A Measurement System Analysis, abbreviated MSA, is a specially designed experiment that seeks to identify the components of variation in the measurement. Just as processes that produce a product may vary, the process of obtaining measurements and data may have variation and produce defects. A Measurement Systems Analysis evaluates 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. MSA analyzes the collection of equipment, operations, procedures, software and personnel that affects the assignment of a number to a measurement characteristic. A Measurement Systems Analysis considers the following: selecting the correct measurement and approach, assessing the measuring device, assessing procedures & operators, assessing any measurement interactions, and calculating the measurement uncertainty of individual measurement devices and/or measurement systems. Common tools and techniques of Measurement Systems Analysis are: Attribute Gage Study, Gage R&R, ANOVA Gage R&R, and Destructive Testing Analysis. The tool selected is usually determined by characteristics of the measurement system itself. The Measurement Systems Analysis process is defined in a number of published documents including the AIAG's MSA (Measurement Systems Analysis) Manual, which is part of a series of inter-related documents the AIAG controls and publishes. These manuals include:
- The FMEA and Control Plan Manual
- The SPC (Statistical process control) Manual
- The MSA (Measurement Systems Analysis) Manual
- The Production Part Approval Process (PPAP) Manual
The AIAG (Automotive Industry Action Group) is a non-profit association of automotive companies founded in 1982. What is measurement system Measurement variation Components of MSA:
- Bias
- Stability
- Linear
- Repeatability and Reproducibility
- Attribute study
- Practical examples for calculating Bias, Stability, Linearity, Repeatability and
reproducibility, Attribute study Measurement uncertainty Reliable Source: Niles, Kim. (May 27, 2002). "Characterizing the Measurement Process". iSixSigma Insights Newsletter. Vol. 3, #42. ISSN: 1530-7603. Found at: http://www.iSixSigma.com/library/content/c020527a.asp. Same article re-published by ASQ San Diego. See http://www.asqsandiego.org/library.htm#Articles. Same article re-published by Soft Solutions. See http://www.softsolutionsit.com/news/articles/CharacterizingProcess.html.
