Measurement Systems Analysis Overview
see also 

Minitab offers several commands to help you determine how much of your process variation arises from variation in your measurement system.

A good starting point is Type 1 Gage Study, which evaluates the capability of the measurement system. This study evaluates the effects of bias and repeatability using multiple measurements from a single part, and is conducted prior to the Gage R&R (Expanded), Gage R&R (Crossed) and Gage R&R (Nested) studies, which help to recognize any deficiencies in your measurement system. To help set up your worksheet for the Gage R&R (Crossed) or (Nested) studies, use Create Gage R&R Study Worksheet.

Other commands are:

·    Gage Run Chart, which helps you quickly assess differences in measurements between different operators and parts

·    Attribute Gage Study (Analytic Method), which examines measurement system precision of an attribute measurement system

·    Gage Linearity and Bias Study, which examines gage linearity and accuracy

·    Attribute Agreement Analysis, which helps assess the agreement of subjective ratings or classifications given by multiple appraisers for both nominal and ordinal data. Use Create Attribute Agreement Analysis Worksheet to help set up your worksheet

Any time you measure the results of a process you will see variation. This variation comes from two sources: differences between parts made by the process and imperfections in taking measurements. Thus, measuring the same part repeatedly does not result in identical measurements.

Statistical Process Control (SPC) is concerned with identifying sources of part-to-part variation, and reducing that variation as much as possible to get a more consistent product. But before you conduct any SPC analyses, you may want to check that the variation you observe is not mostly due to errors in your measurement system.

Measurement system error

Measurement system errors can be classified into two categories: accuracy and precision.

·    Accuracy describes the difference between the measurement and the part's actual value.

·    Precision describes the variation you see when you measure the same part repeatedly with the same device.

Within any measurement system, you can have one or both of these problems. For example, you can have a device that measures parts precisely (little variation in the measurements) but not accurately. You can also have a device that is accurate (the average of the measurements is very close to the accurate value), but not precise, that is, the measurements have large variance. You can also have a device that is neither accurate nor precise.

Accuracy

The accuracy of a measurement system is usually broken into three components:

·    Linearity - a measure of how the size of the part affects the accuracy of the measurement system. It is the difference in the observed accuracy values through the expected range of measurements.

·    Bias - a measure of the bias in the measurement system. It is the difference between the observed average measurement and a master value.

·    Stability - a measure of how accurately the system performs over time. It is the total variation obtained with a particular device, on the same part, when measuring a single characteristic over time.

To examine your measurement system's accuracy, see Gage Linearity and Bias Study.

Precision

Precision, or measurement variation, can be broken down into two components:

·    Repeatability - the variation due to the measuring device. It is the variation observed when the same operator measures the same part repeatedly with the same device.

·    Reproducibility - the variation due to the measurement system. It is the variation observed when different operators measure the same parts using the same device.

Choosing a command

To look at your measurement system's precision, see Gage R&R (Expanded), Gage R&R (Crossed) or Gage R&R (Nested).  

To look at a plot of all of the measurements by operator/part combination, and thus visualize the repeatability and reproducibility components of the measurement variation, see Gage Run Chart.

To look at both the bias and repeatability in an attribute measurement system, see Attribute Gage Study (Analytic Method).