An extended review on the series on building a MSA, this covers the 3rd step of analyzing the results by using the Gage R&R test.

1. Section & Lesson #:
Pre-Requisite Lessons:
Complex Tools + Clear Teaching = Powerful Results
MSA – Gage R&R Test
Six Sigma-Measure – Lesson 31
An extended review on the series on building a MSA, this covers the 3rd
step of analyzing the results by using the Gage R&R test.
Six Sigma-Measure #30 – MSA – Attribute ARR Test
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted by any means
(electronic, mechanical, photographic, photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.

2. Overview of Building the MSA
o Remember, there are 4 general steps for building the MSA:
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
2
1. Plan
the MSA
2. Conduct
the MSA
3. Analyze
the Results
4. Improve
measurement
system
This involves determining:
• What needs to be measured
• What MSA tools will be used
• Who will be part of the study
• What samples/master to use
• How to conduct the study
This involves:
• Scheduling the study
• Leading the study
• Collecting data from the study
This involves:
• Compiling all collected data
• Processing data in MSA tools
• Interpreting analysis results
This involves determining:
• If MS needs improvement
• How to improve the MS
• Redoing MSA (as needed)

3. 3. Analyze the Results
o Compiling all collected data
• As each trial of data is returned by each
operator, be sure to clearly define and
store them before compiling them.
• Once all data is collected from each operator from each trial, begin to compile the data.
 The method for compiling the data depends on the MSA tool to be used (based on below examples).
o Processing data in MSA tools and Interpreting Analysis Results
• There are two basic types of MSA tools we’ll review that depend on the data being analyzed:
 Discrete Data = Attribute ARR
 Continuous Data = Gage R & R
• The next few slides will review the Gage R&R tool and how to interpret the results.
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
3
1. Plan
the MSA
2. Conduct
the MSA
3. Analyze
the Results
4. Improve
measurement
system

4. 3. Analyze the Results: Gage R&R Tool
o Gage (Variable) R&R
• This MSA tool analyzes continuous data.
 There are 2 types of Gage R&R studies; the
difference depends on the parts used in the MSA:
– Crossed Gage R&R Study – use this when the same parts are used by each operator for each trial.
– Nested Gage R&R Study – use this when different parts are used by each operator for each trial.
 Neither study analyzes accuracy; they only target precision (repeatability & reproducibility).
 The below example is from the Minitab worksheet file “MSA Example 03.mtw”.
– This tool is not available in Minitab 14 Student Version.
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
4
1. Plan
the MSA
2. Conduct
the MSA
3. Analyze
the Results
4. Improve
measurement
system
Begin by setting up the data in a Minitab
worksheet as illustrated below…
…then go to Stat > Quality Tools > Gage Study > Gage
R&R Study (Crossed) and select the columns shown.
A reference for each item
being reviewed.
The “master” (correct
answer)
The answer from each
operator
A reference for each
operator
A reference for each trial
run. Minitab doesn’t
require this but it’s helpful
to track it in the data.

5. 3. Analyze the Results: Gage R&R Tool (cont’d)
o Gage (Variable) R&R (cont’d)
• Minitab outputs results in 2 forms:
 Session Window – this descriptively displays the statistical results of the MSA.
 Graphs Window – this visually displays the statistical results of the MSA.
• We’ll examine how to interpret the results from both of these types of outputs.
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
5
1. Plan
the MSA
2. Conduct
the MSA
3. Analyze
the Results
4. Improve
measurement
system
Session Window Graphs Window
Part-to-PartReprodRepeatGage R&R
100
50
0
Percent
% Contribution
% Study Var
1.0
0.5
0.0
SampleRange
_
R=0.080
UCL=0.206
LCL=0
1 2 3
78
75
72
SampleMean
__
X=75.762UCL=75.844LCL=75.680
1 2 3
2019181716151413121110987654321
78
75
72
PartNum
321
78
75
72
Operator
2019181716151413121110987654321
78
75
72
PartNum
Average
1
2
3
Operator
Gage name:
Date of study :
Reported by :
Tolerance:
Misc:
Components of Variation
R Chart by Operator
Xbar Chart by Operator
Answer by PartNum
Answer by Operator
Operator * PartNum Interaction
Gage R&R (ANOVA) for Answer

6. 3. Analyze the Results: Gage R&R Tool (cont’d)
o Gage (Variable) R&R (cont’d)
• Below is how to interpret the descriptive
display of the MSA’s results – shown in Minitab’s session window.
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
6
1. Plan
the MSA
2. Conduct
the MSA
3. Analyze
the Results
4. Improve
measurement
system
P-Value < 0.05 means there is a statistical difference. For
Operator to have 0.402 means there’s no statistical difference
between operators (as also represented by the % Contribution).
% Contribution identifies what factor(s) contributes to
the precision (variation) for the MSA. In this example,
the difference in parts make the greatest contribution.
Acceptance: <1% 1% - 10% >10%
% Study Variation & % Tolerance identify what % of a
certain type of variability is accounted for by a particular
source. This study should account for <30% of variation.
Acceptance: <10% 10% - 30% >30%

