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Measurement System Analysis in Six Sigma

The adequacy and accuracy of measurement systems is vital to the success of any data collection and analysis, and therefore critical to the overall success of any Six Sigma initiatives in an organization. Measurement systems encompass devices, procedures, and the human element of measurement, which together must produce correct measurements and comply with appropriate standards. This course examines how to analyze a measurement system to help it produce correct measurements and minimize its proportion of variability in the overall process variability. It introduces key elements of measurement system capability using gauge repeatability and reproducibility (GR&R) studies, measurement correlation, bias, linearity, percent agreement, and precision to tolerance (P/T) ratio. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward their certification and 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

Measurement System Analysis in Six Sigma

  • distinguish between accuracy and precision
  • identify types of correlation
  • demonstrate your understanding of concepts related to measurement system analysis
  • match types of variation with corresponding characteristics
  • calculate the gauge repeatability and reproducibility value for a given set of data
  • interpret data from a given gauge repeatability and reproducibility study
  • calculate a precision to tolerance ratio
  • conduct a gauge repeatability and reproducibility study and calculate the P/T ratio, given a scenario
  • identify best practices in conducting a bias study
  • calculate and interpret bias, in a given scenario
  • conduct a bias study
  • identify attributes of linearity
  • calculate linearity and interpret a linearity plot
  • conduct a linearity study
  • recognize situations when you would use percent agreement to assess a measurement system
  • interpret numeric output from a given percent agreement analysis
  • recommend changes to a measurement system based on percent agreement findings



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