GR&R (Gage R&R) as Superstitious Tradition

Bev D

Heretical Statistician
Staff member
Super Moderator
#21
hopefully somone will listen and drive AIAG and other standards towards logic and reason in measurement systems analyses
 
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Stijloor

Staff member
Super Moderator
#22
hopefully somone will listen and drive AIAG and other standards towards logic and reason in measurement systems analyses
The AIAG provides an opportunity for feedback about MSA 3rd Ed. on page 225.

The link to a specific page on the AIAG website is dead (so much for communication quality), but page 225 can be filled out and snail-mailed to:
Automotive Industry Action Group
Suite 200
26200 Lahser Road
Southfield, MI 48034

So if my Fellow Covers and experts/users of MSA conclude that for example Dr. Wheeler's approach is a better one, we should raise to the occasion and let the folks at AIAG know what we think.

Stijloor.
 
G

Geoff Withnell

#23
Just to add a little more fuel to the fire. Wheeler in his paper is looking at measurements as a method of detecting process shifts. And when measurements are used for this purpose, I fully agree with his viewpoint. That in NOT, however, the sole purpose of measurements. The GR&R method is to determine at accuracy of placing the measured units into conforming or nonconforming categories. Gage R&R is not about the process! It is about the unit under test, and how much confidence we can place in the determination that this unit is in fact either conforming or nonconforming based on our measurement. This is a completely different issue than what Wheeler is discussing. Notice that GR&R is based on the product tolerance. Since when did product tolerance have anything to do with process stability?

Geoff Withnell
 

Jim Wynne

Staff member
Admin
#24
That [detecting process shifts] in NOT, however, the sole purpose of measurements. The GR&R method is to determine at accuracy of placing the measured units into conforming or nonconforming categories.
I would phrase it differently; the purpose of GR&R is to confirm that a specific measurement system is appropriate to to the application, with the null hypothesis being that the system is appropriate. It's a type of controlled experiment.

Gage R&R is not about the process!
Of course it's about the process. Everything in manufacturing is about the process. If processes are properly controlled, the need for categorization of product conditions is eliminated, or at least greatly diminished.

It is about the unit under test, and how much confidence we can place in the determination that this unit is in fact either conforming or nonconforming based on our measurement.
This is indeed one of the purposes, but one we should be doing our best to avoid, and we do process control to avoid it.
This is a completely different issue than what Wheeler is discussing. Notice that GR&R is based on the product tolerance. Since when did product tolerance have anything to do with process stability?
If not for product tolerance, why would we even be concerned with process control, except perhaps for economic reasons? Tolerance and stability are not independent concepts, even though they may be evaluated independently.

Wheeler makes a basic assumption, and that is that process control is what's really important. A standard AIAG-type GR&R process, sans the questionable arithmetic, can be useful on a number of fronts. It can tell you something about variation between operators, whether it might be physically difficult to use a given device in a given application, etc. Most of what's useful about it can be accomplished through observation and simple charting of the data, without the onerous calculations. The simple fact is that given the AIAG acceptance criteria, much time and effort is wasted, and people are forced into choosing between wasting time and prevarication.

We need to be able to decide when a process shift has taken place so as to avoid using measurement systems to sort good from bad. By and large, the American automotive industry wants its suppliers to engage in statistical play-acting rather than rationally controlling their processes. Any move beyond the edges of the automotive template regardless of the statistical bona fides of the movement, is anathema. The industry is presently teetering precariously on the edge of oblivion because of this kind of thinking.
 

Bev D

Heretical Statistician
Staff member
Super Moderator
#25
I would add to Jim's comments that the AIAG math, specifically when applied to tolerances, and the use of the 3 arbitrary "% of tolerance" rules are not mathematically sound and are not particularly useful in determining if a specific system is useful for the application it is intended for. The use of the intraclass correlation coefficient and the Discrimintation Ratio CAN be used to determine if a system is useful for product acceptance.

Despite Wheeler's focus on process control and behavior charts, his points are well taken for product acceptance.

I still don't understand why people are clinging to AIAG math even when proven incorrect and arbitrary.
 
U

Umang Vidyarthi

#26
I would add to Jim's comments that the AIAG math, specifically when applied to tolerances, and the use of the 3 arbitrary "% of tolerance" rules are not mathematically sound and are not particularly useful in determining if a specific system is useful for the application it is intended for. The use of the intraclass correlation coefficient and the Discrimintation Ratio CAN be used to determine if a system is useful for product acceptance.

Despite Wheeler's focus on process control and behavior charts, his points are well taken for product acceptance.

I still don't understand why people are clinging to AIAG math even when proven incorrect and arbitrary.
A very healthy and fruitful discussion here so far,with plenty of inputs from the experts.I suggest that the 'Cove' may throw the ball in AIAG's court,get the final word from the horse's mouth,and bring this argument to a logical conclusion.

Marc and/or the Moderators may act as the torch bearers here.

/Umang :2cents:
 
G

Geoff Withnell

#27
Of course it's about the process. Everything in manufacturing is about the process. If processes are properly controlled, the need for categorization of product conditions is eliminated, or at least greatly diminished.


This is indeed one of the purposes, but one we should be doing our best to avoid, and we do process control to avoid it.

If not for product tolerance, why would we even be concerned with process control, except perhaps for economic reasons? Tolerance and stability are not independent concepts, even though they may be evaluated independently.

Wheeler makes a basic assumption, and that is that process control is what's really important. A standard AIAG-type GR&R process, sans the questionable arithmetic, can be useful on a number of fronts. It can tell you something about variation between operators, whether it might be physically difficult to use a given device in a given application, etc. Most of what's useful about it can be accomplished through observation and simple charting of the data, without the onerous calculations. The simple fact is that given the AIAG acceptance criteria, much time and effort is wasted, and people are forced into choosing between wasting time and prevarication.

