7/31/2019 Lean House Article DOE 080518

1/1

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.