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A Note on Process Analysis
Article in SSRN Electronic Journal · October 2008
DOI: 10.2139/ssrn.1282478
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UVA-OM-1096
A Note on Process Analysis
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UVA-OM-1096
A NOTE ON PROCESS ANALYSIS
A process is essentially any procedure that converts inputs into outputs. For example, in
a bank loan approval process, bank staff and computer systems process information about a loan
applicant and return either an approval or a rejection of the loan application. In an airport
security check process, security personnel use equipment to inspect passengers and their luggage,
and either deem them as fit for boarding or detain them for further inspection. In a car assembly
process, workers and equipment convert car components into assembled vehicles.
Organizations create value by providing either manufactured products or services. These,
in turn, are the output of processes. For example, Federal Express uses a set of processes to
speedily deliver parcels from point A to point B. Disneyworld creates entertainment experiences
via another set of processes. Nike supplies shoes to stores via a set of processes, which include
obtaining raw materials, manufacturing, and distributing shoes. Clearly, the way in which a
process is managed impacts it performance, and this, in turn, impacts process output. This
makes process management crucial to an organization’s ability to create and deliver value.
To manage processes, we need to understand them. Process mapping or process-flow
diagramming is a visual tool that helps achieve this. Further, we need to develop some criteria
by which to measure the performance of a process. In this note, we illustrate how to analyze
processes using a simple process example: a neighborhood drycleaner.
The Process of Dry Cleaning
Consider the process at a typical dry-cleaning business. As customers enter, they wait in
line to give their clothing to a worker at the counter. The counter worker then asks each
customer when the clothes need to be ready, inspects the clothing, tags each item of clothing,
creates an order ticket, and gives the customer a receipt. The tag number on each item of
clothing corresponds to the number on the order ticket, so that the clothing is identifiable
throughout the dry-cleaning process. In addition, if the counter worker finds a stain on the
clothing, he marks the stain with a piece of tape. At this point, the counter worker puts the
This note was prepared by Joseph Parkhill (MBA ’04) and Kamalini Ramdas, Associate Professor of Business
Administration at the Darden Graduate School of Business. It was written as a basis for class discussion rather than
to illustrate effective or ineffective handling of an administrative situation. Copyright  2003 by the University of
Virginia Darden School Foundation, Charlottesville, VA. All rights reserved. To order copies, send an e-mail to
sales@dardenpublishing.com. No part of this publication may be reproduced, stored in a retrieval system, used in a
spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying, recording, or
otherwise—without the permission of the Darden School Foundation. Rev. 9/04. ◊
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clothes into one of three piles based on the color of the clothing: dark, beige, or white. The
tasks performed by the counter worker comprise the order-taking process.
Next, the machine operator pretreats the stained clothing by washing the stains with a
solution, making sure to maintain the color-based separation of the piles. After clothes have
been pretreated as needed, they are loaded in batches of dark, beige, or white clothing into the
dry-cleaning machine by the machine operator. Upon completion of the machine process, the
machine operator unloads each batch and passes the cleaned clothing on to the pressing station.
There are two pressers there who press the clothing using heavy duty clothespresses and then
pass the clothing on to the packer. The packer combines all the items from an order and wraps
them with plastic. Finally, the packer hangs the order, which is now ready for customer pickup.
To pick up an order, the customer presents his receipt to a second counter worker who
retrieves the order and charges the customer.
Figure 1 illustrates this dry-cleaning process. Typically, boxes are used to represent
stages where some work is done. Solid arrows show the flow of the product through the process.
Inverted triangles are used to depict inventory buildup. In addition, dotted lines can be used to
depict the flow of information. If a resource is shared across stages in a process, this can be
depicted by enclosing the relevant stages in a larger dotted box. For example, in the diagram
below, a dotted box is placed around pretreatment and dry cleaning as these stages share a
worker. It is useful to remember that process-flow diagramming is an art, and there are no strict
rules on how to do it. A process can be drawn in more or less detail depending on the goal of the
exercise. For example, the diagram below shows two pressers. If we were simply trying to
depict the steps an item of clothing must go through at the drycleaners, rather than also show
routing choices, we could replace these with a single box for pressing.
