1. 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
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2. 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 sub-optimal allocation of telemarketing resources.
$1.5 billion sales (70% direct sales/catalogue)
4 call centers in Main Freeport. One is seasonal operating durng the busy time of the year- christmas.
Max. handle 180,00 calls/day.. In million customer contacts.
4000 CSR’s employed during the peak time..
Even though they are concerned with their service to customers very much.. They exhibit certain problems at their call centers.. Here is a sneak preview of what is happening there.
Van Mieghem/Operations/Managing Services

3. 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?
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4. 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
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5. Telemarketing with variability in arrival times + activity times0% 5%
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Flow Time Histogram
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Flow Time (minutes)
In reality inter-arrival times
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Van Mieghem/Operations/Managing Services

6. Telemarketing with variability: The effect of utilization
Average service time =
9 minutes
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7. Inventory (# of calls in system)
Why do queues form? 1 2 3 4 5 6 7 8 9 10 20 40 60 80
TIME
Call #
Inventory (# of calls in system)
variability:
arrival times
service times
processor availability
Role of utilization:
Impact of variability increases as utilization increases! (arrival throughput  or capacity )
Van Mieghem/Operations/Managing Services

8. Flow Times in White Collar Processes
Van Mieghem/Operations/Managing Services

9. Queuing Systems to model Service Processes: A Simple Process
Order Queue
“buffer” size K
Sales Reps
processing
calls
Incoming calls
Answered Calls
Calls
on Hold
MBPF Inc. Call Center
Blocked Calls
(Busy signal)
Abandoned Calls
(Tired of waiting)
Van Mieghem/Operations/Managing Services

10. 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
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11. Performance Measures Sales Cost Customer service 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
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12. The drivers of waiting: How reduce waiting?
Queuing theory shows that waiting increases with:
variability
Arrival times
Service times
length of avg. service time
Arrival throughput
Nonlinearly: “it blows up!”
Hence: reduce waiting by:
Reduction of variability
Reduction of arrivals/throughput
Add “safety” capacity
Reduce length of service
Increase staffing
Average
Wait Time
Variability
100%
Utilization
Process
Capacity
Van Mieghem/Operations/Managing Services

13. 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
Van Mieghem/Operations/Managing Services

14. Example 1: MBPF Calling Center with one server, unlimited buffer
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 =
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15. Example 2: MBPF Calling Center with limited buffer size
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?
Van Mieghem/Operations/Managing Services

16. Putting Tech Support on The Fast Track
THE BAT Case = Managing the operations of a customer service department
Putting Tech Support on The Fast Track
Handouts to be distributed in class
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17. Example 3: MBPF Calling Center with 1 or 2 queues
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 either of the two numbers. Once they are put on hold customers tend to stay on line since the other may be worse..
1 phone number: pooling
both CSRs share the same telephone number and the customers on hold are in a single queue
Which system is “better?”
In which sense?
When?
Why?
Tp = 2.5min
Server
Queue
50%
Ri = 1/3min
Tp = 2.5min
Tp = 2.5min
Servers
Queue
Ri = 1/3min
Van Mieghem/Operations/Managing Services

18. Example 4: MBPF Calling Center with 2 service tasks
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:
Specialization
Each service task is performed by a specialized agent
Average flow time T =
Flexibility
The entire service is performed by one of two flexible agents = generalists.
Agerage flow time T =
Which system is “better?”
In which sense?
When?
Why?
Tp = 2.5min
Tp = 2.5min
Ri = 1/3min
Server
Queue
Servers
Queue
Tp = 5min
Ri = 1/3min
Van Mieghem/Operations/Managing Services

19. 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
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20. How increase quality of service with stochastic variability. 2
How increase quality of service with stochastic variability 2. reducing utilization is your only option
How reduce utilization?
Reduce throughput
Not typically desired b/c of social, ethical, or financial concerns …
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
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21. 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
Number of patients 4 8 12 16
5AM
11AM
5PM
11PM
4AM
Time of day
Maximum #
Patient arrivals/hr
Average #
+1 s
-1 s
Source: McKinsey
Quarterly 2001
Van Mieghem/Operations/Managing Services

22. Smart Staffing/Capacity Management at Sof-Optics10 20 30 40 50 60 70 80 90
100
6:30
7:30
8:30
9:30
10:30
11:30
12:30
13:30
14:30
15:30
16:30
17:30
Demand (# Calls/30min)
Current Supply/Capacity
(# Calls/30min)
Optimized Supply w/o
demand mgt or capital investment
Van Mieghem/Operations/Managing Services

23. 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
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24. 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
Retain experienced employees: increased safety capacity
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, etc.
Supply mgt
Demand mgt
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25. 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
Van Mieghem/Operations/Managing Services

26. 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
Manage demand
Synchronize supply: resource availability
Van Mieghem/Operations/Managing Services