7/31/2019 Lean House Article DOE 080518
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Inside the Lean House
And Homer says..
D.O.E Intelligent Experimentationby: Michael Coyne, SQA, Faurecia Automotive Seating Business Group
(This month I am switching to a subject more in
line with the other side of my training, Six Sigma)
I have heard it so many times. A supplier
explains to me their staff has undertaken to carry outa DOE experiment to determine the root cause of a
quality/productivity problem. When I enquire as tothe details of the experiment, I am told the team has
shutdown the production line to immediately start
experimentation. They will introduce small
incremental changes in one variable at a time whileholding all other variables steady. In an effort to
maintain stability, similar trials will be run one after
another. Due to the large number of variables to betrialed, only a single trial will be undertaken for each
variable. All outputs of the process will be recorded
for future reference. This is a great deal of work tofix the problem, so I should be happy, shouldnt I?
OFAT, I say.
So what exactly is this mysterious DOE
(pronounced D-O-E, not Doh!)? Well, one ofthe best characterizations Ive heard about DOE is,
If SPC listens to the process, DOE interrogates it.
Design of Experiment is a methodology that employsmatrix algebra and statistics to ensure clear and
tangible results come from experimentation.
DOE methodology started with the greatstatistician Sir Ronald Fisher, who refined it while
conducting genetics studies at the Rothamsted
Experimental Station in England and whosubsequently published his work in the book, The
Design of Experiments. In this book he details the
six criteria used to create efficient experimental
designs: Comparison; Randomization; Replication;Blocking; Orthogonality; factorial experimentation.
Comparison refers to the tactic of comparing
treatments one against the other. The best knownexample of this is the use of a placebo trial in
medical drug trials, thus establishing a baseline
response. Randomization refers to the tactic ofcompleting trials in random order, so no unknown
factor skews our test results. Replication refers to
carrying out multiple trials for a particular treatment
or set of parameter settings. This helps us to driveout error in the estimation of the process response
and to quantify the amount of variation in the
response. Blocking involves grouping sets of trialsinto blocks that can be compared against each other.
This helps us to understand irrelevant sources of
variation in the experiment. An example of this
would be grouping the participants in a medical drug
trial into male/female blocks. Orthogonality is amatrix algebra term that means terms are
independent of each other in determining theresponse of the process. What this means in simple
terms is that I can tell that a change in the process is
caused by this variable and not another one. The las
criteria, the use of factorial experiments instead ofOne-Factor-At-a-Time (OFAT) experiments
provides two key benefits, reducing the number of
trials and providing information on interactionsThis last point can be the key to understanding a
process. Some effects are only significant and
present when two or more variables are present andOFAT methods will never show these effects.
Quickly, the typical process for conducting a
DOE experiment is to first identify all possible
factors (variables) that could affect the output of theprocess. These factors can be categorized as
constants, noise and experimentals. A constan
factor is something that does not significantly varyand a noise factor varies, but we have no control over
it. After the experimental factors are identified
based on our expert knowledge of the process wewill choose those factors we wish to experiment on
The number of factors chosen is typically a function
of how many trials we can tolerate and afford. Oncethe factors are chosen, an appropriate design can be
selected based on whether we want to model the
process or just understand which factors have an
impact on the process. Some common designs usedare full factorial, fractional factorial, Taguchi, D
Optimal and Box-Wilson. With our design in hand
we determine the number of trials per treatmentrandomize the order in which they are carried out
run our variables at extreme maximum/minimum
values and start to collect the response data. Withthe response data in hand, we apply statistical tools
such as ANOVA and confidence intervals to validate
our results and understand them. Finally, we run
validation trials to confirm our models and optimizethe process.
Well, I have just barely touched on this subject
books have literally been written on it. But take myword for it; it pays to follow a low-OFAT diet.