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Figure 1. Process-Flow Diagram for the Dry-Cleaning Process
Beige
Clothing
Order
Taking
Beige
Clothing
White
Clothing
Pre-treatment
Dark
clothing
White
Clothing
Dry Cleaning
Dark
clothing
Pressing
Customer
Pick Up
& Payment
Finished
Goods
Packing
Work In
Process
Work In
Process
Pressing
Legend
Material Flow
Information Flow
A lot of useful information about the process can be placed on the process-flow diagram,
the goal being easy visual communication. Suppose we know that it takes the first counter
worker three minutes to process each order and that on average an order is comprised of three
items of clothing. While some clothes need no pretreatment and others require a few minutes for
this step, on average the time the machine operator spends on pretreatment is about one minute
per item. Before clothes are loaded into the dry-cleaning machine, the machine operator needs to
switch the machine’s tanks, which hold the cleaning solvents, according to the color of the load.
If two successive loads are of different colors, it takes five minutes to change the tanks. If they
are of the same color, the tanks do not need to be changed. After switching tanks as needed, the
machine operator loads clothes into the machine. The maximum number of items that can be
loaded at once is 150. Rather than wait for a full load of items to accumulate, the operator loads
about 90 items in each machine cycle. The process of loading a batch of clothing, running the
machine, and unloading the cleaned clothes takes 55 minutes. Loading and unloading itself takes
negligible time, and the machine can run unattended. At the pressing station, it takes a presser
about 2.5 minutes to press an item of clothing. The packer can assemble and shrink wrap 40
orders per hour, which include three items each on average. The second counter worker takes
three minutes to retrieve a customer’s order and charge the customer. In Figure 2 this
information has been placed on the process-flow diagram.
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Figure 2. Drycleaners’ Process-Flow Diagram with Process Information
Beige
Clothing
Order
Taking
Beige
Clothing
White
Clothing
Pre-treatment
3 min per order
White
Clothing
1 min per item
Dark
clothing
Dark
clothing
Dry Cleaning
5 min to change tanks
55 min per 90 item load
Pressing
Customer
Pick Up
& Payment
3 min per order
Finished
Goods
Packing
40 orders per hour
Work In
Process
2.5 min per item
Work In
Process
Pressing
2.5 min per item
Legend
Material Flow
Information Flow
A glance at Figure 2 gives us an idea of the process flow and how much time it takes to
complete various stages of the process. It is important to remember that the times presented on
this diagram are actually average times. For example, it is highly unlikely that pressing will
always take exactly 2.5 minutes per item. Rather, 2.5 minutes is the average time per item for
pressing. Some items may take considerably longer to press than others. Although the average
hides this variability, working with averages gives us a way to start analyzing a process.
Before moving on to analyzing the dry-cleaning process, it is important to understand
how the process characteristics relate to the criteria that customers care about.
Order-Winning Criteria and Capabilities
What do customers at the drycleaner care about? Clearly, price and quality are important.
In addition, customers may care about the variety of services offered: for example, can one get
same-day service, or can one get shirts laundered or clothes altered at the same establishment?
Another issue customers care about is how readily the service, also known as delivery, is
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available. How early does the drycleaner open? Can one drop off and/or pick up clothes on the
way to work? Is there a long wait for service? Finally, because customers care about
innovation, does this drycleaner offer any new, innovative services that are unavailable
elsewhere?
Thus, customers care about price, quality, variety, delivery, and innovation. These
dimensions of value are known as order-winning criteria, as they enable firms to win customer
orders. As in any business, the drycleaner must create and deliver value along the dimensions its
customers care about.
