slide 1service operations capacity management in services module why do queues build up? process...
Post on 22-Dec-2015
215 views
TRANSCRIPT
Slide 1Service Operations
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
Slide 2Service Operations
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.
Slide 3Service Operations
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?
Slide 4Service Operations
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
Flow Time (minutes)
Pro
bab
ilit
y
0%
20%
40%
60%
80%
100%
0 15 30 45 60 75 90 105
120
135
150
165
180
195
8
Slide 5Service Operations
Telemarketing with variability inarrival times + activity times
In reality service times– exhibit variability
0%
5%
10%
15%
20%
25%
30%
0 10 20 30 40 50 60 70 80 90 100
110
120
130
140
150
160
170
180
190
Mor
e
Flow Time (minutes)
Pro
bab
ilit
y
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Cu
mu
lati
ve P
rob
abil
ity
0%
5%
10%
15%
20%
25%
0 10 20 30 40 50 60 70 80 90 100
110
120
130
140
150
160
170
180
190
Mor
e
Flow Time Histogram
Pro
bab
ilit
y
0%
20%
40%
60%
80%
100%
90%
Cu
mu
lati
ve P
rob
abil
ity
Flow Time (minutes)
In reality inter-arrival times– exhibit variability
Slide 6Service Operations
Telemarketing with variability: The effect of utilization
Average service time = – 9 minutes
Average service time =– 9.5 minutes
0%
1%
2%
3%
4%
5%
6%
7%
8%
0 10 20 30 40 50 60 70 80 90
100
110
120
130
140
150
160
170
180
190
Mor
e
Flow Time
Pro
bab
ilit
y
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0%
5%
10%
15%
20%
25%
0 10 20 30 40 50 60 70 80 90
100
110
120
130
140
150
160
170
180
190
Mor
e
Flow Time
Pro
bab
ilit
y
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Slide 7Service Operations
Why do queues form?
1. variability: – arrival times– service times– processor availability
Role of utilization: – Impact of variability increases
as utilization increases! (arrival throughput or capacity )
0123456789
10
0 20 40 60 80 100 TIME
0
1
2
3
4 5
0 20 40 60 80 100
TIME
Call #
Inventory (# of calls in system)
Slide 8Service Operations
Industry Process AverageFlow Time
TheoreticalFlow Time
Flow TimeEfficiency
Life Insurance New PolicyApplication
72 hrs. 7 min. 0.16%
ConsumerPackaging
NewGraphicDesign
18 days 2 hrs. 0.14%
CommercialBank
ConsumerLoan
24 hrs. 34 min. 2.36%
Hospital PatientBilling
10 days 3 hrs. 3.75%
AutomobileManufacture
FinancialClosing
11 days 5 hrs 5.60%
Flow Times in White Collar Processes
Slide 9Service Operations
Queuing Systems to model Service Processes: A Simple Process
Sales Repsprocessing
calls
Incoming callsCalls
on Hold
Answered Calls
MBPF Inc. Call Center
Blocked Calls(Busy signal)
Abandoned Calls(Tired of waiting)
Order Queue“buffer” size K
Slide 10Service Operations
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
Slide 11Service Operations
Performance Measures
Sales– Throughput R
– Abandonment Ra
Cost– Server utilization – Inventory/WIP : # in queue Ii /system I
Customer service– Waiting/Flow Time: time spent in queue Ti /system T
– Probability of blocking Rb
Slide 12Service Operations
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
Variability
AverageWait Time
Utilization 100%
ProcessCapacity
Slide 13Service Operations
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
Levers to reduce waiting and increase QoS: variability reduction + safety capacity
Slide 14Service Operations
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 =
Slide 15Service Operations
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?
Example 2: MBPF Calling Center with limited buffer size. Impact of blocking
Slide 16Service Operations
THE BAT Case = Managing the operations of a customer service department
Handouts to be distributed in class
Putting Tech Support on The Fast Track
Slide 17Service Operations
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?
Servers
Queue
ServerQueue
ServerQueue
50%
50%
Tp = 2.5min
Tp = 2.5min
Tp = 2.5min
Ri = 1/3min
Ri = 1/3min
Slide 18Service Operations
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?
ServerQueue ServerQueue
Tp = 2.5min
Ri = 1/3min
Tp = 2.5min
ServersQueue
Tp = 5min
Ri = 1/3min
Slide 19Service Operations
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
Slide 20Service Operations
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
Slide 21Service Operations
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
Nu
mb
er
of
pa
tie
nts
0
4
8
12
16
5AM 11AM 5PM 11PM 4AMTime of day
Maximum #Patient arrivals/hr
Average #Patient arrivals/hr
+1
-1
Source: McKinseyQuarterly 2001
Slide 22Service Operations
Smart Staffing/Capacity Management at Sof-Optics
0
10
20
30
40
50
60
70
80
90
100
6:3
0
7:3
0
8:3
0
9:3
0
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
Slide 23Service Operations
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
Slide 24Service Operations
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, e-mail etc.
Supply mgt
Demand mgt
Slide 25Service Operations
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
Slide 26Service Operations
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