A Better Way to Do R&R Studies

Evaluating the measurement process approach

Last month’s column looked at how to fix some of the Problems with Gauge R&R Studies. This month I will show you how to learn more from your gauge repeatability and reproducibility (R&R) data with less effort. Rather than getting lost in a series of computations, the "evaluating the measurement process" (EMP) approach uses the power of the graph to reveal the interesting aspects of your data so that you can know how to ask the important questions.

An EMP study

The idea behind an EMP study is both simple and profound. As expressed by my friend and colleague, the late Richard Lyday, “Measurement is a process, and with rational subgrouping you can study any process.” An EMP study begins very much like a gauge R&R study, but rather than computing estimates of everything possible, it immediately places the data on an average and range chart in order to discover what is happening in the data.

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Comments

General Comment

Howdy, Don,

This comment is late in that I usually review these articles en masse, rather than weekly. There are several issues that I want to raise:

1) References: I know this is not a 'reviewed' journal and more of a forum for discussion, but a reference or two covering the basic source of the information would be helpful for those of us who like to investigate more. The only reference is EMP III which internally only references your other books. Actually, these concepts on 'test error' are part of the Classical Test Theory dating back to Spearmann and <1910. Even the notation used is taken from several books in the 50's and 60's. For those of us who do extra work, it would be appreciated that some references are given connecting us with the excellent works done before.
2) Probable Error needed? The standard deviation is nonparametric. Once a value of 0.675 is applied, the metric now becomes related to a normal distribution -- parametric. I looked at the prior articles and could not find a compelling reason to express all the charts as a function of PE over simply rescaling relative to the standard deviation. It would be nice to see why the PE concept is even needed. If you actually need the 25-75% range to be the basis, then perhaps use the Interquartile Range (IQR) which would also maintain the nonparametric basis.
3) One way to Assess a Gauge? This article does present alternate methods to improve on gauge capability assessments. Thanks for this. I also found problems with using the AIAG method specifically where the gauge is related to the specification. An 'excellent' gauge is found using 6Sigma(grr) / Spec Range < 0.10. This implies that the Spec range must be at least 60 Sigma(grr) which is excessive as the author has pointed out previously. Further, it cannot be really interpreted statistically - AND I can change my gauge capability by adjusting my specs or renegotiating the specs with the customer.

What I would like to add is that the method presented is more focused on internal process viewing -- detecting a process shift. An alternate assessment (used by calibration personnel) provides methods by which a gauge is assessed based more on a customer view. The methods describe guardbanding (and inferences about the gauge) with appropriate methods to quantify Consumer/Producer Risks. A good review is provided by David Deavers (Fluke) found at http://assets.fluke.com/appnotes/Calibration/ddncsl94.pdf. I have used those methods to set guardbands when we had gauges pushing the state-of-the-art technology, as well as calculating the impact of gauge improvement projects relating product costs, yields, etc. directly to measurement error improvements.