For any process, an important aspect of creating and delivering value is to understand
what the process is currently capable of doing. In this note, we focus on three important aspects
of capability:
•
What is the capacity of the process? In other words, how many items or orders can it
process in any given time period?
•
How well is the capacity of the process being utilized?
•
How long does it take for an item or order to go through the process?
These aspects of process capability directly impact the order-winning criteria. For
example, if an item takes a long time to go through the dry-cleaning process, same-day
processing might not be possible, thus reducing the variety of services offered and delivery time.
Process Terminology
To answer the above questions about capability, we introduce some process terminology.
Batch: A batch is a group of items or orders that is processed at one time. For instance,
the machine operator loads 90 items at a time into the dry-cleaning machine, and each load is
called a “batch.” Often, successive batches will be different from one another. For example,
successive batches loaded into the dry-cleaning machine may differ by color. For example in
another industry such as insurance, an agent may first process a batch of home insurance claims
and then process a batch of auto insurance claims. Sometimes items will be batched together
only in some stages of a process. For instance, in the dry-cleaning process, the counter workers
process customers as they arrive, so in this stage the batch size is one. Furthermore, while the
clothes are batched by color for pretreatment and dry-cleaning, they are not batched for pressing.
Batching provides a way to spread the effort involved in completing a task over multiple items.
For instance, when you go grocery shopping, you typically buy many items in one trip to save
yourself from having to make multiple trips. In some situations, batching may make the task for
successive items in a batch easier due to repetition.
Batch Size: The batch size at a process stage is the number of items or orders processed
as a batch. For example, the normal batch size for the dry-cleaning machine is 90 items. The
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maximum batch size at this stage is 150 items. Sometimes, the production batch size at a
process stage can differ from the transfer batch size, which refers to the number of items
transferred all at once from one stage to the next.
Set-up Time: The set-up time at a process stage is the amount of time it takes to set up
or prepare before processing a batch of items. For instance, it takes the machine operator five
minutes to change the tanks in the dry-cleaning machine to match the solvent with the color of
the load. This comprises a set-up time. Examples of setting up in other process scenarios
include tasks like changing machine settings (e.g., changing the temperature of an oven),
lubricating a machine, gathering raw materials, and preparing the workplace for a different
product or service. Because set-up time is often incurred in order to change from working on
one type of product or service to another, it is also known as changeover time. Typically, the
time taken to set up will not change based on batch size. For example, at the drycleaner, no
matter what the batch size for the dry-cleaning machine, it still takes the machine worker five
minutes to change the tanks on the machine.
Run Time: The run time at a process step is the time taken to actually process an item at
that specific step1 independent of any set-up time that may be involved. For example, the run
time for the order-taking process is three minutes per order or one minute per item.
If items are batched at an operation, then run time can be computed for a batch of items.
For instance, the run time for the dry-cleaning machine is 55 minutes per 90-item batch. As it
happens, in the dry-cleaning example the run time for a batch does not vary with the batch size,
as long as the batch size is less than or equal to 150 items, the maximum load size. In other
situations, the run time for a batch may vary directly with the batch size. For example, when you
are filling out party invitations, you will likely spend a longer amount of time if you have more
guests on your list.
Capacity: The capacity of a resource is the maximum number of items that can be
processed by that resource in a given length of time. Workers, managers, production equipment,
testing equipment, and computers are all examples of resources. Often, but not always, a
resource will be associated with a particular stage of a process, so that one can also think about
the capacity at each process stage. At the drycleaner, for example, the tasks comprising the
order-taking stage are performed by a single resource, the counter worker.
Let’s compute capacity at each stage in the drycleaner example. At the order-taking
stage, the counter worker can process an item each minute, so capacity at this stage is 60 items
per hour. From its units, it is clear that capacity is a processing rate. It is, in fact, the maximum
processing rate at a process stage.
