Koç University Graduate School of Business
MBA Program
OPSM 301: Operations Management
Session 12:
Service processes and flow variability
Zeynep Aksin
zaksin@ku.edu.tr
Recall the smiley face game: an unbalanced
line
 if average task times are different, will have an
unbalanced line
• will have idleness
 in unbalanced case, slowest task determines
output rate
• bottleneck is busy
• idleness in other stages
The role of variability
Capacity/hr:
6units/hr
Capacity/hr:
6units /hr
6
4 or 8/hr
4 or 8/hr
5
2 or 10
2 or 10
4
0 or 12
0 or 12
3
As variability increases, throughput (rate) decreases
The role of task times: a balanced line
 if task times are similar will have a balanced line
• in the absence of variability (deterministic)
complete synchronization is possible
• in a balanced line idleness is minimized,
though in the presence of variability full
synchronization cannot be achieved
Compounding effect of variability and unbalanced
task times
6/hr
4/hr
4/hr
4 or 8/hr
2 or 6/hr
3.5/hr
2 or 10
0 or 8
2.5/hr
Resource interaction effects
In a serial process downstream resources depend on upstream
resources: can have temporary starvation (idleness)
6/hr
6/hr
6/hr
6/hr
4 or 8/hr
4 or 8/hr
4 or 8/hr
6/hr
2 or 10
2 or 10
2 or 10
6/hr
0 or 12
0 or 12
6/hr
0 or 12
6/hr
4.5/hr
3/hr
1.5/hr
As variability increases, the impact of resource interaction increases
Variability in multi-stage processes
 We have seen how variability hurts performance
in a multi-stage process
– Worse with unbalanced task times and resource
interference
 Note that
– We assumed a very simplistic form of processing time
variability
– We assumed there is no variability in arrivals
 We now know variability hurts, but can’t say how
much yet
Want to eliminate as much variability as
possible from your processes: how?







specialization in tasks can reduce task time variability
standardization of offer can reduce job type variability
automation of certain tasks
IT support: templates, prompts, etc.
Incentives
Scheduled arrivals to reduce demand variability
Initiatives to smoothen arrivals
Want to reduce resource interference in your
processes: how?
 smaller lotsizes (smaller batches)
 better balanced line
 by speeding-up bottleneck (adding staff, changing
procedure, different incentives, change
technology)
 through cross-training
 eliminate steps
 buffers
 integrate work (pooling)
What differentiates services
 Customer contact: the physical presence of the
customer in the system
– Service systems with a high degree of customer
contact are more difficult to control
 The product is the process: the work process
involved in providing the service itself
Structuring the Service Encounter:
Service-System Design Matrix
Fundamental Problem:
Customer Demand
Variable Usage
Service Delivery System
Limited Capacity
Services cannot be produced in advance and stored for later consumption;
they must be produced at the time of consumption.
Designing Service Organizations
 We cannot inventory services
 In services capacity becomes the dominant
issue
– Too much capacity leads to excessive costs
– Insufficient capacity leads to lost customers
 Managing waiting lines is a central issue in
services
Service Blueprinting and Fail-Safing
 The standard tool for service process design is
the flowchart
– Called a service blueprint
 A unique feature of the service blueprint is the
distinction made between the high customer
contact aspects of the service and those
activities that the customer does not see
– Made with a “line of visibility” on the flowchart
Process Blueprint Example:
Automotive Service Operation
F
F
F
F
Not served in order
Process time-consuming
incorrect
diagnosis
incorrect
estimate
15
To address the “how much does variability
hurt” question: Consider service processes
 This could be a call center or a restaurant or a ticket
counter
 Customers or customer jobs arrive to the process; their
arrival times are not known in advance
 Customers are processed. Processing rates have some
variability.
 The combined variability results in queues and waiting.
 We need to build some safety capacity in order to reduce
waiting due to variability
Components of the Queuing System
Visually
Customers
come in
Customers are
served
Customers
leave
Specifications of a Service Provider
Reneges or abandonments
Arriving
Customers
Waiting
Pattern
Demand
Pattern
Service
Provider
Waiting
Customers
Served
Customers
Service Time
Resources
• Human resources
• Information system
• other...
Leaving
Customers
Satisfaction
Measures
The Service Process
 Customer Inflow (Arrival) Rate (Ri) ()
– Inter-arrival Time = 1 / Ri
 Processing Time Tp (unit load)
– Processing Rate per Server = 1/ Tp (µ)
 Number of Servers (c)
– Number of customers that can be processed simultaneously
 Total Processing Rate (Capacity) = Rp= c / Tp (cµ)
Operational Performance Measures
() Ri
waiting
processing
R ()
e.g10 /hr
10 /hr
Tw?








