Measurement system analysis

A measurement systems analysis (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. (US Department of Agriculture,pp45)

A measurement systems 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 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 measurement 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
ASTM E2782 Standard Guide for Measurement Systems Analysis

ASME

ASME has several procedures and reports targeted at task specific uncertainty budgeting and methods for utilizing those uncertainty estimates when evaluating the measurand for compliance to specification.

B89.7.3.1 - 2001 Guidelines for Decision Rules: Considering Measurement Uncertainty Determining Conformance to Specifications
B89.7.3.2 - 2007 Guidelines for the Evaluation of Dimensional Measurement Uncertainty (Technical Report)
B89.7.3.3 - 2002 Guidelines for Assessing the Reliability of Dimensional Measurement Uncertainty Statements

Automotive industry

The measurement systems analysis process is defined in a number of published documents including the AIAG's MSA manual, which is part of a series of inter-related documents the AIAG controls and publishes. The Automotive Industry Action Group (AIAG) is a non-profit association of automotive companies founded in 1982. These manuals include:

The failure mode and effects analysis (FMEA) and Control Plan manual
The statistical process control (SPC) manual
The measurement systems analysis manual
The production part approval process (PPAP) manual

Goals

Measurement uncertainty
Accuracy and precision
Bias
Stability
Linearity
Repeatability and reproducibility
Attribute study
Practical examples for calculating Bias, Stability, Linearity, Repeatability and reproducibility, Attribute study...

References