Dials for adjusting pressure, speed, and temperature
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Demystifying Design of Experiments

By Ron Pereira Updated on May 1st, 2026

I love Design of Experiments (DOE). Over the years, I have done my fair share of them – everything from simple 2^2 full factorial designs to your more complicated Response Surface Methodology designs.

In this article, I want to start by explaining what DOE’s are and what they are not. I will build on this topic of DOE over the coming months (and years). I don’t want to attempt a 3-part series or anything like this since this topic is monstrous.

OFAT – What is it?

OFAT stands for “one factor at a time”, and many people confuse this with DOE. OFAT problem-solving occurs when someone changes a single step in a process to see what happens. Let’s use a real-life example to explain. I experienced this exact situation when I visited a supplier for my former company. The names of the guilty will be omitted for obvious reasons.

During my tour of this supplier, I met an engineer who wanted to show me how he determined the optimal settings for his injection molding machine. He didn’t know I had a Six Sigma background, and I guess I forgot to mention it. I did later train this man on DOE during one of our “Supply Chain” green belt training classes so the story did end up nicely.

Anyhow, before his training on DOE, the nice engineer started off by showing how he tested “parameter 1” at the high, medium, and low settings. When he found the best one, he set it in place and did not change it. Then he tested “parameter 2” at the high, medium, and low settings. Once he found the best one, he set that parameter in place. So now he had the “best” settings for both parameters 1 and 2. He continued on until he had set all his parameters to the “optimal” settings. In the end, I will grant him, he had a good part. But he did tell me there was a lot of variation in the process and that their scrap rates were too high.

This engineer was using your traditional OFAT problem-solving approach. The problem with this technique is that you cannot determine how the various parameters interact with one another. It is this understanding of interaction that makes DOE so powerful.

Full Factorial Designs

full_factorial_design.JPGA better approach to identifying the optimal parameters would be to design a simple full factorial experiment. Let’s assume a screening experiment (a topic for another day) had been completed and the engineer had determined that there were three key parameters he needed to optimize for this process. Let’s call them pressure, speed, and temperature for the sake of simplicity. Let’s also assume he knows, from process knowledge, the realistic range each of these parameters operates within.

The picture is what this 2^3 (3 factors at 2 levels) full factorial designed experiment would look like. In this DOE, all possible combinations are tested, and thus any and all interactions would be seen. The output in this DOE may be something like the weight of the part. To be sure, much care must be taken in choosing the correct output. We also must be sure our measurement system is repeatable and reproducible, or we are just wasting our time.

This full factorial approach is far more effective than the aforementioned OFAT approach. In many cases, it is also quicker to complete!

In the future, I will build on this DOE topic more, explaining how to analyze them, etc. I have only begun to scratch the surface.

Learn More About DOE

Gemba Academy offers a full course on Design of Experiments as part of the School of Six Sigma. Additionally, you can get hands-on coaching in learning DOE and other statistical methods with Gemba Academy’s Black Belt Certification program.

Ron Pereira

Ron has more than 20 years of experience in various manufacturing, supply chain, and senior leadership roles. Ron has held the titles of process engineer, engineering manager, master black belt, and director of manufacturing & continuous improvement. In 2002 Ron was the first American to win the Nokia Global Six Sigma and Overall Quality award in Helsinki, Finland. Ron holds a bachelor's degree in automated manufacturing and an MBA in technology management.

  1. robert

    March 20, 2007 - 7:02 am
    Reply

    Ron – good luck with the posts on DoE. As you know its a massive topic. I came across a really interesting example of good experimental design over on Wikipedia.
    http://en.wikipedia.org/wiki/Design_of_experiments

    “In 1747, while serving as surgeon on HM Bark Salisbury, James Lind, the ship’s surgeon, carried out a controlled experiment to discover a cure for scurvy.” … “the main thing that is missing is randomized allocation of subjects to treatments”.

    Bizarre!

    Rob

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