10 min, Rp=12/hr
Flow time T
=
Tw
+
Tp (waiting+process)
Inventory I
=
Iw
+
Ip
Flow Rate R
=
Min (Ri, Rp)
Stable Process =
Ri < Rp,, so that R = Ri
Little’s Law: I = R  T,
Iw = R  Tw, Ip = R  Tp
Capacity Utilization  = Ri / Rp < 1
Safety Capacity = Rp – Ri
Number of Busy Servers = Ip= c  = Ri  Tp
Flow Times with Arrival Every 4 Secs
(Service time=5 seconds)
Customer
Number
Arrival
Time
Departure
Time
Time in
Process
1
0
5
5
2
4
10
6
3
8
15
7
4
12
20
8
5
16
25
9
6
20
30
10
3
7
24
35
11
2
8
28
40
12
9
32
45
13
10
36
50
14
10
9
Customer Number
8
7
6
5
4
1
0
10
What is the queue size? Can we apply Little’s Law?
What is the capacity utilization?
20
30
Time
40
50
Flow Times with Arrival Every 6 Secs
(Service time=5 seconds)
Arrival
Time
Departure
Time
Time in
Process
10
1
0
5
5
9
2
6
11
5
8
3
12
17
5
4
18
23
5
5
24
29
5
6
30
35
5
7
36
41
5
2
8
42
47
5
1
9
48
53
5
10
54
59
5
What is the queue size?
What is the capacity utilization?
Customer Number
Customer
Number
7
6
5
4
3
0
10
20
30
Time
40
50
60
Effect of Variability
Customer
Number
Arrival
Time
Processing
Time
Time in
Process
1
0
7
7
2
10
1
1
3
20
7
7
4
22
2
7
5
32
8
8
6
33
7
14
7
36
4
15
8
43
8
16
9
52
5
12
10
54
1
11
10
9
8
Customer
7
6
5
4
3
2
1
0
10
20
30
40
50
60
70
Time
Queue Fluctuation
4
What is the queue size?
What is the capacity utilization?
Number
3
2
1
0
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64
Time
Effect of Synchronization
Customer
Number
Arrival
Time
Processing
Time
Time in
Process
1
0
8
8
2
10
8
8
8
3
20
2
2
7
4
22
7
7
6
5
32
1
1
5
6
33
1
1
4
7
36
7
7
3
8
43
7
7
2
9
52
4
4
1
10
54
5
7
What is the queue size?
What is the capacity utilization?
10
9
0
10
20
30
40
50
60
70
Conclusion
 If inter-arrival and processing times are constant, queues will
build up if and only if the arrival rate is greater than the
processing rate
 If there is (unsynchronized) variability in inter-arrival and/or
processing times, queues will build up even if the average
arrival rate is less than the average processing rate
 If variability in interarrival and processing times can be
synchronized (correlated), queues and waiting times will be
reduced
A measure of variability




Needs to be unitless
Only variance is not enough
Use the coefficient of variation
C or CV= s/m
Interpreting the variability measures
Ci = coefficient of variation of interarrival times
i) constant or deterministic arrivals
Ci = 0
ii) completely random or independent arrivals Ci =1
iii) scheduled or negatively correlated arrivals Ci < 1
iv) bursty or positively correlated arrivals
Ci > 1
Why is there waiting?
 the perpetual queue: insufficient capacity-add
capacity
 the predictable queue: peaks and rush-hourssynchronize/schedule if possible
 the stochastic queue: whenever customers
come faster than they are served-reduce
variability