Message from discussion
A defense of Taguchi
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Message-ID: <9210291639.AA04477@procad.uicc.com>
Newsgroups: bit.listserv.stat-l
Date: Thu, 29 Oct 1992 11:39:24 EST
Sender: "STATISTICAL CONSULTING" <STA...@MCGILL1.BITNET>
From: "(sheldon haynie)" <s...@UICC.COM>
Subject: Re: A defense of Taguchi
Lines: 124
in a recent message Steve Simon, says
>>
>>April Milliken asks "if anyone can inform me as to why Taguchi
methods are
>>used." In particular, she is concerned that many of the methods
"exclude
>>interactions" and eliminate "potentially important information".
>>
>>I'm not an expert in Taguchi methods, but I hope I can come to his
defense.
>>Taguchi is in the company of some excellent statisticians like
George Box and
>>Jeff Wu when he proposes designs that have small sample sizes and
"exclude"
>>interactions. In many engineering problems, it is critical to gain
an
>>understanding of the simultaneous impact of many variables. There
is
>>susbstantial empirical and theoretical evidence that this is far
superior to
>>focussing on one factor at a time. In fact, it is far better than
doing a
>>very complete study of two, three, or four factors and all their
interactions.
>>
>>If you have a limited budget, you put your money (or your data)
where they
>>will give you the most information. In many engineering problems,
the size
>>of the main effects tends to be much larger than the size of the
>>interactions. The importance of the main effects tends also to be
much
>>larger than the importance of the interactions. It is often more
efficient,
>>more economical, and more logical to study 16 factors with no
interactions
>>than to study only 4 of these factors with all possible
interactions.
>>
>>When you find yourself in very complex settings or when you are
running the
>>first experiment in a series of experiments, experiments with many
factors
>>are especially useful. This is counter-intuitive: most people
would like to
>>run a small focussed experiment on a few factors (and all their
interactions)
>>to "get their feet wet". But the engineers know better: get a very
general
>>picture looking at all factors without worrying about interactions
and then
>>run the small focussed experiments based on that picture.
>>
>>There are times when estimating interactions are necessary, and
Taguchi
>>methods allow for estimation of interactions when needed.
>>
>>To be fair, Taguchi methods are not always without problems.
Technometrics
>>does an outstanding job of explaining the strengths and weaknesses
of Taguchi
>>methods: take a look at the May 1992 issue, for example.
>>
>>I like what Taguchi, Box, Wu and the engineers of the world have
done. Other
>>disciplines can learn from their experience and wisdom. I've been
promoting
>>these ideas with the biologists and toxicologists that I work
with.
>>
>>* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
>>* Steve Simon, Mail Stop C-22 * Delivering on the *
>>* Chief, Statistics Activity * nation's promise: *
>>* National Institute for Occupational * safety and health *
>>* Safety and Health * at work for all *
>>* 4676 Columbia Parkway * people...through *
As a development engineer for ~15 years I have to agree that
empirical results of
simple orthogonal "splits" gives a good initial "feel" for the
response of the system.
These splits are on the "major" variables that are either indicated
by physical models
or are those that are availble in a "black box" process.
We tend to use the "goldilocks" approach to selecting variable
values:
- too small
- too big
- just right
based on exisiting process data, simulation or available setpoints. I
typically
like to select hi/lo points that are likely to produce mean output
shifts of about
1.5 sigma to be "sure" of the actual repetition of the experiment.
Sometimes
that strategy is not selectable due to a lack of knowledge for a new
process.
These split conditions are randomized and the processes run for data
collection.
After initial run(s) depending on time and budget constraints
(sometimes time to do it over,
rarely time to do it "right") we will select the most "interesting"
variables and
design an experiment that focuses on the interactions and sequences.
(many times
the most important factor was the previous conditions :-( )
in the semiconductor industry, you rarely have the time to track all
possible
interactions of a 200+ step process, so some insight and "engineering
horse-sense"
as well as occasional blind MVA seems to be the only path.
Sheldon Haynie
Development Engineer |
Unitrode IC Corp | s...@simon.unh.edu (grad school M,W,F am)
7 Continental Blvd. |
Merrimack, NH 03054 |
FAX:603-424-3460 |voice:603-424-2410 (EST)|
s...@procad.uicc.com
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