How to Conduct a Six Sigma 2k Factorial Experiment - dummies

How to Conduct a Six Sigma 2k Factorial Experiment

By Craig Gygi, Bruce Williams, Neil DeCarlo, Stephen R. Covey

Carrying out a well-planned 2k factorial experiment for Six Sigma is easy — it’s like falling off a log. You just have to roll up your sleeves and get into the scientific trenches.

Randomize: Safeguard against unknown nuisance factors

Despite your best efforts, external factors beyond the control of your selected experiment variables may creep in and influence the outcome of your experiment. These influences are factors (called nuisance factors) that you haven’t foreseen, but they have the potential to blur the clarity of your analysis and insights.

For example, in the ice cream carton filling process discussed in “Planning your experiment,” a rise in the ambient factory temperature while you’re conducting the experiment may affect the experiment outcomes and lead you to believe it’s a real effect from your selected experimental factors.

One way to compensate for these unknown nuisance variables is to randomize the order of your experimental runs. Doing so spreads out the potential for nuisance effects evenly and fairly over all of the experimental runs and preserves the validity of your results.

Always randomize the order of your experiment runs to reduce the risk of extraneous variables skewing the results of your analysis. Also, randomize your experiment materials, your personnel, and your equipment. The idea is to guarantee that only the effect of your selected factors is purposely concentrated during your experiment.

Block: Safeguard against known nuisance factors

When you know the source of nuisance variation, you can purposely include this nuisance effect in all your experimental runs. You guarantee that the nuisance factor won’t bias only a portion of your experimental settings. In the ice cream carton filling example, you may decide to perform each experimental run at the same time each day.

This way, the influences from different times of day are blocked from impacting only some of the experimental runs.

A catchy phrase may help you remember the roles of randomizing and blocking in your experiments: Block what you can and randomize against what you can’t block.

Perform the experiment and gather the data

Running the experiment is the fun part. All you have to do is follow your experimental plan.

There’s an added column to the coded design matrix after the Run column, showing the random order in which the experimental runs are conducted. We’ve also added a column on the far right to capture the outcome Y variable for each experimental run and assigned specific variables to the X values.

Run Order X1: Flavor X2: Time X3: Pressure Y
1 7 –1 –1 –1 1,238
2 2 +1 –1 –1 1,252
3 5 –1 +1 –1 1,228
4 8 +1 +1 –1 1,237
5 3 –1 –1 +1 1,223
6 6 +1 –1 +1 1,234
7 1 –1 +1 +1 1,238
8 4 +1 +1 +1 1,250