Example of Measurement Systems Analysis

In this example, three operators measured the same five parts. See how the measurement system is performing, based on how much variation is found in the measurements.

1.	Select Help > Sample Data Library and open Variability Data/Gasket.jmp.

2.	Select Analyze > Quality and Process > Measurement Systems Analysis.

3.	Assign Y to the Y, Response role.

4.	Assign Part to the Part, Sample ID role.

5.	Assign Operator to the X, Grouping role.

Notice that the MSA Method is set to EMP, the Chart Dispersion Type is set to Range, and the Model Type is set to Crossed. See The Measurement Systems Analysis Window.

Click OK.

MSA Initial Report

The Average Chart shows the average measurements for each operator and part combination. In this example, the means of the part measurements are generally beyond the control limits. This is a desirable outcome, because it indicates that you can detect part-to-part variation.

The Range Chart shows the variability for each operator and part combination. In this example, the ranges are within the control limits. This is a desirable outcome, because it indicates that the operators are measuring parts in the same way and with similar variation.

The color coding for each part is shown in the legend below the charts.

7.	From the Measurement Systems Analysis red triangle menu, select Parallelism Plots.

Parallelism Plot for Operator and Part

The Parallelism Plots chart shows the average measurements for each part by operator. Because the lines are generally parallel and there is no major crossing, you conclude that there is no interaction between operators and parts.

Tip: Interactions indicate a serious issue that requires further investigation.

8.	From the Measurement Systems Analysis red triangle menu, select EMP Results.

EMP Results Report

The EMP Results report computes several statistics to help you assess and classify your measurement system. The Intraclass Correlation indicates the proportion of the total variation that you can attribute to the part.

From the EMP Results report, you can conclude the following:

•	The Intraclass Correlation values are close to 1, indicating that most of the variation is coming from the part instead of the measurement system.

•	The classification is First Class, meaning that the strength of the process signal is weakened by less than 11%.

•	There is at least a 99% chance of detecting a warning using Test 1 only.

•	There is 100% chance of detecting a warning using Tests 1-4.

Note: For more information about tests and detecting process shifts, see Shift Detection Profiler.

There is no interaction between operators and parts, and there is very little variation in your measurements (the classification is First Class). Therefore, you conclude that the measurement system is performing quite well.

Launch the Measurement Systems Analysis Platform

Launch the Measurement Systems Analysis platform by selecting Analyze > Quality and Process > Measurement Systems Analysis.

The Measurement Systems Analysis Window

The Measurement Systems Analysis window contains the following features:

Select Columns

lists all of the variables in your current data table. Move a selected column into a role.

MSA Method

select the method to use: EMP (Evaluating the Measurement Process) or Gauge R&R. This chapter covers the EMP method. For details about the Gauge R&R method, see the Variability Gauge Charts section.

Chart Dispersion Type

designates the type of chart for showing variation. Select the Range option or the Standard Deviation option.

Note: For the EMP method, the chart dispersion type determines how the statistics in the EMP Results report are calculated. If the Range option is selected, and you have a one factor or a two factor, balanced, crossed model, the statistics in this report are based on ranges. Otherwise, the statistics in this report are based on standard deviations.

Model Type

designates the model type:

‒	Main: variables with nominal or ordinal modeling types are treated as main effects.

‒	Crossed: the model is crossed when every level of every factor occurs with every level of every other factor.

‒	Crossed with Two Factor Interactions: the model is crossed when each level of two factors occurs with every level of the other factor.

‒	Nested: the model is nested when all levels of a factor appear within only a single level of any other factor.

‒	Cross then Nested (3 Factors Only): the factors are crossed and then nested for 3 factors.

‒	Nested then Crossed (3 Factors Only): the factors are nested and then crossed for 3 factors.

Options

contains the following options:

‒	Analysis Settings sets the REML maximum iterations and convergence.

‒	Specify Alpha specifies the 1-alpha confidence level.

Y, Response

is the column of measurements.

Part, Sample, ID

is the column designating the part or unit.

X, Grouping

is the column(s) representing grouping variables.

identifies a column that creates a report consisting of separate analyses for each level of the variable.