This document discusses balancing demand and capacity in services. It introduces key concepts like excess demand, excess capacity, maximum capacity and optimum capacity. It describes how demand can vary relative to capacity over time and strategies for managing capacity like leveling, stretching/shrinking, chasing demand and flexible capacity. The document also discusses predicting demand patterns, managing queues, using reservations systems and setting capacity allocation targets.

1. Chapter 9
Balancing Demand
and Capacity

2. Relating Demand to Capacity:
Four Key Concepts
• Excess demand: too much demand relative to
capacity at a given time
• Excess capacity: too much capacity relative to
demand at a given time
• Maximum capacity: upper limit to a firm’s ability
to meet demand at a given time
• Optimum capacity: point beyond which service
quality declines as more customers are serviced

3. Variations in Demand Relative to
Capacity
(Fig. 9-1)
VOLUME DEMANDED
Demand exceeds capacity
(business is lost)
CAPACITY UTILIZED
Maximum Available Demand exceeds
Capacity optimum capacity
(quality declines)
Optimum Capacity
(Demand and Supply
Well Balanced
Excess capacity
Low Utilization (wasted resources)
(May Send Bad Signals)
TIME CYCLE 1 TIME CYCLE 2

4. Defining Productive Capacity
in Services
• Physical facilities to contain customers
• Physical facilities to store or process goods
• Physical equipment to process
people, possessions, or information
• Labor used for physical or mental work
• Public/private infrastructure—
e.g., highways, airports, electricity

5. Alternative Capacity Management
Strategies
• Level capacity (fixed level at all times)
• Stretch and shrink
– offer inferior extra capacity at peaks (e.g. bus/metro
standees)
– vary seated space per customer (e.g. elbow room, leg room)
– extend/cut hours of service
• Chase demand (adjust capacity to match demand)
– schedule downtime in low demand periods
– use part-time employees
– rent or share extra facilities and equipment
– cross-train employees
• Flexible Capacity (vary mix by segment)

6. Predictable Demand Patterns and
Their Underlying Causes (Table 9-1)
Predictable Cycles Underlying Causes of
of Demand Levels Cyclical Variations
– day – employment
– week – billing or tax payments/refunds
– month – pay days
– year – school hours/holidays
– seasonal climate changes
– other
– public/religious holidays
– natural cycles
(e.g. coastal tides)

7. Causes of Seemingly Random Changes
in Demand Levels
• Weather
• Health problems
• Accidents, Fires,
Crime
• Natural disasters
Question: which of
these
events can be
predicted?

8. Alternative Demand Management
Strategies (Table 9-2)
• Take no action
– let customers sort it out
• Reduce demand
– higher prices
– communication promoting alternative times
• Increase demand
– lower prices
– communication, including promotional incentives
– vary product features to increase desirability
– more convenient delivery times and places
• Inventory demand by reservation system
• Inventory demand by formalized queueing

9. Hotel Room Demand Curves by
Segment
and by Season (Fig. 9-2)
Price per
Room Night
Bl Bh
Th Bh = business travelers in high season
Bl = business travelers in low season
Tl Th = tourist in high season
Tl = tourist in low season
Th
Bh
Bl Tl
Quantity of Rooms Demanded at Each Price
by Travelers in Each Segment in Each Season Note: hypothetical example

10. Avoiding Burdensome Waits for
Customers
• Add extra capacity so that demand can be met at
most times (problem: may add too many costs)
• Rethink design of queuing system to give priority
to certain customers or transactions
• Redesign processes to shorten transaction time
• Manage customer behavior and perceptions of
wait
• Install a reservations system

11. Alternative Queuing Configurations
(Fig. 9-4)
Single line, single server, single stage
Single line, single servers at sequential stages
Parallel lines to multiple servers
Designated lines to designated servers
Single line to multiple servers (“snake”)
21
29
28
“Take a number” (single or multiple servers) 30 25
20
26 24
31 27
32 23

12. Tailoring Queuing Systems to Market
Segments: Criteria for Allocation to
Designated Lines
• Urgency of job
– emergencies vs. non-emergencies
• Duration of service transaction
– number of items to transact
– complexity of task
• Payment of premium price
– First class vs. economy
• Importance of customer
– frequent users/loyal customers vs. others

13. Ten Propositions on the Psychology
of Waiting Lines (Table 9-3)
1. Unoccupied time feels longer
2. Preprocess/postprocess waiting feel longer
than in-process
3. Anxiety makes waiting seem longer
4. Uncertain waiting is longer than known, finite
waiting
5. Unexplained waiting seems longer
6. Unfair waiting is longer than equitable waiting
Sources: Maister; Davis & Heineke; Jones & Peppiatt
7. People will wait longer for more valuable

14. Benefits of Effective Reservations
Systems
• Controls and smoothes demand
• Pre-sells service
• Informs and educates customers in advance of
arrival
• Customers avoid waiting in line for service (if
service times are honored)
• Data capture helps organizations prepare
financial projections

15. Characteristics of Well-designed
Reservations Systems
• Fast and user friendly for customers and staff
• Can answer customer questions
• Offers options for self service (e.g. Web)
• Accommodates preferences (e.g., room with
view)
• Deflects demand from unavailable first choices
to alternative times and locations
• Includes strategies for no-shows and
overbooking

16. Setting Capacity Allocation Sales
Targets for a Hotel by Segment and
Capacity (% rooms)
Time Period (Fig. 9-5)Week 36
Week 7
(Low Season) (High Season)
100%
Out of commission for renovation Executive service guests
Executive service
guests
Transient guests
Weekend
package
50% W/E
package
Transient guests
Groups and conventions
Groups (no conventions)
Airline contracts Airline contracts
Nights: M Tu W Th F S Sn M Tu W Th F S Sn
Time

17. Information Needed for Demand
and Capacity Management
Strategies
• Historical data on demand level and
composition, noting responses to marketing
variables
• Demand forecasts by segment under specified
conditions
• Fixed and variable cost data, profitability of
incremental sales
• Site-by-site demand variations