7. 3. Analyze the Results: Gage R&R Tool (cont’d)
o Gage (Variable) R&R (cont’d)
• Below is how to interpret the graphical
display of the MSA’s results – shown in Minitab as a separate graph.
o How do I interpret the results?
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
7
1. Plan
the MSA
2. Conduct
the MSA
3. Analyze
the Results
4. Improve
measurement
system
Part-to-PartReprodRepeatGage R&R
100
50
0
Percent % Contribution
% Study Var
1.0
0.5
0.0
SampleRange
_
R=0.080
UCL=0.206
LCL=0
1 2 3
78
75
72
SampleMean
__
X=75.762UCL=75.844LCL=75.680
1 2 3
2019181716151413121110987654321
78
75
72
PartNum
321
78
75
72
Operator
2019181716151413121110987654321
78
75
72
PartNum
Average
1
2
3
Operator
Gage name:
Date of study :
Reported by :
Tolerance:
Misc:
Components of Variation
R Chart by Operator
Xbar Chart by Operator
Answer by PartNum
Answer by Operator
Operator * PartNum Interaction
Gage R&R (ANOVA) for Answer
Stability
Repeatability
Product vs.
Measurement
Variation
Reproducibility

8. 3. Analyze the Results: Gage R&R Tool (cont’d)
o Stability & Repeatability Range Chart
• What does it do?
 Identifies within operator variability.
 Reflects variation between different measurements
made by the same operator for the same part used
by the same measurement tool/method.
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
8
1. Plan
the MSA
2. Conduct
the MSA
3. Analyze
the Results
4. Improve
measurement
system
Part-to-PartReprodRepeatGage R&R
100
50
0
Percent
% Contribution
% Study Var
1.0
0.5
0.0
SampleRange
_
R=0.080
UCL=0.206
LCL=0
1 2 3
78
75
72
SampleMean
__
X=75.762UCL=75.844LCL=75.680
1 2 3
2019181716151413121110987654321
78
75
72
PartNum
321
78
75
72
Operator
2019181716151413121110987654321
78
75
72
PartNum
Average
1
2
3
Operator
Gage name:
Date of study :
Reported by :
Tolerance:
Misc:
Components of Variation
R Chart by Operator
Xbar Chart by Operator
Answer by PartNum
Answer by Operator
Operator * PartNum Interaction
Gage R&R (ANOVA) for Answer
Part-to-PartReprodRepeatGage R&R
100
50
0
Percent
% Contribution
% Study Var
1.0
0.5
0.0
SampleRange
_
R=0.080
UCL=0.206
LCL=0
1 2 3
78
1 2 3
Gage name:
Date of study :
Report
Toleran
Misc:
Components of Variation
R Chart by Operator
Xbar Chart by Operator
Gage R&R (ANOVA) for Answer
Any data points outside the red control
limits indicate significant variation in that
data point’s measurement.
Compare the layout and shape of data
points between each trial to observe any
variation difference between the trials.

9. 3. Analyze the Results: Gage R&R Tool (cont’d)
o Part vs. Measurement Variation
and Reproducibility Xbar Chart
• What does it do?
 Identifies measurement system variation as
compared to part variation.
 Also identifies the difference between the
average measures of different operators.
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
9
1. Plan
the MSA
2. Conduct
the MSA
3. Analyze
the Results
4. Improve
measurement
system
Part-to-PartReprodRepeatGage R&R
100
50
0
Percent
% Contribution
% Study Var
1.0
0.5
0.0
SampleRange
_
R=0.080
UCL=0.206
LCL=0
1 2 3
78
75
72
SampleMean
__
X=75.762UCL=75.844LCL=75.680
1 2 3
2019181716151413121110987654321
78
75
72
PartNum
321
78
75
72
Operator
2019181716151413121110987654321
78
75
72
PartNum
Average
1
2
3
Operator
Gage name:
Date of study :
Reported by :
Tolerance:
Misc:
Components of Variation
R Chart by Operator
Xbar Chart by Operator
Answer by PartNum
Answer by Operator
Operator * PartNum Interaction
Gage R&R (ANOVA) for Answer
Part-to-PartReprodRepeatGage R&R
100
50
0
Percent
% Contribution
% Study Var
1.0
0.5
0.0
SampleRange
_
R=0.080
UCL=0.206
LCL=0
1 2 3
78
75
72
SampleMean
__
X=75.762UCL=75.844LCL=75.680
1 2 3
Date of study : Misc:
Components of Variation
R Chart by Operator
Xbar Chart by Operator
The data points should reflect
the same pattern between the
different operators.
The goal is to have 50% of the data
points outside the control limits (red
lines) in order to evaluate randomness.

10. Practical Application
o Open the “MSA Example 03.mtw” file in Minitab.
• Run the Gage R&R Analysis in Minitab as instructed in this lesson.
 Do your results look the same as those described in the lesson?
• Randomly select at least 10 of the values in the “Answer” column and change their values.
 Re-run the analysis and compare the results from that last set of results. How do they differ?
Copyright © 2011-2019 by Matthew J. Hansen. All Rights Reserved. No part of this publication may be
reproduced, stored in a retrieval system, or transmitted by any means (electronic, mechanical, photographic,
photocopying, recording or otherwise) without prior permission in writing by the author and/or publisher.
10