Capacity Management in Services Module

Why do queues build up?

Process attributes and Performance measures of queuing processes

Safety Capacity
 Its effect on customer service
 Pooling of capacity

Queuing Processes with Limited Buffer
 Optimal investment


Specialists versus generalists
Managing Customer Service
 SofOptics
Service Operations
Slide 1
Telemarketing at




During some half hours, 80% of calls dialed received a busy signal.
Customers getting through had to wait on average 10 minutes for an available agent.
Extra telephone expense per day for waiting was $25,000.
For calls abandoned because of long delays, L.L.Bean still paid for the queue time
connect charges.
L.L.Bean conservatively estimated that it lost $10 million of profit because of suboptimal allocation of telemarketing resources.
Service Operations
Slide 2
Some Questions to discuss:

Why did they loose money?

What are the performance measures for a call center?

How model this as a process?

What decisions must managers make?
Service Operations
Slide 3
Telemarketing: deterministic analysis


it takes 8 minutes to serve a
customer
6 customers call per hour
–

one customer every 10 minutes
Flow Time = 8 min
–
same for every customer
– histogram: →
Flow Time Histogram
100%
Probability
80%
60%
40%
20%
Flow Time (minutes)
Service Operations
Slide 4
195
180
165
150
135
120
105
90
75
60
45
30
15
8
0
0%
Telemarketing with variability in
arrival times + activity times
0%
25%
90%
80%
70%
20%
60%
50%
40%
15%
10%
30%
Service Operations
Slide 5
190
More
Flow Time (minutes)
170
180
140
150
160
0%
120
130
0%
90
100
110
20%
10%
70
80
5%
40
50
60
exhibit variability
100%
0
10
–
30%
20
30
In reality service times
Probability

Cumulative Probability
Flow Time (minutes)
190
0%
More
20%
170
180
5%
140
150
160
40%
120
130
10%
90
100
110
60%
70
80
15%
0
10
– exhibit variability
20%
40
50
60
In reality inter-arrival times
100%
90%
80%
20
30

Probability
25%
Cumulative Probability
Flow Time Histogram
Telemarketing with variability:
The effect of utilization

Average service time =
–
9 minutes
8%
100%
7%
90%
Probability
80%
6%
70%
5%
60%
4%
50%
3%
40%
30%
2%
20%
190
180
170
160
150
140
130
120
110
90
100
80
70
60
50
40
30
20
Flow Time
9.5 minutes
More
–
0%
10
Average service time =
10%
0%
0

1%
25%
100%
90%
Probability
20%
80%
70%
15%
60%
50%
10%
40%
30%
5%
20%
10%
Service Operations
Slide 6
190
180
170
160
150
More
Flow Time
140
130
120
110
100
90
80
70
60
50
40
30
20
10
0%
0
0%
Why do queues form?
Call #
1. variability:
– arrival times
– service times
– processor availability

10
9
8
7
6
5
4
3
2
1
0
0
20
40
Role of utilization:
–
Impact of variability increases
as utilization increases!
(arrival throughput  or
capacity )
60
80
100
TIME
Inventory (# of calls in system)
5
4
3
2
1
0
0
20
40
60
TIME
Service Operations
Slide 7
80
100
Flow Times in White Collar Processes
Industry
Process
Average
Flow Time
Theoretical
Flow Time
Flow Time
Efficiency
Life Insurance
New Policy
Application
72 hrs.
7 min.
0.16%
Consumer
Packaging
New
Graphic
Design
Consumer
Loan
18 days
2 hrs.
0.14%
24 hrs.
34 min.
2.36%
Hospital
Patient
Billing
10 days
3 hrs.
3.75%
Automobile
Manufacture
Financial
Closing
11 days
5 hrs
5.60%
Commercial
Bank
Service Operations
Slide 8
Queuing Systems to model Service Processes: A Simple
Process
Order Queue
“buffer” size K
Sales Reps
processing
calls
Incoming calls
Calls
on Hold
MBPF Inc. Call Center
Blocked Calls Abandoned Calls
(Busy signal) (Tired of waiting)
Service Operations
Slide 9
Answered Calls
What to manage in such a process?