What about the next stage, “pretreatment”? Here, the average run time is one minute per
item. However, this task is performed by the machine operator, who also switches solvent tanks
1
We use the terms “step,” “stage,” and “operation” interchangeably.
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on the machine and loads and unloads it (although loading and unloading take negligible time).
Suppose at first that successive batches are always of different colors. For a batch of 90 items, it
takes the machine operator 90 minutes on average to pretreat them and 5 minutes to switch the
solvent tanks on the machine2. So the machine operator can process the batch of 90 items in 95
minutes. Therefore, her hourly capacity is: (90 items/95 minutes) x 60 minutes/hour = 56.8
items per hour. Notice that in this case we computed capacity for the machine operator, a single
resource used to accomplish two stages in the process: pretreatment and machine operation.
Also, notice that if consecutive batches were sometimes the same color, the machine operator’s
capacity would be greater. If the machine operator is actually working at capacity, he will be
busy all of the time. He will spend most of his time pretreating items, and the remaining portion
of his time switching solvent tanks on the dry-cleaning machine while loading, and unloading
batches.
When calculating capacity in any time period, it is important to remember that what we
are really calculating is the average capacity in that time period. For instance, the machine
worker may actually process fewer or more than 56 items in any particular hour, but on average
over many hours he will process 56.8 items per hour.
Capacity can be calculated similarly for automated stages. At the dry-cleaning machine,
5 minutes are spent switching solvent tanks and 55 minutes processing each load. Thus, it takes
one hour of machine time to run a load of 90 items, so the capacity is 90 items per hour. This
capacity calculation assumes that the solvent tanks are switched prior to every load. However, if
consecutive loads are sometimes of the same color, the machine capacity would increase because
the set-up time would be spread over a larger number of items. In fact, this gives us an intuitive
way to think about batch size: a batch is a group of items that require only one setup. So, for
example, if the shop always runs two consecutive loads of each color before switching to another
color, in effect the batch size is 180 items. Larger batches increase machine capacity.
For the dry-cleaning machine, the run time for a load of 1 item is the same as the run time
for a load of 150 items. Thus, batch size and machine capacity can also be increased by
increasing the size of each load. Of course, to determine what batch size is optimal, other factors
will need to be considered. For example, if there is a chance that the clothes waiting to be
pressed could gather dust or odors, then smaller batch sizes might be more suitable.
We found that the capacity of the dry-cleaning machine is 90 items per hour for a batch
size of 90 items assuming there are no consecutive batches of the same color. The number of
items that can be dry-cleaned per hour, however, is limited by the capacity of the machine
operator, which is only 56.8 items per hour.
2
Notice that pretreatment was not described as a batch process and involved no set-up time. Nevertheless,
thinking in terms of a batch of 90 items is helpful, since the machine operator, who performs the pretreating, must
switch tanks once for each batch of 90 items.
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At the pressing stage, each presser can process an item in 2.5 minutes. Therefore, the
number of items a presser can process in an hour is: (60 minutes per hour) / (2.5 minutes per
item) = 24 items per hour. So the capacity at the pressing stage, given two pressers, is 48 items
per hour. Similarly, the packer can process 40 orders per hour, with 3 items per order on average
or 120 items per hour. Finally, the second counter worker can process an order in three minutes.
Therefore, his capacity is 20 orders per hour or 60 items per hour. It is important to note that
these calculations assume that the two counter workers work only on their separate jobs and do
not cover for each other.
In Figure 3, which follows, we label the process-flow diagram to include this
information on capacity.
