Design of experiments (DOE or sometimes DoE), also known as “planned experimentation,” is an experimental method backed by statistics to help you make breakthrough product and process improvements while reducing your operations budget. See below for in-depth explanations on multifactor testing and how it works:

• Trimming the FAT out of Experimental Methods— a primer on DOE versus the traditional OFAT method of experimentation.
• Trimming the FAT: Part II—This follow-up article offers a case study that illustrates how a two-level factorial DOE can reveal a breakthrough interaction.
• Magic of Multifactor Testing Revealed by Fun Physics Experiment—a fun intro to design of experiments with a bouncy ball experiment.
• Eight Keys to Successful DOE—Using DOE successfully depends on understanding eight fundamental concepts which are explained in this article.

If you’re doing Six Sigma or Lean, DOE is a very important element in these methodologies. Learn more about how they interact with these articles:

• Design of Experiments—A Primer—This article looks at some of the basic DOE concepts that are necessary for practitioners to understand.
• The Six Sigma Method and Design of Experiments—This article by Peter Peterka (www.6sigma.us) explains what Six Sigma is and DOE's role in it.
• Cost-Effective and Information-Efficient Robust Design for Optimizing Processes and Accomplishing Six Sigma Objectives—Standard factorial designs (one array) offer a cost-effective and information-efficient robust design alternative to parameter designs (two array) made popular by Taguchi. This paper compares these two methods (one-array versus two-array) in depth via an industrial case study. It then discusses advanced tools for robust design that involve application of response surface methods (RSM) and measurement of propagation of error (POE). Also see this postscript to the article.
• Six Sigma for the Road—Read this Stat-Teaser newsletter article from Mark Anderson on using propagation of error (POE) and multiple response optimization to analyze a DOE on driving time.

First, install your copy (or download a trial version) of Stat-Ease software. Our best-in-class DOE tools are powerful, yet easy to use. With an intuitive interface and comprehensive annotation and tips, you’ll be able to apply these principles with a minimal learning curve. Then, start your learning with the below resources:

• Take our free Stat-Ease Academy eLearning courses for hands-on exploration of basic DOE topics.
• Grab DOE Simplified and/or RSM Simplified at your local book retailer for a practical walkthrough with exercises, explanations, and anecdotes.
• Watch the “DOE Crash Course for Experimenters” video on our YouTube channel, then explore the other hundreds of hours of DOE topics available.

I’m a formulator

Formulation work requires a specific type of DOE known as “mixture designs.” Factorial and RSM methods simply won’t work when you’re handling components of a mixture as opposed to independent factor variables. Learn more with:

• A Primer on Mixture Design: What’s In It for Formulators?—a "what's in it for me" introduction to encourage formulators to work through the more intense aspects of mixture design, response modeling, statistical analysis and numerical optimization.
• A Crash Course in Mixture Design of Experiments—this on-demand, recorded webinar by Stat-Ease President Martin Bezener, Ph.D. provides a compelling demo that lays out what makes mixture design of experiments (DOE) so effective for accelerating your formulation efforts. The fuel provided in this 1-hour webinar will kick-start your first mixture designed experiment.
• Formulation Simplified, the mixture design entry in the Simplified book series.

Still have questions?

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