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Design of experiments (DOE) is used to understand the effects of factors and interactions that influence the output of a process. It is designed to systematically build understanding and enhances how predictable a process is. DOE investigates a list of potential factors which could be derived from a variety of sources such as process maps, FMEAs, Multi-Vari studies, Fishbone Diagrams, brainstorming techniques, and Cause and Effect Matrices, whose variation might impact the process output. In typical data-analysis method, what happens in a process is observed without intervening. With a designed experiment, the process settings are changed to see its effect on the process output.

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Design of experiments refers to the structured way you change these settings so that the effects of changing multiple settings can be studied simultaneously. This active approach allows for effective and efficient exploration of the relationship between multiple process variables (x’s) and the output, or process performance variables (y’s). DOE is commonly used in the Analyze step of the DMAIC method as an aid in identifying and quantifying the key drivers of variation, and in the Improve step as an aid in selecting the most effective solutions from a long list of possibilities.

  • DOE easily identifies the “vital few” sources of variation (x’s) (i.e. the factors that have the biggest impact on the results).
  • DOE identifies the x’s that have little effect on the results.
  • It enables easy quantification of the effects of important x’s and their interactions.
  • It produces an equation that quantifies the relationship between the x’s and the y’s.
  • It predicts how much gain or loss will result from changes in process conditions.

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Types of DOEs

  • Screening DOEs: This type of DOE ignores most of the higher order interaction effects so that the team can reduce the candidate factors down to the most important ones.
  • Characterization DOEs: In this, main factors and interactions are evaluated to provide a prediction equation. These equations can range from 2k designs up to general linear models with multiple factors at multiple levels. Some software packages readily evaluate nonlinear effects using center points and also allow for the use of blocking in 2k analyses.
  • Optimizing DOEs: Optimizing DOEs uses more complex designs like Response Surface Methodology or iterative simple designs such as evolutionary operation or plant experimentation to determine the optimum set of factors.
  • Confirming DOEs: Here, experiments are conducted to ensure that the prediction equation matches reality.

Applications of DOE

Situations, where experimental design can be effectively utilized include the following: 

  • Choosing between alternatives.
  • Selecting the key factors affecting a response.
  • Response surface modeling to hit target, reduce variability, maximize or minimize a response, make a process robust (despite uncontrollable “noise” factors), seek multiple goals.

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About the Author

Adebayo is a thought leader in continuous process improvement and manufacturing excellence. He is a Certified Six Sigma Master Black Belt (CSSMBB) Professional and Management Systems Lead Auditor (ISO 9001, 45001, ISO 22000/FSSC 22000 etc.) with strong experience leading various continuous improvement initiative in top manufacturing organizations. 

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