FabTime Cycle Time Management
Newsletter Abstracts - Volume 1 (8
Issues)
Single-path tools are a common feature
in wafer fabs. They occur whenever a single
tool is the only piece of equipment
qualified to process a particular
operation. During fab startup, the majority
of equipment will be single-path (since
only one tool of each type has been
purchased). As fab volume grows, and
duplicate tools are brought on-line, the
number of single-path tools is usually
reduced. At this point, however, there is
often a choice in how the duplicate tools
are configured -- cross-qualified in some
fashion, or dedicated to individual
operations.
In this article, we examine the impact
of this tool-dedication decision on the
number of single-path tools, and ultimately
on cycle time, using concrete numerical
examples and simple queueing
approximations. Based on our analysis, the
sample 100% dedicated-tool configuration
results in an average cycle time that is
nearly twice as long as the fully
cross-qualified configuration. We also
include a more intuitive explanation of the
advantages of cross-qualification, based on
other real-life examples.
While there are certainly other factors
affecting the cross-qualification decision,
our results suggest that if you do have a
legitimate choice between
cross-qualification and tool-dedication,
you should consider the cycle time benefits
of cross-qualification when making your
decision.
Discussion topics in this issue include a proposal for cycle time reduction through tool integration and a suggestion about using Production OEE.
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If you want to improve throughput for
your fab, you need to start with the
bottleneck (or bottlenecks), and work from
there. However, this is not necessarily
true when you’re trying to reduce
cycle time. We believe that you can reduce
overall cycle time by reducing cycle time
at any tool group in the factory.
The notion that you can improve overall
cycle times by reducing cycle time at the
bottleneck is obvious. And in fact, the
bottleneck is a good place to start cycle
time improvement efforts, since you
probably have a large queue there, and lots
of waiting time. The purpose of this
article is to point out that you can ALSO
reduce cycle time by making changes at
non-bottleneck tools. This is far less
obvious. With throughput, it doesn’t
matter if you process at a higher rate at
non-bottleneck tools, because things get
held up at the bottleneck anyway. Sometimes
this happens with cycle time, too. But not
always. We divide our discussion into three
cases: tools located after the bottleneck
in the process flow, tools located before
the bottleneck, and tools located between
visits to the bottleneck. We also include a
series of concrete, low-cost suggestions
for improving cycle time at non-bottleneck
tools.
Our overall point is very simple:
actions that you take to improve cycle time
at non-bottleneck tools often improve
overall product cycle times. For operations
located before the first visit to the
bottleneck, or after the last visit to the
bottleneck, the cycle time reduction leads
to an essentially direct reduction in the
overall cycle time. For intermediate
operations the situation is less clear, but
we believe that improvements here can
sometimes improve cycle time dramatically,
and in the worst case, will not make cycle
time any worse. If you focus your efforts
strictly on bottleneck tools, then, you
miss out on many opportunities for
improvement.
Discussion topics in this issue include: a question about tool performance vs. rate of return.
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Wafer fabs cost a lot of money. Fab
managers, therefore, are constantly under
pressure to run them well, so that the huge
investment in capital equipment is not
wasted. But what does it mean to run a
wafer fab “well”? In an ideal
world, we would be able to keep all of that
expensive equipment highly utilized, with
the utilization dedicated completely to
productive work. At the same time, we would
have low and predictable cycle times, and a
minimal amount of capital tied up in WIP.
We would keep our operators busy and
effective all of the time, so that we
weren’t wasting salary on having
people stand around the fab. We would
constantly improve our products, yet always
maintain 100% line yield. We would keep
costs down, but be able to charge high
prices by having speedy time to market.
Of course this combination of
circumstances is impossible for many
reasons. A wafer fab, as we discussed in
the early issues of this newsletter, is a
highly variable environment. In the
presence of variability, high utilizations
lead inevitably to high cycle time and WIP.
You can load your operators and your tools
heavily, or you can have low cycle time and
WIP. You can’t do both, unless you
stamp out variability.
So the question is, what performance
metrics should a fab manager use to make
sure things are on track? And after
deciding which to use, what are the correct
definitions to use for these metric? We
have observed, during our years of
consulting, that different people often
define the same metric differently. This is
a source of confusion when comparing
performance between or within companies.
When people talk about utilization, for
example, there are several things that they
might mean. Similarly for turns. We
therefore are proposing some definitions to
apply within our niche of cycle time
management. The terms defined in this
article include starts, utilization, OEE,
turns, throughput, line yield, cycle time,
cycle time/raw process time, and cycle time
per layer. We discuss each of these in
detail.
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This article is concerned with an
apparent conflict between an implication of
the Theory of Constraints (TOC) as applied
to wafer fabs and the application of
just-in-time manufacturing (JIT). One
implication of TOC is that utilization of
manufacturing resources should be
intentionally unbalanced. The result is an
identifiable bottleneck that is managed to
optimize the throughput-accounting
performance measures (throughput dollars,
operating expense, and inventory
dollars).
