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A measurement system analysis (MSA) is a thorough assessment of a measurement process, and typically includes a specially designed experiment that seeks to identify the components of variation in that measurement process. Just as processes that produce a product may vary, the process of obtaining measurements and data may also have variation and produce incorrect results. A measurement systems analysis evaluates the test method, measuring instruments, and the entire process of obtaining measurements to ensure the integrity of data used for analysis (usually quality analysis) and to understand the implications of measurement error for decisions made about a product or process. Proper measurement system analysis is critical for producing a consistent product in manufacturing and when left uncontrolled can result in a drift of key parameters and unusable final products.
MSA is also an important element of Six Sigma methodology and of other quality management systems. MSA analyzes the collection of equipment, operations, procedures, software and personnel that affects the assignment of a number to a measurement characteristic.
A measurement system analysis considers the following:
Selecting the correct measurement and approach
Assessing the measuring device
Assessing procedures and operators
Assessing any measurement interactions
Calculating the measurement uncertainty of individual measurement devices and/or measurement systems
Common tools and techniques of measurement system analysis include: calibration studies, fixed effect ANOVA, components of variance, attribute gage study, gage R&R,[1] ANOVA gage R&R, and destructive testing analysis.
The tool selected is usually determined by characteristics of the measurement system itself.
An introduction to MSA can be found in chapter 8 of Doug Montgomery's Quality Control book.
[2]
These tools and techniques are also described in the books by Donald Wheeler
[3]
and Kim Niles.
[4]
Advanced procedures for designing MSA studies can be found in Burdick et al.[5]
Management: training programs, metrology system, support of people, support of quality management system.
These can be plotted in a "fishbone" Ishikawa diagram to help identify potential sources of measurement variation.
^Measurement System in Manufacturing. Lean Six sigma. (n.d.). Retrieved from https://theengineeringarchive.com/sigma/page-measurment-systems.html.
^
Montgomery, Douglas C. (2013). Introduction to Statistical Quality Control (7th ed.). John Wiley and Sons. ISBN 978-1-118-14681-1.
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Wheeler, Donald (2006). EMP III: Evaluating the Measurement Process & Using Imperfect Data. SPC Press. ISBN 978-0-945320-67-8.
^
Niles, Kim (2002). Characterizing the Measurement Process in iSixSigma Insights Newsletter, Vol. 3, #42. ISSN 1530-7603.
^
Burdick, Richard K.; Borror, Connie M.; Montgomery, Douglas C. (2005). Design and Analysis of Gauge R&R Studies: Making Decisions with Confidence Intervals in Random and Mixed ANOVA Models. SIAM. ISBN 978-0-898715-88-0.
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