Figure 3. Run Time, Set-Up Time and Capacity at Each Stage in the Dry-Cleaning Process
Beige
Clothing
Order
Taking
Beige
Clothing
White
Clothing
Run Time: 1 min
Capacity: 60 items per hour
Pre-treatment
White
Clothing
Run Time: 1.06 min
Capacity: 56.8 items per hour
Dark
clothing
Dark
clothing
Dry Cleaning
Setup Time: 5 min
Run Time: 55 min per batch
Capacity: 90 items per hour
Pressing
Customer
Pick Up
& Payment
Finished
Goods
Run Time: 1 min
Capacity: 60 items per hour
Packing
Run Time: 0.5 min
Capacity: 120 items per hr
Work In
Process
Run Time: 2.5 min
Capacity: 24 items per hour
Work In
Process
Pressing
Run Time: 2.5 min
Capacity: 24 items per hour
Legend
Material Flow
Information Flow
Bottleneck: The bottleneck of a process is defined as the resource that limits production
or service delivery. What limits a drycleaner’s ability to process an order? The pressing station
can process at most 48 items per hour. Although every other stage in the dry-cleaning process
can handle more than 48 items per hour, clearly the process as a whole cannot operate any faster
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than the slowest stage in the process, which is pressing. This slowest stage is the bottleneck of
the process.
Why is capacity important? Capacity gives us an upper bound on the amount of revenue
that the operation can generate in an hour or a day. Knowing capacity also gives the drycleaner’s
management a sense of the extent to which its operations will be able to meet demand.
Capacity Utilization: Capacity utilization for any resource is defined as the ratio of the
amount of the resource used to the amount available. Suppose that, on average, 180 customers
drop off clothes to be dry cleaned in a typical 12-hour day. On any day, the first counter worker
is available for 12 hours or 720 minutes and spends 3 x 180 = 540 minutes processing orders.
Thus, his capacity utilization is 540 minutes / 720 minutes = 75 percent. Note that it is possible
that order taking is in fact operated on two shifts, with one employee taking orders on each shift.
This will not alter overall capacity utilization.
Next, consider the machine operator. How long does it take the machine operator to
process 180 orders or 540 items, assuming that other stages of the process do not interfere with
his ability to process orders? The machine operator takes 95 minutes to process a load of 90
items, so it will take him 95 x 6 = 570 minutes to process 6 loads (540 items/90 items per load).
The machine operator is available for 12 hours each day (or 720 minutes each day), so his
capacity utilization is 570 minutes / 720 minutes = 79.2 percent.
Capacity utilization must be calculated separately for the dry-cleaning machine. The
machine takes 60 minutes (one hour) to process a load of 90 items (5 minutes set-up time plus 55
minutes run time). Therefore, it can process a day’s orders or 540 items in six hours (540
items/90 items x 1 hour = 6 hours). Because the shop works one 12-hour shift, machine
utilization is 50 percent.
Recall that with a batch size of 90 items, daily machine capacity is (12 hours per day) x
(90 items per hour) = 1080 items per day. With 540 items dropped off per day, the portion of
machine capacity actually used is 540 items/1080 items = 50 percent. So for the dry-cleaning
process, dividing the number of items actually processed each day by the daily capacity at each
stage provides us with another way to compute utilization.
At the pressing station, the time needed to process a day’s orders or 540 items is 540
items x 2.5 minutes per item = 1350 minutes. The amount of time the pressers have available
each day is: 2 pressers x 720 minutes per day per presser = 1440 minutes. Therefore, capacity
utilization at pressing is 1350 minutes/ 1440 minutes = 93.8 percent.
Similarly, utilization can be computed at the remaining stages in the process. The
utilizations for packing and the second counter worker are 37.5 percent and 75 percent
respectively. It should be no surprise that the highest utilization occurs at the bottleneck pressing
stage.
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Cycle Time: The cycle time at a process stage is defined as inverse of the capacity at
that stage. For example, at the dry-cleaning business, the capacity for order taking is 60 items
per hour. Therefore, the cycle time for order taking is 1/60 hours per item or one minute per
item. The cycle time at a process stage is, in fact, the average amount of time that elapses
between the completions of successive items at that stage, assuming that the process stage is
operating at capacity.
