Gage R&R: Are 10 Parts, 3 Operators, And 2 Replicates Enough?

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Gage R&R: Are 10 Parts, 3 Operators,and 2 Replicates Enough?Most quality improvement projects have aclear goal, like reducing defects, improvinga response, or making a change that benefits customers. Teams often want to jumpright in and start gathering and analyzingdata so they can solve the problems.Checking measurement systems first, withmethods like Gage R&R, may seem like awaste of time.But a Gage R&R study is a critical step inany statistical analysis involving continuousdata, because it allows you to determine ifyour measurement system for that data isadequate or not.Gage R&R can help you answer questionssuch as: Can my measurement system discriminate between parts? Is the measurement system variabilitysmall compared to the manufacturingprocess variability? How much variability in my measurement system is caused by differencesbetween operators?You can also use Gage R&R to determine whereany weaknesses are. For example, you can useGage R&R to figure out why different operatorsreported different readings.The Standard Approach to Gage R&R“You take 10 parts and have 3 operatorsmeasure each 2 times.”If your measurement system can’t producereliable measurements, any analysis youconduct with those measurements is likelymeaningless. After all, if you can’t trustyour measurement system, then you can’ttrust the data it produces.This approach to a Gage R&R experiment isso common, so accepted, that few peopleever question whether it is effective. We’regoing to explore that in this paper.Visit 2017 Minitab Inc. All rights reserved.

Assessing a Measurement System with10 PartsThe good news is that it is roughly averagingaround the true value.First, let’s look at how accurately you canassess your measurement system with just10 parts.However, the distribution is highly skewed—adecent number of observations estimated%Contribution to be at least double the truevalue, with one estimating it at about six timesthe true value! In addition, the variation is huge.In fact, about 1 in 4 studies would have resultedin failing this gage.We’ll focus on the %Contribution for GageR&R,which tells us how much of the variation in yourprocess can be attributed to your measurementsystem. Anything under 1% is typically considered excellent, and 9% is poor.We simulated the results of 1,000 GageR&R studies with the following underlyingcharacteristics: There are no operator-to-operator differences, and no operator*part interaction.The measurement system variance andpart-to-part variance used would result in a%Contribution of 5.88%, between the popular 1% and 9% guidelines.Based on these 1,000 simulated Gage studies,what do you think the distribution of %Contribution looks like? Do you think it is centerednear the true value (5.88%), or do you think thedistribution is skewed? And if so, how much doyou think the estimates vary?The distribution, with the guidelines and truevalue indicated, is shown in the graph below.Now, a standard gage study is no smallundertaking. A total of 60 data points must becollected, and once randomization and “masking” of the parts is done, conducting the studyitself can be quite tedious (and possibly annoying to the operators).So just how many parts would we needto obtain a more accurate assessment of%Contribution?Assessing a Measurement System with30 PartsWe simulated another 1,000 gage studies, thistime using 30 parts (that’s a total of 180 datapoints). Then for good measure, we went aheadand simulated 1,000 gage studies using 100parts (600 data points). So now consider thesame factors from before for these counts—mean, skewness, and variation. The followinggraph shows the distributions of all three sets ofsimulations.Visit 2017 Minitab Inc. All rights reserved.

Mean is easy: if it was centered before, it’s probably centered still.Now let’s look at skewness and how much wewere able to reduce variation. Skewness andvariation have clearly decreased, but perhapsyou supposed variation would decrease morethan it did. Keep in mind that %Contribution isaffected by your estimates of repeatability andreproducibility as well, so increasing the numberof parts will only tighten this distribution by somuch. But still, even gage studies that use 30parts—which would be an enormous experimentto undertake—still result in this gage failing 7%of the time!So what is a quality practitioner to do?Here are two recommendations. First, consider%Process. Often we are evaluating a measurement system that has been in place for sometime, and we are simply verifying its effectiveness. In this situation, you can use the historicalstandard deviation as your estimate of overallvariation, instead of relying on your smallsampling of parts to come up with an estimate.This can eliminate much of the variation causedby the same sample size of parts.Now your output will include an additionalcolumn of information called %Process. Thiscolumn is the equivalent of the %StudyVarcolumn, but uses the historical standarddeviation (which comes from a much largersample) instead of the overall standard deviation estimated from the data collected in yourexperiment:A second recommendation is to include confidence intervals in your output. When usingMinitab for Gage R&R, this can be done in theConf Int subdialog:In Minitab’s Gage R&R tools, it’s a simple matterto enter your historical standard deviation in theOptions subdialog:Including confidence intervals in your outputdoesn’t inherently improve the wide variation ofestimates the standard gage study provides, butit does force you to recognize just how muchuncertainty there is in your estimate. For example, consider this output based on the gageaiag.mtw sample dataset included with Minitab, withconfidence intervals turned on:Visit 2017 Minitab Inc. All rights reserved.

