Number of Distinct Categories" or NDC Calculation in MSA Studies


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Hi Folks,

I wanted to start a discussion to see what others are thinking about the "number of distinct categories" or NDC calculation in MSA studies. The calculation is, per my interpretation of the MSA manual (see page 46), essentially an effort to improve on the old 10:1 rule (where gage discrimination should be at least 1/10 of the specification range).

So, AIAG seems to suggest that making gage discrimination 1/10 of process variation is an improvement over the old 1/10 of spec range rule. I suppose in some cases this may be true, but since the objective of most manufacturers is to reduce process variation, I would suggest that any calculation that carries the risk of penalizing your measuring system because the process variation is low is probably, to some degree, errant.

And this brings me to the subject of NDC. As a reminder, the formula for NDC is 1.41(PV/GRR). I'm sure many of you have experienced what I have, a measurement system with excellent discrimination that exceeds the old 10:1 rule but yields a low NDC anyway. And the reason the NDC is low is that the process variation is low.

I would further guess many of you, like me, have found that your actual R&R raw data has yielded 7,8,9,10 or even more different outcomes yet the NDC came out low anyway. Again, this is most likely because the process variation is very low. I have conversed with other quality professionals who have been as frustrated as I have been by such NDC results.

Some of those folks have taken to inducing variation into their studies, ie: machining parts to increase the variation in the sample set to achieve a better NDC to satisfy their customers. While that does work in many cases, what's the point? To satisfy the customer or to validate a measuring system?

In the old days (I've been at this a while) we used to count (yes, count) the number of distinct outcomes in our study and if the count exceeded 5 we were pretty happy (assuming we met the old 10:1 rule first). And usually we could further validate our measuring system by looking at the X-bar and R charts (bad measurement systems reveal themselves pretty quickly).

I recently had a case study where I had a rather unique measurement for which only one device was available, and the process capability was something in excess of Cpk=5.00, yet the NDC was low because the process did not vary much. And because the person evaluating the study was probably trained to view NDC as an absolute rather than an indicator, they still wanted it improved. (How much money you got to spend?)

So, I would put forth that the real objective of MSA is to have an adequate measurement system; the MSA manual does, in fact, refer to NDC as an indicator not an absolute. So what is really needed is better understanding of what constitutes an adequate measurement system. (Because anything more than adequate could be wasted money.)

So, care to share your thoughts, opinions, experiences with NDC?

By the way, for those of you keeping score, in the AIAG MSA manual, 4th edition, the index lists NDC references as: page 47 (correct); page 80 (blank page); pages 125 & 131 (NDC not specifically mentioned); page 216 (correct); page 218 (not mentioned); and page 227 (Index cover page). Anyone working in Quality Control over there at the AIAG?
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Statistical Steven

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NDC is a meaningless metric for the reason you state, and well as other reasons. The "process variation" part is key, so that if you have parts with low variability, your measurement system can be capable but still have a NDC=1. My biggest problem is many organizations worry more about testing costs and instrumentation costs, that they will take a lower discrimination gage that is barely capable.

The real issue about MSA studies is they do not reflect the actual use of methods in many cases. If you do a 2 operator x 10 parts by 3 repeats per part for your MSA, but when you use the method for release of product, it's one operator and one repeat, the MSA under estimates the true measurement variability. In other words, if you want a better NDC, you can measurement the same part 3 times and average them for a single reportable value.

Bev D

Heretical Statistician
Staff member
Super Moderator
Steven is correct, the NDC metric is mostly useless for many reasons. The biggest issue here is the delusional need for a 'bright line'. Unfortunately we are a culture that tries to score everything: if you have the highest score you win, your process is good if it is greater than this score and bad if it's less than this score (substitute Cpk, p values, NDC scores, RPNs, etc. for 'this score'). We no longer value thinking...

I wrote a brief paper (The Statistical Cracks in the Foundation of the Popular Gauge R&R Method) for publication within my organization that explains some of the weaknesses of the AIAG method for MSA as well as some alternative approaches. The paper is attached. There are also numerous references for you to perform your own research...

In brief I plot the MSA results (only 2 repeats as 3 repeats provides no better information on the repeatability variation than 2) on a Youden plot with the specification tolerances and I look at the data and then I think about what it means to my process...



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OK, Bev, took me longer to get through your document than I expected, but I guess that's normal enough in the quality biz. Some questions:

1) You say you wrote the paper for publication within your organization. Not sure the scope there, but has your paper been professionally peer reviewed and published? (If not it should be!) :applause:
2) I would like to try the Youden plot. Do you know of an Excel template that I might find somewhere? It seems to be hard to find.

I know there are those out there who are detractors to the Youden plot, and I don't think I'll get my customers to accept it, but I see the benefit to understanding my measurements systems for the sake of truly understanding my measurement systems.

Following the same thinking regarding sample size, I have been experimenting with determining the true standard deviation of my process and using that in the NDC calculation, and it seems to help. Of course, I usually need the MSA study before the true process variation is known.

Yea...that sound you hear is my head hitting the wall...:frust:

Bev D

Heretical Statistician
Staff member
Super Moderator
Try this MSA spreadsheet I posted some time ago.

You can find some other things I've posted easily at in the resources tab under practical quality engineering resources. You will have to register but it's free. It is a 'little sister' forum to the cove...

I have found that some Customers in the automotive world will accept the Youden approach if you have the conversation with them...the key is getting people to think about what is really happening and not relying on one dimensional statistical numbers
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Involved In Discussions
Thanks, Bev! The first link doesn't work, but I am already on Quality Forum (same forum name as here) and found your stuff there. Thanks for sharing all this. I think it will take me some time to digest all that but looks like some very good stuff you've posted!

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