Cycle time gives us a different way to think about capacity. In some settings, cycle time
can give us a way to picture the process. For example, at a car assembly line that operates
nonstop on an eight-hour shift and produces 480 cars per shift, the cycle time is 1/480 shifts per
car, or one minute per car (an eight-hour shift lasts 8*60 = 480 minutes, thus 480 minutes to
produce 480 cars gives a cycle time of one minute per car). We would, in fact, see a fully
assembled car roll off the line every minute.
Returning to the dry-cleaning example, at the pretreatment stage it becomes clear why
cycle time at a process stage represents the average time between the start of two consecutive
items at that stage. We found earlier that when no two consecutive batches are of the same color,
the machine worker can process a batch of 90 items in 95 minutes, so his capacity is (90/95) x 60
= 56.8 items per hour. Thus, his cycle time is 1/56.8 hours per item or 1.06 minutes per item.
While it takes one minute to pretreat an item, we find that the cycle time is actually longer than
one minute because the machine worker who pretreats the items also performs other tasks. In
most cases, the amount of time that elapses between the completion of the pretreatment of
successive items is one minute, the run time. Occasionally, when the machine operator switches
tanks on the dry-cleaning machine, the time elapsed from the completion of pretreatment of one
item to the completion of the next item is six minutes. On average over all items, the time that
elapses between the completion of pretreatment of one item and the next is 1.06 minutes.
Next consider the dry-cleaning machine. For a batch size of 90 with no consecutive loads
of the same color, its capacity is 90 items per hour. Therefore, its cycle time is 1/90 hours per
item or 0.67 minutes per item. Unlike the car-assembly example, cycle time here does not yield
an intuitive interpretation in that a piece of clothing is not finished every 0.67 minutes.
At the pressing station, capacity is 48 items per hour, so cycle time is 60 minutes/48
items or 1.25 minutes per item. Because each presser can process an item every 2.5 minutes, on
average, the amount of time between the completions of two consecutive items in the pressing
department is 1.25 minutes. Similarly the cycle time at the packing station is 0.5 minutes per
item. For the second counter worker, cycle time is three minutes per order or one minute per
item.
It turns out that the cycle time at the pressing station is longer than that at any other stage
in the dry-cleaning process. Again, this is no coincidence. The bottleneck of any process is the
stage or resource with the longest cycle time. The cycle time for the entire process is defined as
the cycle time of its bottleneck stage.
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Because cycle time is the inverse of capacity, if we know the cycle time of a process, we
can calculate its capacity. Having said this, one does not need to calculate cycle time in order to
calculate process capacity. In fact, in the previous dry-cleaning example, we calculated capacity
before even defining cycle time. Cycle time simply provides an alternative way to think about
bottlenecks and capacity.
Table 1 below summarizes the calculations we have done so far.
Table 1: Capacity and Capacity Utilization Calculations
Process
Stage or
Resource
Batch
size
(items)
Set-up
time
(minutes
per
batch)
Run time
(minutes
per item)
Daily
Capacity
(items per
day)
Daily
Demand
(items/day)
Capacity
Utilization
Cycle
Time
(minutes
per item)
Order
Taking
1
0
1
720
540
75%
Machine
operator3
(pre-treat +
tank
switching)
90
5
1
682.1
540
79.2%
1.064
1
Drycleaning
machine
90
5
555
1080
540
50%
0.67
Pressing
1
0
2.5
576
540
93.8%
1.25
Packing
1
0
0.5
1440
540
37.5%
0.5
Customer
Pickup
1
0
1
720
540
75%
1
Raw Materials: Materials that have not yet started being processed are referred to as
raw materials.
3
The capacity, capacity utilization, and cycle-time calculations for the machine operator reflect the fact that he
is responsible both for pretreatment and for the operation of the dry-cleaning machine.
4
Using cycle time = 1.06, the machine operator’s daily capacity is 720/1.06 = 679.2 items/day. The slight
discrepancy is due to a rounding error.
5
Run time is in minutes per batch for the dry-cleaning machine.