For some processes you might accept this gagebased on the %Contribution being less than 9%.But for most processes you really need to trustyour data, and the 95% CI of (2.14, 66.18) is a redflag which indicates you really shouldn’t be veryconfident that you have an acceptable measurement system.Now let’s consider the other two factors in thestandard Gage experiment: 3 operators and2 replicates. What if, instead of increasing thenumber of parts in the experiment, you increasedthe number of operators or number of replicates?Again, we are interested in the effect on %Contribution for Gage R&R, the estimate of overallGage variation. Increasing operators will give youa better estimate of of the operator term andreproducibility, and increasing replicates wouldget you a better estimate of repeatability—but wewant to look at the overall impact on the assessment of the measurement system.OperatorsFirst we will look at operators. We performedtwo additional sets of simulations. In one, weincreased the number of operators to 4 andcontinued using 10 parts and 2 replicates, fora total of 80 runs. In the other, we increasedto 4 operators and still used 2 replicates, butdecreased the number of parts to 8 to get backclose to the original experiment size (64 runscompared to the typical 60).ReplicatesNow let’s look at replicates in the same manner.In one run of simulations, we will increase thenumber of replicates to 3 while continuing to use10 parts and 3 operators (for a total of 90 runs).In another, we will increase number of replicatesto 3 and operators to 3, but reduce parts to 7 tocompensate (which results in a total of 63 runs).Now we can compare the standard experiment toeach of these scenarios:The graph and output shown at the top of thenext column is a comparison of the standardexperiment and each scenario laid out here.It may not be obvious in the graph, but increasingto 4 operators while decreasing to 8 parts actuallyincreased the variation seen in despite requiring 4 more runs, this is a poorerchoice.Further, the experiment that involved 4 operatorsbut maintained 10 parts (for a total of 80 runs)showed no significant improvement over thestandard study.We see the same pattern we observed when weincreased the number of operators. Increasing to3 replicates while compensating by reducing to 7parts (for a total of 63 runs) significantly increasesVisit 2017 Minitab Inc. All rights reserved.

the variation seen in %Contribution. Increasing to3 replicates while maintaining 10 parts shows noimprovement.Conclusions about Operators andReplicates in Gage StudiesAs stated above, we’re only looking at the effectof these changes on the overall estimate ofmeasurement system error. So while increasingto 4 operators or 3 replicates either showed noimprovement in our ability to estimate %Contribution—or actually made it worse—you couldencounter a situation where you are willing tosacrifice that in order to get more accurate estimates of the individual components of measurement error. In that case, one of these designsmight be a better choice.What’s the best way tosample parts for GageR&R?Find out most situations, if you’re able to collectmore data, increasing the number of parts usedremains your best choice for obtaining betterestimates of %Contribution.However, this raises a separate question: How dowe select those parts.?This document compiles material first published on the Minitab Blog. The authors of the original postsare Michelle Paret, technical sales manager at Minitab, and Joel Smith, director of rapid continuousimprovement at Dr Pepper Snapple Group and former technical sales manager at Minitab.Visit 2017 Minitab Inc. All rights reserved.

You can also use Gage R&R to determine where any weaknesses are. For example, you can use Gage R&R to figure out why different operators reported different readings. The Standard Approach to Gage R&R “You take 10 parts and have 3 operators measure each 2 times.” This approach to a Gage R&am

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The Sources of Variation in a Gage R&R Study Average and Range Gage R&R Analysis ANOVA Gage R&R Analysis EMP Gage R&R Analysis Comparison of Results Summary Quick Links The Data The data we will use is from the 4th edition of the Measurement Systems Analysis manual published by AIAG. In this Gage R&R study, there are three operators and ten parts.File Size: 809KBPage Count: 10

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