Inputs
– InterArrival times/distribution
– Service times/distribution

System structure
– Number of servers
– Number of queues
– Maximum queue length/buffer size

Operating control policies
– Queue discipline, priorities
Service Operations
Slide 10
Performance Measures

Sales
– Throughput R
– Abandonment Ra

Cost
– Server utilization r
– Inventory/WIP : # in queue Ii /system I

Customer service
– Waiting/Flow Time: time spent in queue Ti /system T
– Probability of blocking Rb
Service Operations
Slide 11
The drivers of waiting:
How reduce waiting?

Queuing theory shows that
waiting increases with:
– variability
 Arrival times
 Service times
Average
Wait Time
– length of avg. service time
Variability
– Arrival throughput
 Nonlinearly: “it blows up!”

100% Utilization
Hence: reduce waiting by:
Process
Capacity
– Reduction of variability
– Reduction of arrivals/throughput
– Add “safety” capacity
 Reduce length of service
 Increase staffing
Service Operations
Slide 12
Levers to reduce waiting and increase QoS:
 variability reduction + safety capacity

How reduce system variability?

Safety Capacity = capacity carried in excess of expected demand to cover
for system variability
– it provides a safety net against higher than expected arrivals or services and
reduces waiting time
Service Operations
Slide 13
Example 1: MBPF Calling Center with one server, unlimited
buffer. The basics of QoS



Consider MBPF Inc. that has a customer service representative (CSR) taking calls.
When the CSR is busy, the caller is put on hold. The calls are taken in the order
received.
Assume that calls arrive exponentially at the rate of one every 3 minutes. The CSR
takes on average 2.5 minutes to complete the reservation. The time for service is
also assumed to be exponentially distributed.
The CSR is paid $20 per hour. It has been estimated that each minute that a
customer spends in queue costs MBPF $2 due to customer dissatisfaction and loss
of future business.
– Holding cost H =
– Average number waiting in buffer Ii =
– MBPF’s waiting cost = H  Ii =
Service Operations
Slide 14
Example 2: MBPF Calling Center with limited buffer
size. Impact of blocking


In reality only a limited number of people can be put on hold (this depends on the
phone system in place) after which a caller receives busy signal. Assume that at
most 5 people can be put on hold. Any caller receiving a busy signal simply calls a
competitor resulting in a loss of $100 in revenue.
– # of servers c =
– buffer size K =
What is the hourly loss because of callers not being able to get through?
Service Operations
Slide 15
THE BAT Case = Managing the operations of a
customer service department
Putting Tech Support on The Fast Track
Handouts to be distributed in class
Service Operations
Slide 16
Example 3: MBPF Calling Center with 1 or 2 queues. Impact of
Resource Pooling

2 phone numbers
–
MBPF hires a second CSR who is assigned a new
telephone number. Customers are now free to call
50%
either of the two numbers. Once they are put on hold
customers tend to stay on line since the other may be
Ri = 1/3min
worse..
Tp = 2.5min
Queue Server
Tp = 2.5min
50%
Queue Server

1 phone number: pooling
–
both CSRs share the same telephone number and the
customers on hold are in a single queue
Tp = 2.5min

Ri = 1/3min
Which system is “better?”
–
–
–
In which sense?
When?
Why?
Queue
Servers
Service Operations
Slide 17
Example 4: MBPF Calling Center with 2 service tasks. The impact of process
structure & resource capabilities: Specialization Vs. Flexibility
A second service task is added. Two
possibilities to structure the process:
Tp = 2.5min

Specialization
Tp = 2.5min
Ri = 1/3min
–
Each service task is performed by a
specialized agent
– Average flow time T =

Queue Server
Tp = 5min
Flexibility
–
The entire service is performed by one of
two flexible agents = generalists.
– Agerage flow time T =
Ri = 1/3min
Queue
Servers

Which system is “better?”
–
In which sense?
– When?
– Why?
Service Operations
Slide 18
Queue Server
Increase quality of service:
1. reduce variability