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Work-in-Process Inventory: Any items that are currently in process, either at a process
stage or between stages, are referred to as work-in-process inventory or WIP.
Buffer: A buffer is an area where work-in-process inventory can be stored.
Finished Products:
finished products.
Items that have been completely processed are referred to as
Blocking and Starvation: The clothes processed at each stage must be stored while they
wait to be processed at the next stage. For instance, clothes emerging from pretreatment must
wait to be dry-cleaned, and those emerging from dry-cleaning must wait to be pressed. If there
was very limited buffer space to store clothes that were between stages, then tasks preceding
pressing, which is the bottleneck stage, could at times be “blocked” by the bottleneck stage. For
example, if there were very little space to put the clothes that emerged from the dry-cleaning
machine prior to pressing, the dry-cleaning process would need to come to a standstill if the
pressers could not press the clothes fast enough. Gradually, tasks such as pretreatment and
eventually order taking could also come to a standstill. Similarly packing, the task following the
bottleneck, could at times be “starved” by the bottleneck in that the packing worker might be
standing idle if there were no items ready to be packed, because pressing is slower than packing.
Throughput Time: Throughput time or flow time is the time taken for a specific item,
job, or order to go through the entire process. For a customer with dry cleaning, a shorter
throughput time means that cleaned clothing is ready for pickup sooner. In manufacturing
settings, throughput time is also known as manufacturing lead-time.
Consider a stained item of clothing given to a drycleaner for service. Suppose for
simplicity that there is no work-in-progress at any stage in the process when this item arrived and
that the item will be processed through all stages in a batch of one. Then, the item will spend 66
minutes being processed: 1 minute (order taking) + 1 minute (pretreatment) + 5 minutes (to
switch solvents, assuming this is needed) + 55 minutes (dry-cleaning machine process) + 2.5
minutes (pressing) + 0.5 minutes (packing) + 1 minute (customer pickup and payment). The
throughput time for an item that encounters no work-in-process inventory is also known as rushorder throughput time, as it is the throughput time that would result if the item were expedited
through the process, while items that had arrived earlier were kept waiting.
If there are other items waiting for service when an item of clothing arrives at any step in
the process, the throughput time for that piece of clothing will be greater than 66 minutes, as it
will include the time spent waiting before being processed. In addition, in practice, the item of
clothing will spend extra time waiting to be batched into a full load for the dry-cleaning machine.
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Nuances to the Process
In this section, we introduce some nuances to the basic picture of the dry-cleaning
process painted thus far.
One such nuance involves stains that sometimes do not get removed completely during
the dry-cleaning process. If a presser finds a stain, then the item is returned to the pretreatment
stage. This rework affects the throughput time for that item, and it also impacts overall capacity.
If a large number of items need to undergo rework, the capacity of the drycleaner will be lower.
Another consideration is that our calculations assumed a steady stream of customers
throughout each day. In reality, there are more customers in the early morning between 7 a.m.
and 9 a.m., during lunch between 12 p.m. and 2 p.m., and after work between 4 p.m. and 7 p.m.
Additionally, Saturday is the most popular day for customers, followed by Friday and Monday.
This means the capacity utilization figures we calculated are only averages. Actual capacity
utilization will vary quite a bit during the day and the week.
Another source of complexity is the actual mix of orders received. For example, an
increase in the percentage of dresses in the product mix would result in more time needed for
pressing and would therefore decrease capacity.
Aside from the machine downtime required for changing solvent tanks, the machine is
also shut down for three hours each week for scheduled maintenance, and it also occasionally
breaks down. These occurrences reduce the time the machine is available for operation, so its
actual capacity would be less than the capacity calculated earlier.
Management Decisions
Managing a process involves making a lot of decisions. For example, management must
decide what types of services to offer. If utilization is very low, it may become costly to offer
same-day service because the dry-cleaning machine would need to run smaller batches. With
smaller batch sizes, the machine would have to be run more frequently, increasing utility costs.