Just-in-time manufacturing refers to the
mindset spearheaded by Taiichi Ohno at
Toyota Motor Company. In an effort that
dates to the 1940’s, the company
developed and implemented a number of
improvement techniques aimed at two basic
goals:
- Just-in-time delivery of material
precisely when it is needed.
- Autonomation, or machines that are
both automated and fool-proofed.
JIT manufacturing techniques include
setup reduction, total quality management,
and kanbans. Kanbans in particular have
developed a strong association with
just-in-time manufacturing, which can cause
considerable confusion, since kanbans
require a more balanced line.
FabTime asks: Do the manufacturing
recommendations of the theory of
constraints (an unbalanced line being one
of these) conflict with just-in-time
manufacturing? We then reconcile
Jonah’s quote with Toyota’s
success by recognizing that both the theory
of constraints and just-in-time
manufacturing use WIP-limiting techniques -
the difference lies in the extent to which
these techniques are applied throughout the
factory.
We conclude that if you are going to
adopt a just-in-time manufacturing mindset,
or a goal manufacturing mindset, you should
set aside sufficient time to apply the
entire process. Saving time by skipping to
the answers (e.g. using existing
implementation techniques such as kanbans
or drum-buffer-rope) will likely result in
little long-term gain.
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The Theory of Constraints is now in its
fourth decade of development. In order to
install any scheduling system into a
complex job-shop environment (like a wafer
fab), Eli Goldratt discovered that it may
be necessary to first solve much deeper
basic problems. It is this insight that led
Goldratt to the concepts found in
“The Goal”, first published in
1984. Most people are introduced to the
theory of constraints via “The
Goal”, often at the urging of a
friend or colleague who has previously read
it. The book is a fast-moving novel that
considers the plight of Alex Rogo, a plant
manager whose factory is in deep
trouble.
The book outlines Alex’s
development (through the help of his
mentor, Jonah) of a series of performance
measures that, if improved, will result in
the factory meeting its goal. To improve
these performance measures requires a
sequential process of identifying the
bottleneck, improving the
bottleneck’s performance, and then
identifying the next bottleneck.
Eventually, Alex’s team learns that
not all bottlenecks are physical tools in
the factory, and that policy constraints
can cause bottlenecks too. The book
concludes with a systematic method for
identifying and attacking system
constraints (this is the theory of
constraints, or TOC). FabTime’s
write-up on the subject concludes with some
implications of TOC for wafer fabs.
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Little’s Law: The relationship
between cycle time, WIP, and
throughput
The relationship between cycle time and
WIP was first documented in 1961 by J. D.
C. Little. Little’s Law states that
at a given throughput level, the ratio of
WIP to cycle time equals throughput, as
shown in the formulas below:
Throughput = WIP / Cycle Time Cycle Time
= WIP / Throughput
In other words, for a factory with
constant throughput, WIP and cycle time are
proportional. Keep in mind that
Little’s Law doesn’t say that
WIP and cycle time are independent of start
rate. Little’s Law just says if you
have two of these three numbers, you should
be able to solve for the remaining one. The
tricky part is that cycle time and WIP are
really functions of the start rate. So
changing the start rate in fact changes all
three parameters, but Little’s Law
should hold for the new numbers.
Discussion topics in this issue include: reducing variability in observed process times.
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The Pollaczek-Khintchine (called P-K,
for obvious reasons) formula gives the
expected average WIP at a single-tool
workstation where arrivals to the
workstation are highly variable, and
process times are somewhat less variable.
More specifically, the formula applies when
interarrival times to the workstation are
exponentially distributed, and process
times follow a general distribution (what
is known as an M/G/1 queue). For tools that
fit this description, the expected WIP can
be easily computed from the mean
interarrival time, the mean process time,
and the variance of the process time
distribution.
The P-K formula tells us that, if we
look at individual tools in the fab,
anything that we can do to reduce
variability in the process times seen by
successive lots will directly act to reduce
WIP at these tools, without requiring a
reduction in tool loading. And, as will be
discussed in the next issue of the
newsletter, cycle time will go down at the
same time. The P-K formula is the
mathematical justification for variability
reduction efforts in a wafer fab.
Discussion topics in this issue include: contributors to wafer fab variability.
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The Hawthorne Effect is named after a
series of studies conducted at the Western
Electric Hawthorne plant in the early 20th
century. The initial aim of the studies was
to understand the impact of lighting levels
on worker productivity. As expected, the
first studies found that as lighting levels
increased, so did productivity. However,
researchers did a parallel experiment in
which lighting levels were decreased, and
found that productivity went up as the
light decreased, even when lighting was
very low. After conducting a number of
other related studies, the researchers
concluded that productivity increases as a
result of attention received by the
workers. This phenomenon is believed to be
due at least in part to the fact that work
is a group activity, and employees strive
for a sense of belonging (Hopp and
Spearman, Factory Physics, 1996).
This month’s subscriber discussion forum focuses on knowledge-sharing regarding lot release, dispatch, and scheduling techniques for cycle-time management.
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