We need to be able to decide when a process shift has taken place so as to avoid using measurement systems to sort good from bad. By and large, the American automotive industry wants its suppliers to engage in statistical play-acting rather than rationally controlling their processes. Any move beyond the edges of the automotive template regardless of the statistical bona fides of the movement, is anathema. The industry is presently teetering precariously on the edge of oblivion because of this kind of thinking.

Even if we are deciding with 100% accuracy that a process shift "has occured" (note the tense in your words) we must decide what to do with the material produced after the shift and before it was detected. and this is not about the process, it is about this material sitting with hold tags on it in the inspection area. "Everything in manufacturing" is NOT about the process until one eliminates shifts or detects them in zero cycles.

You also state "Most of what's useful about it [GR&R] can be accomplished through observation and simple charting of the data, without the onerous calculations." Really? Can you do observation and charting of data when GR&R is initially supposed to be happening, on the prototypes, during process planning, when the process itself does not yet even exist? The problem with GR&R as with most of PPAP and APQP, is that neither the suppliers nor the customer QEs are properly educated in what they are trying to accomplish.
As witness craziness such as doing GR&R (yearly, sometimes!!!) on measuring a simple turned OD with 1" mics.

Geoff Withnell
 

Jim Wynne

Staff member
Admin
#28
Even if we are deciding with 100% accuracy that a process shift "has occured" (note the tense in your words) we must decide what to do with the material produced after the shift and before it was detected. and this is not about the process, it is about this material sitting with hold tags on it in the inspection area. "Everything in manufacturing" is NOT about the process until one eliminates shifts or detects them in zero cycles.
Why do you assume that a process shift (i.e., a shift in the mean) means that nonconforming material has been produced, or that sorting is necessary when a shift has occurred? The idea behind being able to detect shifts in the mean is to evaluate the situation before nonconforming material has been produced. This is the underlying fallacy in the Six Sigma's 1.5-sigma shift: that there will be no realization that it's occurring before something bad happens.

You also state "Most of what's useful about it [GR&R] can be accomplished through observation and simple charting of the data, without the onerous calculations." Really? Can you do observation and charting of data when GR&R is initially supposed to be happening, on the prototypes, during process planning, when the process itself does not yet even exist?
Yes, so long as I have prototypes that are sufficiently analogous to design intent. And if you give me two operators, ten parts and the measurement data plotted on a simple line graph, I can tell you whether there's something to be concerned about, without the voodoo math. If there aren't enough prototypes, as is sometimes the case, then we should be able to rely on experience and good judgment, and actually knowing what operators are capable of.
The problem with GR&R as with most of PPAP and APQP, is that neither the suppliers nor the customer QEs are properly educated in what they are trying to accomplish.
As witness craziness such as doing GR&R (yearly, sometimes!!!) on measuring a simple turned OD with 1" mics.
I agree--that's one of the problems, but not the problem. If proper process control is in effect--and that's a basic APQP tenet--then I need for my measurement systems to be able to tell me what the process is actually doing, which is Wheeler's point. The AIAG math doesn't help much in that regard, and usually leads to waste, prevarication, and missing the point.
 
G

Geoff Withnell

#29
Why do you assume that a process shift (i.e., a shift in the mean) means that nonconforming material has been produced, or that sorting is necessary when a shift has occurred? The idea behind being able to detect shifts in the mean is to evaluate the situation before nonconforming material has been produced. This is the underlying fallacy in the Six Sigma's 1.5-sigma shift: that there will be no realization that it's occurring before something bad happens.
Why do you assume that parts from a process that is not in control (after the shift) do not need to be evaluated for conformance? I hold no brief for the 1.5 shift that orthodox Six Sigma uses either.

Yes, so long as I have prototypes that are sufficiently analogous to design intent. And if you give me two operators, ten parts and the measurement data plotted on a simple line graph, I can tell you whether there's something to be concerned about, without the voodoo math. If there aren't enough prototypes, as is sometimes the case, then we should be able to rely on experience and good judgment, and actually knowing what operators are capable of.
I prefer when trying to decide on whether a proposed process will in fact be capable, to have numbers. And for judging whether the measurement portion of a proposed process is going to make having a capable process unduely difficult, the GR&R is not "voodoo numbers". They work. If the prototypes are not sufficiently analogous to design intent, then what good are they? That's the whole point of prototypes.

I agree--that's one of the problems, but not the problem. If proper process control is in effect--and that's a basic APQP tenet--then I need for my measurement systems to be able to tell me what the process is actually doing, which is Wheeler's point. The AIAG math doesn't help much in that regard, and usually leads to waste, prevarication, and missing the point.
Wheeler does NOT say that the AIAG math doesn't help. He says that a 10% GR&R number is excessively conservative. So AIAG will work fine for controlling your process. Wheeler's method may or may not work fine for product characterization. I'll stick with AIAG, properly implemented without the prevarication.


Geoff Withnell
 

Jim Wynne

Staff member
Admin
#30
Wheeler does NOT say that the AIAG math doesn't help.
Quoth Dr. Wheeler:
... if you want to obfuscate and confuse the issues, if you want to needlessly condemn measurement systems, and if you want to continue to beat vendors over the head, then by all means continue to use the AIAG Gauge R&R Study.

Nevertheless, I think this is one of those subjects upon which reasonable people might disagree. I think we've given folks something to think about here, and maybe it will spur some into deeper consideration of MSA.

 
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