1. CH 12 Managing Capacity and Demand

2. Strategies for Matching Supply and Demand for Services
Partitioning
demand
Developing
complementary
services
Establishing
price
incentives
reservation
systems
Promoting
off-peak
Yield
management
SUPPLY
Cross-
training
employees
Increasing
customer
participation
Sharing
capacity
Scheduling
work shifts
Creating
adjustable
Using
part-time

3. Segmenting Demand at a Health Clinic60 70 80 90
100
110
120
130
140 1 2 3 4 5
Day of week
Percentage of average daily
physician visits
Smoothing Demand by Appointment
Scheduling
Day Appointments
Monday
Tuesday
Wednesday
Thursday
Friday

4. Discriminatory Pricing for Camping
Experience No. of Daily
type Days and weeks of camping season days fee
Saturdays and Sundays of weeks 10 to 15, plus $6.00
Dominion Day and civic holidays
Saturdays and Sundays of weeks 3 to 9 and 15 to 19,
plus Victoria Day
Fridays of weeks 3 to 15, plus all other days of weeks
9 to 15 that are not in experience type 1 or 2
Rest of camping season free
EXISTING REVENUE VS PROJECTED REVENUE FROM DISCRIMINATORY PRICING
Existing flat fee of $ Discriminatory fee
Experience Campsites Campsites
type occupied Revenue occupied (est.) Revenue
$14, , $30,000
, , , ,250
, , ,
, , … ….
Total , $ 64, , $59,000

5. Hotel Overbooking Loss Table
Number of Reservations Overbooked
No Prob-
shows ability
Expected loss, $

6. LP Model for Weekly Workshift Schedule with Two Days-off Constraint
Schedule matrix, x = day off
Operator Su M Tu W Th F Sa
x x … … … … … x x … … … …
… x x … … …
… x x … … …
… … … … x x …
… … … … x x …
… … … … x x …
x … … … … … x
Total
Required
Excess

7. Scheduling Part-time Bank Tellers
Decreasing part-time teller demand histogram
Tellers required
Mon. Tues Wed. Thurs. Fri.
Two Full-time Tellers 5 4 1 3 2
Fri Mon Wed Thurs Tues.
Tellers required
DAILY PART-TIME WORK SCHEDULE, X=workday
Teller Mon Tues Wed Thurs Fri.
x … x … x
x … … x x
3, x … … … x
… … x … x

8. Ideal Characteristics for Yield Management
Relatively Fixed Capacity
Ability to Segment Markets
Perishable Inventory
Product Sold in Advance
Fluctuating Demand
Low Marginal Sales Cost and High Capacity Change Cost

9. Seasonal Allocation of Rooms by Service Class for Resort Hotel
First class
Standard
Budget
20%
20%
20%
30%
50%
30%
50%
60%
Percentage of capacity allocated
to different service classes
50%
30%
30%
10%
Peak Shoulder Off-peak Shoulder
(30%) (20%) (40%) (10%)
Summer Fall Winter Spring
Percentage of capacity allocated to different seasons

10. Surfside Hotel No-Show Experience
Probability
Reservations
Overbooked
Cumulative d
P(d) x
P(d〈 x)
0.07
0.00 1
0.19 2
0.22
0.26 3
0.16
0.48 4
0.12
0.64 5
0.10
0.76 6
0.86 7
0.04
0.93 8
0.02
0.97 9
0.01
0.99
Opportunity loss of no-show : $ 40
Overbooking loss of guest “walked” : $ 100
The expected number of no-show is calculated as
0*(0.07) + 1*(0.19) + 2*(0.22) + … + 8*(0.02) + 9*(0.01) = 3.04
Expected opportunity loss per day = 3.04 x $ 40 = $

11. E (revenue of next booking) ≥ E (cost of next booking)
Marginal Cost Approach
E (revenue of next booking) ≥ E (cost of next booking)
Revenue of filling a room ⅹ Probability of more no-shows than overbooked rooms
≥ Cost of dissatisfied customer ⅹ Probability of fewer of the same number of no-
Shows than overbooked rooms
Cu ⅹ P (Overbookings ＜ No-shows) ≥ Co ⅹ P (Overbookings ≥ No-shows)
Cu ⅹ P( x ＜ d ) ≥ Co ⅹ P( x ≥ d )
Cu ⅹ {1 - P( x ＞ d )} ≥ Co ⅹ P( x ＞ d )
Cu ⅹ P( x ＞ d ) + P( x ＞ d ) ≤ Cu
P( x ＞ d ) ≤ o Cu
Co + Cu
Where
Cu = the $40 room contribution that is lost when a reservation is not honored
(i.e., the number of no-shows is underestimated)
Co = the $100 opportunity loss associated with not having a room available for
an overbooked guest (i.e., the number of no-shows is overestimated)
d = the number of no-shows based on past experience
x = the number of rooms overbooked
$40
$40 + $100
P( x ＞d ) ≤ ≤ 0.286