However, if management decides not to offer same-day service, the business could lose
customers to competitors.
Changes in business conditions also impact process decision making. For example, the
owners of the dry-cleaning establishment may expect to see an increase in demand at their
location, because a nearby drycleaner run by a competitor is closing down. Management expects
that the number of customers dropping off clothes will increase to 240 per day, on average.
Does the drycleaner have enough capacity to meet this demand surge? If not, at which stages
will capacity need to be added?
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Management is also considering expanding the business by opening dry-cleaning stores
at other locations. If they choose to expand in this fashion, should the dry-cleaning process be
centralized at one store with other stores acting mainly as drop off and pick up points, or should
the process be installed at each store? How would either option impact capacity and throughput
time?
In the current process, capacity was lowest at the bottleneck stage, pressing. So if
demand increases, we would suspect that this would be the first stage to run out of capacity. In
fact, if demand increases to 240 orders per day, then the pressing station will not be able to keep
pace as it can only process 48 items per hour, which equals 48 x 12 = 576 items per day or 192
orders in a typical 12-hour day. Management would need to consider whether or not to buy an
additional press and hire another employee to meet the excess demand. Management might find
ways to avoid hiring another employee right away by reallocating tasks and providing additional
training to the current employees. For example, if the packer could be trained to press clothes in
his spare time, some additional pressing capacity could be generated.
Summary
In this note, we have introduced some basic metrics used to analyze any process and have
learned how to determine three important aspects of process capability: capacity, capacity
utilization, and throughput time.
The capacity of a process is the capacity of its scarcest resource. Therefore, to determine
capacity, one needs to determine the scarcest resource or slowest stage, known as the bottleneck.
The bottleneck can be found by calculating the capacity of every stage or resource and finding
the lowest capacity.
Capacity utilization provides managers with a sense of the extent to which the capacity of
an operation is being used. For any resource, it is the ratio of the amount of the resource used in
a period of time, to the amount of the resource available in that time period. Capacity utilization
is highest at the bottleneck stage or resource.
Throughput time varies for each item or order. If there are no other items in the process,
the throughput time for an item is computed by adding up the run times for the process stages,
being careful not to double count when stages occur in parallel. If there are other items in the
process, the throughput time includes waiting time.
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GLOSSARY
Batch: Group of items or orders processed at one time. A batch can also refer to a group of
items or orders that is transferred from one process stage to another at one time.
Batch Size: Number of items or orders processed as a batch.
Setup Time: Amount of time it takes to set up or prepare before processing a batch of items.
Run Time: Run time at a process step is the time taken to actually process an item at that
specific step independent of any set-up time that may be involved.
Capacity: Maximum number of items that can be processed by a resource in a given length of
time.
Bottleneck: Resource that limits production or service delivery.
Capacity Utilization: Ratio of the amount of a resource used to the amount available.
Cycle Time: Average amount of time that elapses between the completion of successive items
at a process stage, assuming the process stage is operating at capacity; also the inverse of the
capacity at that stage.
Raw Materials: Materials that have not yet started being processed are referred to as raw
materials.
Work-in-Process Inventory: Any items that are currently in process, either at a process stage
or between stages, are referred to as work-in-process inventory or WIP.
Buffer: Area where work-in-process inventory can be stored.
Finished Products: Items that have been completely processed are referred to as finished
products.
Blocking: Blocking occurs when a process stage comes to a halt due to the slower pace of the
stages following that stage. Once the buffer area following a stage is full, there is no place where
output can be placed to await further processing, resulting in blocking.
Starvation: Starvation occurs when a process stage comes to a halt due to the slower pace of the
stages preceding that stage. The starved stage essentially runs out of material to process.
Throughput Time: Time taken for a specific item, job or order to go through the entire process.
In manufacturing settings, throughput time is also known as manufacturing lead-time.
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