)
200
20
10%
Jan. 4 (Wed.)
250
25
10%
Jan. 5 (Thurs.)
250
50
20%
Jan. 6 (Fri.)
200
20
10%
Jan. 7 (Sat.)
200
20
10%
Jan. 8 (Sun.)
10
1
10%
Jan. 9 (Mon.)
100
10
10%
Jan. 10 (Tues.)
200
20
10%
Jan. 11 (Wed.)
250
25
10%
Jan. 12 (Thurs.)
250
50
20%
Jan. 13 (Fri.)
200
20
10%
Jan. 14 (Sat.)
200
20
10%
Total/average
2,420
292
12%
221


The analysis of early departures in Figure 4 presents an additional challenge as
the FOM uses historical data. Note that, despite the relatively consistent early depar-
ture percentage, the actual number of early departures ranges from a low of 1 (on
Jan. 1 and Jan. 8) to a high of 50 (on Jan. 5 and Jan. 12). In most cases, the number
of early departures increases as the number of anticipated stayovers increases.
Therefore, the Altoona Hotel’s FOM must not use the average early departure per-
centage (12 percent) on a day when, historically, the actual number of early depar-
tures varies greatly from that percentage. Professional FOMs know that historical
data about early departures should be reviewed carefully and closely, especially on
days when a sellout or near sellout is expected.
Overstays
The PMS provides historical data about the number of guests who extend their stay
beyond their original check-out date. These data should be carefully reviewed and
monitored by FOMs. The most accurate forecast of overstays frequently requires the
consideration of room type. For example, assume that the 300-room Altoona Hotel
consists of the room types listed in Figure 5.
Assume also that the hotel’s reservations, no-shows, and early departures have
been forecast for a day one week from today. The FOM estimates that 10 overstays
are likely. According to the occupancy forecast formula in Figure 3, the 10 addition-
al overstay rooms should be added to the number of hotel rooms sold. If the hotel
were in a sellout (or near sellout) situation, it would likely face difficulty if the 10
overstays were in the eight WH room types and two BR room types. Why? On a sell-
out night at least some of the guests with reservations arriving that day will likely
have reserved a WH room and may be ever a BR suite. These arriving guests will find
that the room types they reserved are unavailable, because the guests in those rooms
decided to extend their stays. Imagine the difficulties arising if one of the bridal suites
is not available to a bride and groom checking in that day (especially if they are hold-
ing their 500-person wedding reception at the hotel!). To avoid potential difficulties
such as these, many FOMs carefully monitor the historical number of overstays by
room total and by selected room type.

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