Two types of variability:
–
Predictable
– Unpredictable = “Stochastic”

Two sources of variability:
–
Arrivals
– Length of service

Predictable variability is reduced by:
–
Proper triage: differentiated treatment
– Proper scheduling & appointments
– Standardization of service (not always an option)
 Key = synchronize arrivals with end of service
Service Operations
Slide 19
How increase quality of service with stochastic variability
2. reducing utilization is your only option

How reduce utilization?
1. Reduce throughput
– Not typically desired b/c of social, ethical, or financial concerns …
2. Increase capacity
 Recall section 3!: bottleneck management
 Key: when one cannot perfectly synchronize flows so that there is
remaining, irreducible stochastic process variability then one must build in
a capacity cushion.
 One cannot provide high quality of service at high utilization
Service Operations
Slide 20
Increase quality of service: anticipate predictable variability +
build safety-capacity for stochastic variability. e.g. smart staffing
–
Average walk-ins often are fairly predictable
 Keep data (use IT!): find average trend (predictable) + stochastic variations
 Staff accordingly: use time-buckets + build safety-capacity staffing
16
Number of patients
12
+1 s
8
Maximum #
Patient arrivals/hr
Average #
Patient arrivals/hr
4
-1 s
0
5AM
Service Operations
11AM
5PM
Time of day
Slide 21
11PM
4AM
Source: McKinsey
Quarterly 2001
Smart Staffing/Capacity Management at Sof-Optics
100
90
80
70
60
50
40
Demand (# Calls/30min)
30
20
Current Supply/Capacity
(# Calls/30min)
10
Optimized Supply w/o
demand mgt or capital investment
Service Operations
Slide 22
17:30
16:30
15:30
14:30
13:30
12:30
11:30
10:30
9:30
8:30
7:30
6:30
0
Call Centers

In U.S.: $10B, > 70,000 centers, > 3M people (>3% of workforce)
 Most cost-effective channel to serve customers
 Strategic Alignment
– accounting: 90% are cost centers, 10% are revenue centers
– role: 60% are viewed as cost, 40% as revenue generators
– staffing: 60% are generalists, 40% specialists
– Trend: more towards profit centers & revenue generators

Trade-off: low cost (service) vs. high revenue (sales)
Source: O. Zeynep Aksin 1997
Service Operations
Slide 23
Framework for Analysis and Improvement of Service
Systems

Divide day into blocks based on arrival rates:
– Separate “peaks” from “valleys”


For each block evaluate performance measures given current staffing
Quantify financial impact of each action
– Workforce training: reduces mean and variability of service time
– Work flexibility from workforce: pools available capacity
– Time flexibility from workforce: better synchronization
Supply
– Retain experienced employees: increased safety capacity
mgt
– Additional workforce: Increases safety capacity
– Improved Scheduling: better synchronization
– Incentives to affect arrival patterns: better synchronization

Reservation mgt, pre-sell, Disney’s FastPass
– Decrease product variety: reduces variability of service time
– Increase maximum queue capacity
– Consignment program, fax, e-mail etc.
Service Operations
Slide 24
Demand
mgt
How do these insights related to our earlier “Levers for
Reducing Flow Time?”

“is to decrease the work content of (only ?) critical activities”,

and/or move it to non critical activities.
 Reduce waiting time:
– reduce variability


arrivals & service requests
synchronize flows within the process
– increase safety capacity


lower utilization
Pooling
– Match resource availability with flows in and out of process
Service Operations
Slide 25
Learning objectives:
General Service Process Management
 Queues build up due to variability.
 Reducing variability improves performance.
 If service cannot be provided from stock, safety capacity must
be provided to cover for variability.
– Tradeoff is between cost of waiting, lost sales, and cost of capacity.
 Improving Performance
– Reduce variability
– Increase safety capacity

Pooling servers/capacity
– Increase synchronization between demand (arrivals) and service
Service Operations

Manage demand

Synchronize supply: resource availability
Slide 26