Koç University
OPSM 301 Operations Management
Class 2:
Business process flows:
Measurement
Zeynep Aksin
zaksin@ku.edu.tr
Announcements
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Wrap-up: King Sooper’s video
New module: business process flows
From notes and handouts
Study questions
Also: cases at the end
– Kristen’s Cookie
– Benihana
Performance Measurement
 External performance strongly depends on
output, input, and resource markets, and
transformation effectiveness of the process
 Internal performance measures: processing
cost, flow time, variety, service availability...
The Dynamics of a Process
 We examine processes from the perspective of flow
 To study process flows, we first answer three important
questions:
– On average, how many flow units pass through the process
per unit time?
– On average, how much time does a typical flow unit spend
within process boundaries?
– On average, how many flow units are within process
boundaries at any point in time?
Operational Measures
 On average, how many flow units pass
through the process per unit time?
THROUGHPUT RATE (TH)
Operational Measures
 On average, how much time does a typical
flow unit spend within process
boundaries?
FLOW TIME (FT)
Operational Measures
 On average, how many flow units are
within process boundaries at any point in
time?
INVENTORY (I)
LITTLE’S LAW
 Relating throughput rate, flow time, and inventory
Inventory
Throughput Rate 
Flow Time
Inventory I
...
... ...
[units]
... ...
Throughput Rate
Flow Time T [hrs]
[units/hr]
Understanding Little’s Law:
Consider a first come first served Queue
Inventory I
Time=0 ...
... ...
[units]
... ...
Throughput Rate
Flow Time T [hrs]
Time=t
...
[units/hr]
Inventory I
... ...
[units]
... ...
Throughput Rate
Flow Time T [hrs]
[units/hr]
Inventory I
Time=FT
...
... ...
[units]
... ...
Throughput Rate
Flow Time T [hrs]
[units/hr]
An Intuitive Argument for Little's Law
 Consider a process with the FCFS queue discipline
 An order departs the process: At this moment there are I (Inventory)
orders within the process
 The orders that are in the process now are the ones that came after
our departing order had arrived, in other words, they arrived during
the waiting period of the departing order
 Since order arrival rate is equal to the throughput rate, we have the
following relationship:
Inventory = Throughput Rate x Flow Time
Once again...
Inventory
Throughput Rate 
Flow Time
Inventory = Throughput x Flow Time
Inventory
Flow Time 
Throughput Rate
Little’s Law basics
 Little’s Law is for a system in steady
state:
input rate = output rate
 Applies to most systems, even those with
variability
 Uses AVERAGE values
Example: flow unit is material
 Fast food restaurant processes an average of
5000kgs, of hamburgers per week. Typical
inventory of raw meat in cold storage is 2500kg.
 Throughput R=5000kg/week
 Average Inventory I=2500 kg.
 Average flow time T=I/R=2500/5000=0.5 weeks
Example: flow unit is customers
 A café in Beyoglu serves on average 60
customers per night. A typical night is about 10
hours. At any point there are on average 18
customers in the café.
 Throughput R=60 customers/night; 6
customers/hour
 Average Inventory I=18 customers
 Average flow time T= I/R= 3 hours
Example: flow unit is cash
 A steel company processes $400 million of iron ore per
year. The cost of processing is $200 million per year. The
average inventory is $100 million. How long does a
typical dollar spend in the process?
 R=$600 million/year
 I=$100 million
 T=I/R=1/6 year or 2 months
Example
 Koç University
3000 students
enrolled
600 new students
admitted on average
Example: MBPF Finance Inc.
adapted from Managing Business Flows, by Anunpindi et al.
 MBPF Finance Inc. provides loans to
qualified customers. The company receives
about 1000 loan applications per 30-day
working month and makes accept/reject
decisions based on an extensive review of
each application
MBPF Finance Inc.
 Currently, MBPF Finance Inc. processes each
application individually. On average, 20% of all
applications received approval. An internal
audit showed that, on average, MBPF Finance
Inc. had about 500 applications in process at
various stages of the approval procedure, but on
which no decisions had yet been made.
 In response to customer complaints about the
time taken to process each application, MBPF
Finance Inc. called in OPSM Consulting Inc.
Example: cont’d
 OPSM Consulting found out that although most
applications could be processed rather quickly,
some took a disproportionate amount of time
because of insufficient and/or unclear
documentation. They suggested the following
Process II:
 Because, the percentage of approved applications
is fairly low, and Initial Review Team should be set
up to pre-process all applications according to
strict but fairly mechanical guidelines.
MBPF Finance Inc.
 Each application would fall into one of three
categories: type A (looks excellent), type B
(needs more detailed evaluation), and type C
(reject summarily). Type A and B applications
would be forwarded to different specialist
subgroups
 Each subgroup would then evaluate the
applications in its domain and make
accept/reject decisions
Example: (cont’d)
 Process II was implemented on an
experimental basis. The company
found out that, on average, 25% of all
applications were of type A, 25% were
of B, and 50% were of C. Typically,
about 70% of type A and 10% of B were
approved on review.
Example (cont’d)
 Internal audit checks showed that, on
average, 200 applications were with the Initial
Review Team undergoing preprocessing.
Only 25 were with the Subgroup A Team
undergoing the next stage of processing and
approximately 150 were with the Subgroup B
Team
 MBPF Finance Inc. would like to determine if
the implemented changes have improved
service performance.
FlowLoan Inc.
(Initial Process)
1000/month
20%
Accept
200/month
80%
Reject
800/month
Review
500
FlowLoan Inc.
(Modified Process)
Subgroup A
Review
25%
1000/month
Initial
Review
200
25%
25
Subgroup B
Review
150
50%
70%
30%
Accept
200/month
Reject
800/month
10%
90%