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MSA 2019-11-11

Bill Levinson

Industrial Statistician and Trainer
#21
None of that contradicts the fact that the origin of it all was the automotive industry. If you believe that the AIAG sponsors always do what makes sense, you might not have been paying attention for a long time.
My training is in industrial statistics so it certainly makes sense to me. AIAG also addressed (2019) the longstanding concern over risk priority number in FMEA (the product of three ordinal numbers) by developing a risk priority matrix. The newest manual is a major improvement on past approaches.
 
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Miner

Forum Moderator
Staff member
Admin
#22
HI Guys

Long time no talk. Nice to see you all in here
Hope you are all well?

We were asked to supply R&R Anova MSA study. Never done it. I've looked online and there are many tools available to purchase.
I've got OK from the boss so can you recommend some easy to use excel spreadsheet for reasonable price?
What are you using ?

Looking forward to your replies
Check out my MSA resources. There is an AIAG version MSA using ANOVA as well as a Dr. Wheeler Honest Gauge Study version, both of which are in MS Excel. If your customers do not force you to use the AIAG version, I recommend that you use the Dr. Wheeler version or the ones posted by BevD.
 

Bill Levinson

Industrial Statistician and Trainer
#23
Check out my MSA resources. There is an AIAG version MSA using ANOVA as well as a Dr. Wheeler Honest Gauge Study version, both of which are in MS Excel. If your customers do not force you to use the AIAG version, I recommend that you use the Dr. Wheeler version or the ones posted by BevD.
I have never done non-replicable, and your article on this looks interesting.
 

Bill Levinson

Industrial Statistician and Trainer
#24
Check out my MSA resources. There is an AIAG version MSA using ANOVA as well as a Dr. Wheeler Honest Gauge Study version, both of which are in MS Excel. If your customers do not force you to use the AIAG version, I recommend that you use the Dr. Wheeler version or the ones posted by BevD.

I tried Wheeler's Honest Gage Study (which uses ANOVA) on a data set I simulated for teaching purposes. The results (variances reported on the GRR report tab match the ones I got from StatGraphics for the same data. Minitab gets similar results. The nice thing about your spreadsheet is that it does everything automatically for the user, including the accompanying graphs.
 

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Moncia

Quite Involved in Discussions
#25
thank you guys

our results are really go/no go - so struggling in here to see the benefit of the whole exercise. but we had non-conformance and this is how our customer wants it closed. :(

might you have a copy of formatted excel spreadsheet we could use??
 

Bev D

Heretical Statistician
Staff member
Super Moderator
#27
AIAG uses the same methods that are seen in textbooks, ASQ certification review materials, and are also used by StatGraphics and Minitab. Also, AIAG has a vested interest in making sure they work as MSA is used to help quantify gage precision in automotive manufacture.
Funny I was having a very similar conversation in a class I was teaching yesterday. First AIAG’s ‘vested interest’ is only in making things easy for the auto industry. They have dumbed things down to simply make them easier for the Supplier quality engineers and managers so they can check the box. They have no real interest in making things ‘better’. ASQ has done the same thing with many of the methods they support in their certification exams. Thinking is hard. Putting numbers into a spreadsheet is easy. Certainly many of the ‘popular’ methods came about in the 40s and 50s before calculators and computers and EXCEL. So we got simple little ditties, because they were easy not because they were correct. If you read the articles of many of ASQ’s founders they were against this simplification. The recent moves by AIAG to eliminate the RPN is only a sign that they are hearing the critics after decades of turning a deaf ear. And they have only changed a little to go the severity/occurrence matrix. Occurence is still only a guess that is subject to human bias.

Remember that teh existing generally accepted thoughts at one time insisted that he earth was flat and teh sun revolved around the earth. Popular doesn’t mean correct. “The manipulation of mathematical formulas is no substitute fro thinking”.
 

Miner

Forum Moderator
Staff member
Admin
#28
thank you guys

our results are really go/no go - so struggling in here to see the benefit of the whole exercise. but we had non-conformance and this is how our customer wants it closed. :(

might you have a copy of formatted excel spreadsheet we could use??
When you say go/no-go, there are two possibilities:
  1. Measurements are taken in numerical form, but results are displayed/recorded as pass/fail (common in automated test equipment)
  2. Measurements are taken as pass/fail
In case #1, these methods still apply. You just have to dig for the actual measurements.
In case #2, you can use another approach called attribute agreement analysis. I believe that BevD has posted on this topic and should have some Excel templates uploaded.
 

Bill Levinson

Industrial Statistician and Trainer
#29
Funny I was having a very similar conversation in a class I was teaching yesterday. First AIAG’s ‘vested interest’ is only in making things easy for the auto industry. They have dumbed things down to simply make them easier for the Supplier quality engineers and managers so they can check the box. They have no real interest in making things ‘better’. ASQ has done the same thing with many of the methods they support in their certification exams. Thinking is hard. Putting numbers into a spreadsheet is easy. Certainly many of the ‘popular’ methods came about in the 40s and 50s before calculators and computers and EXCEL. So we got simple little ditties, because they were easy not because they were correct. If you read the articles of many of ASQ’s founders they were against this simplification.
My night school statistics professor told us that the sample standard deviation chart contains more information than the range chart, and I realized that the reason for the R chart was that it is much easier to subtract the smallest measurement from the largest than to calculate the standard deviation with a slide rule (and hand calculations for sums and differences also are required). People still use the R chart by habit today. I suspect that the median rather than x-bar chart was used for the same reason.

I think AIAG wants its methods to work, though, and has put a lot of effort into developing methodology for MSA and FMEA. I don't really like MSA for attributes regardless of whose method is used unless, for example, it involves a go/no go gage that is set to a specific dimension, in which case the equipment variation can actually be estimated. The AIAG manual, however, includes a correction factor for a specific sample size and I don't know how it was derived. I was however able to get the formulas for derivation of the d*2 factors so I am more comfortable with those.

I also investigated some of the math in ANSI/ASQ Z1.9, and some of the calculations involve the noncentral t distribution (which Minitab and StatGraphics will handle)--I am always happier when I understand how something works.
 

Bill Levinson

Industrial Statistician and Trainer
#30
When you say go/no-go, there are two possibilities:
  1. Measurements are taken in numerical form, but results are displayed/recorded as pass/fail (common in automated test equipment)
  2. Measurements are taken as pass/fail
In case #1, these methods still apply. You just have to dig for the actual measurements.
In case #2, you can use another approach called attribute agreement analysis. I believe that BevD has posted on this topic and should have some Excel templates uploaded.
In case 1, the equipment should be able to report the actual measurements; otherwise valuable information is being thrown away. There are however applications in which a go/no-go gage is set using gage blocks so its acceptance limit is known. If parts of known dimensions (e.g. as measured with micrometers or similar equipment) are put through the gage, and the acceptances and rejections counted for each dimension, then we get a good estimate of the chance of acceptance for each specific dimension. This can then be mapped onto a bell curve whose standard deviation (equipment variation) can be calculated. The AIAG MSA manual has a procedure and an example for this but it uses a specific sample size (trials per dimension as I recall) and a correction factor whose origin is unknown which means I can't replicate it for other sample sizes.
 
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