Content By Davis Balestracci

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By: Davis Balestracci

Davis Balestracci’s picture

By: Davis Balestracci

In spite of the overwhelming odds against me, every new year I firmly resolve to reignite my relentless passion about creating a critical mass of colleagues committed to practicing improvement as “built-in” to cultural DNA using data sanity.

Will this be the year you join me?

Here is a challenging road map of 12 synergistic resolutions for those of you willing to take this nontrivial risk.

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By: Davis Balestracci

Client A came to me for a consultation and told me upfront his manager would allow him to run only 12 experiments. I asked for his objective. When I informed him that it would take more than 300 experiments to test his objective, he replied, “All right, I’ll run 20.”

Sigh. No, he needed either to redefine his objectives or not run the experiment at all.

I never saw him again.

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By: Davis Balestracci

Referring back to June’s column, I hope you’ve found C. M. Hendrix’s “ways to mess up an experiment” helpful in putting your design of experiments training into a much better perspective. Today, I’m going to add two common mess-ups from my consulting experience. If you’re not careful, it’s all too easy to end up with data that’s worthless.

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By: Davis Balestracci

I hope this little diversion into design of experiments (DOE) that I’ve explored in my last few columns has helped clarify some things that may have been confusing. Even if you don’t use DOE, there are still some good lessons about understanding the ever-present, insidious, lurking cloud of variation.

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By: Davis Balestracci

Today I want to concentrate on the foundation of what is most commonly taught as design of experiments (DOE)—factorial designs.

Davis Balestracci’s picture

By: Davis Balestracci

In my last column I explained how many situations have an inherent response surface, which is the “truth.” However, any experimental result represents this true response, which is unfortunately obscured by the process’s common-cause variation. Regardless of whether you are at a low state of knowledge (factorial) or a high state of knowledge, the same sound design principles apply.