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Design of Experiments in Six Sigma

In the Improve stage of Six Sigma DMAIC, Six Sigma teams design and conduct experiments to investigate the relationships between input variables and response variables. By controlling and changing the input variables and observing the effects on the response variables, a Six Sigma team gains a deep understanding of their relationships. After determining what and how much needs to be changed to gain the desired improvement, teams generate solution ideas. This course surveys the concepts that are fundamental to the Design of Experiments methodology, including the basic elements of experiments: variables, factors and levels, responses, treatments, errors, repetition, blocks, randomization, effects, and replication. It also introduces and analyzes main effects, interaction effects, and their plots. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward their certification and to become productive members on their Six Sigma project teams.

Target Audience
Candidates seeking Six Sigma Green Belt certification; quality professionals, engineers, production managers, and frontline supervisors; and process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level

Expected Duration (hours)

Lesson Objectives

Design of Experiments in Six Sigma

  • recognize the benefits of Design of Experiments
  • match key DOE elements with examples
  • sequence the steps in the DOE process
  • recognize concepts related to using the Design of Experiments methodology
  • recognize examples of types of experimental error
  • determine whether a given experiment design is balanced
  • identify the benefits of randomization
  • recognize factors that should be blocked in a given scenario
  • distinguish between reasons for using replication and repetition
  • recognize the implications of choosing full and fractional factorial designs
  • use principles and techniques of DOE
  • recognize the principles for interpreting main effects plots
  • recognize which terms should be included in the model, based on results from a full factorial experiment
  • use principles and techniques of DOE



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