Measurement Systems Analysis

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 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. MSA is 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 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 include: calibration studies, fixed effect ANOVA, components of variance, Attribute Gage Study, Gage R&R, ANOVA Gage R&R, Destructive Testing Analysis and others. The tool selected is usually determined by characteristics of the measurement system itself.

Factors affecting measurement systems

Factors might include:
* Equipment: measuring instrument, calibration, fixturing, etc
* People: operators, training, education, skill, care
* Process: test method, specification
* Samples: materials, items to be tested (sometimes called "parts"), sampling plan, sample preparation, etc
* Environment: temperature, humidity, conditioning, pre-conditioning,
* Management: training programs, metrology system, support of people, support of quality management system, etc

These can be plotted in a "fishbone" Ishikawa diagram to help identify potential sources of measurment variation.

ASTM

ASTM has several procedures for evaluating measurement systems and test methods, including:
* ASTM D4356 Standard Practice for Establishing Consistent Test Method Tolerances
* ASTM E691 Standard Practice for Conducting an Interlaboratory Study to Determine the Precision of a Test Method
* ASTM E1169 Standard Guide for Conducting Ruggedness Tests
* ASTM E1488 Standard Guide for Statistical Procedures to Use in Developing and Applying Test Methods

Auto Industry

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. [http://www.aiag.org/publications/quality/iatfquality.cfm 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.

Components of MSA

*Bias
*Stability
*Linear
*Repeatability and Reproducibility
*Attribute study
*Practical examples for calculating Bias, Stability, Linearity, Repeatability and reproducibility, Attribute study

ee also

* Metrology
* Measurement uncertainty
* Calibration
* ANOVA Gage R&R

References

*Wheeler and Lynday, "Evaluating the Measurement Process", SPC Press, ISBN-13: 9780945320067 ISBN: 094532006X

*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.