What is Measurement Systems Analysis (MSA)?

Data analysis can be largely affected by measurement system error. Measuring instruments, inspection techniques of technicians and fixturing all make up a measuring system. Any of these components can introduce variation in the measuring system. This variation in the measuring system is reflected as part of total process variation in the SPC analysis and negatively affects key quality statistics like Cp, Cpk, Pp, Ppk, etc.

Every process contains inherent variation. The sources of variation can be related to any of the inputs from the mechanics of the process itself to raw material or personnel consistency. However, it is possible that the variation in the measurement process is a significant contributor to the overall variation of a process or is causing special causes of variation. This variation is analyzed during the Measurement Systems Analysis (MSA) studies, previously called Gage Reproducibility & Repeatability studies. MSA is a mandatory step in any quality control application and there are for example complete manuals like in TS16949 (Automotive) and RM13003 (Aerospace). For variable data two types of variation can be distinguished:

Precision:
Repeatability: Within an operator or piece of equipment
Reproducibility: Operator to operator or attribute gage to attribute gage
Accuracy:
Stability: Accuracy over time
Linearity: Accuracy throughout the measurement range
Resolution
Bias: Offset from true value

During MSA studies the repeatability and reproducibility are analyzed.

Gage Repeatability:

Does a measuring system report the same result when the same master is “blindly” measured several times by the same person? Can one operator be consistent in measuring the same part/characteristic using the same gage? Repeatability is also referred to as equipment variation, because it often reflects on equipment design or condition.

Gage Reproducibility:

Does a measuring system report the same results when two or three operators measure the same part/ characteristic “blindly” several times? Between-operator reproducibility or between gage reproducibility is the other dimension of variation in a measuring process. Gage reproducibility is also referred to as appraiser variation, because it most significantly reflects on operators’ consistency in using the measurement system. Solutions to problems of reproducibility often involve operator training.

What is Measurement systems analysis
Datalyzer grid icon variant 1

Calibration

Mandatory in ISO 9001

Datalyzer grid icon variant 2

10 to 30 %

Accepted Variation Error

Datalyzer grid icon variant 3

40% faster audits

With automated traceability

Datalyzer grid icon variant 4

< 3 months

From pilot to full rollout

Equipment and Appraiser Variation

MSA studies are usually made up of two components: equipment variation and appraiser variation. These components serve to focus any remedial attention on repairing or improving the gage itself (EV-equipment variation) or training operators to use the gage correctly or more consistently (AV-appraiser variation).

Either total tolerance (USL-LSL) or total process variation (6 sigma from long range SPC data) or total study variation (6 sigma of data collected in the gage study only) can be used for calculating the proportion used in measurement error.

Guidelines for acceptance of gage repeatability and reproducibility (R&R):

Under 10% error: measurement is acceptable.
0% to 30% error: measurement system may be acceptable depending on application importance cost of the gage, cost of repairs or improvement and existing process capability.
Over 30% error: measurement system needs improvement (depending on process capability); make every effort to identify problem and correct them.

Planning and executing Calibration and MSA studies requires software. Datalyzer offers a state-of-the art software solution which can be used as a SaaS solution or on premise. For more information check the MSA Gage Management software page.

SPC implementation at Gulf Can industries

How Gulf Cans Industries used Datalyzer to achieve stabilized and controlled processes

Gulf Cans Industries replaced their paper-based quality and process measurement records with Datalyzer, introducing Control Limits and its added value for Process Improvements