Measure Phase
Measurement System Analysis
Measurement System Analysis
Welcome to Measure
Process Discovery
Six Sigma Statistics
Measurement System Analysis
Basics of MSA
Variables MSA
Attribute MSA
Process Capability
Wrap Up & Action Items
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Introduction to MSA
So far we have learned that the heart and soul of Six Sigma is
that it is a data-driven methodology.
– How do you know that the data you have used is accurate and
precise?
– How do know if a measurement is a repeatable and
reproducible?
How good are these?
Measurement System Analysis
or
MSA
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Measurement System Analysis
MSA is a mathematical procedure to quantify variation introduced to
a process or product by the act of measuring.
Reference
Item to be
Measured
Measurement
Operator
Measurement Equipment
Process
Procedure
Environment
The item to be measured can be a physical part, document or a scenario for customer service.
Operator can refer to a person or can be different instruments measuring the same products.
Reference is a standard that is used to calibrate the equipment.
Procedure is the method used to perform the test.
Equipment is the device used to measure the product.
Environment is the surroundings where the measures are performed.
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Measurement Purpose
In order to be worth collecting, measurements must provide value that is, they must provide us with information and ultimately,
knowledge
The question…
What do I need to know?
…must be answered before we begin to consider issues of measurements,
metrics, statistics, or data collection systems
Too often, organizations build complex data collection and
information management systems without truly understanding how
the data collected and metrics calculated actually benefit the
organization.
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Purpose
The purpose of MSA is to assess the error due to
measurement systems.
The error can be partitioned into specific sources:
– Precision
• Repeatability - within an operator or piece of equipment
• Reproducibility - operator to operator or attribute gage to
attribute gage
– Accuracy
• Stability - accuracy over time
• Linearity- accuracy throughout the measurement range
• Resolution
• Bias – Off-set from true value
– Constant Bias
– Variable Bias – typically seen with electronic equipment,
amount of Bias changes with setting levels
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Accuracy and Precision
Accurate but not precise - On
average, the shots are in the
center of the target but there is a
lot of variability
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Precise but not accurate - The
average is not on the center, but
the variability is small
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MSA Uses
MSA can be used to:
Compare internal inspection standards with the standards of your
customer.
Highlight areas where calibration training is required.
Provide a method to evaluate inspector training effectiveness as
well as serves as an excellent training tool.
Provide a great way to:
–Compare existing measurement equipment.
–Qualify new inspection equipment.
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Why MSA?
Measurement System Analysis is important to:
• Study the % of variation in our process that is caused by our
measurement system.
• Compare measurements between operators.
• Compare measurements between two (or more) measurement
devices.
• Provide criteria to accept new measurement systems (consider
new equipment).
• Evaluate a suspect gage.
• Evaluate a gage before and after repair.
• Determine true process variation.
• Evaluate effectiveness of training program.
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Appropriate Measures
Appropriate Measures are:
• Sufficient – available to be measured regularly
• Relevant –help to understand/isolate the problems
• Representative - of the process across shifts and people
• Contextual – collected with other relevant information that
might explain process variability.
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Poor Measures
Poor Measures can result from:
• Poor or non-existent operational definitions
• Difficult measures
• Poor sampling
• Lack of understanding of the definitions
• Inaccurate, insufficient or non-calibrated
measurement devices
Measurement Error compromises decisions that affect:
– Customers
– Producers
– Suppliers
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Examples of What to Measure
Examples of what and when to measure:
• Primary and secondary metrics
• Decision points in Process Maps
• Any and all gauges, measurement devices, instruments, etc
• “X’s” in the process
• Prior to Hypothesis Testing
• Prior to modeling
• Prior to planning designed experiments
• Before and after process changes
• To qualify operators
MSA is a Show Stopper!!!
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Components of Variation
Whenever you measure anything, the variation that you observe can
be segmented into the following components…
Observed Variation
Measurement System Error
Unit-to-unit (true) Variation
Precision
Repeatability
Reproducibility
Accuracy
Stability
Bias
Linearity
All measurement systems have error. If you don’t know how much of the
variation you observe is contributed by your measurement system, you
cannot make confident decisions.
If you were one speeding ticket away from losing your license, how
fast would you be willing to drive in a school zone?
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Precision
A precise metric is one that returns the same value of a
given attribute every time an estimate is made.
Precise data are independent of who estimates them or
when the estimate is made.
Precision can be partitioned into two components:
– Repeatability
– Reproducibility
Repeatability and Reproducibility = Gage R+R
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Repeatability
Repeatability is the variation in measurements obtained with one
measurement instrument used several times by one appraiser
while measuring the identical characteristic on the same part.
Y
Repeatability
For example:
– Manufacturing: One person measures the purity of multiple samples
of the same vial and gets different purity measures.
– Transactional: One person evaluates a contract multiple times (over
a period of time) and makes different determinations of errors.
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Reproducibility
Reproducibility is the variation in the average of the
measurements made by different appraisers using the same
measuring instrument when measuring the identical
characteristic on the same part.
Reproducibility
Y
Operator A
Operator B
For example:
– Manufacturing: Different people perform purity test on samples
from the same vial and get different results.
– Transactional: Different people evaluate the same contract and
make different determinations.
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Time Estimate Exercise
Exercise objective: Demonstrate how well you can
estimate a 10 second time interval.
1. Pair up with an associate.
2. One person will say start and stop to indicate how
long they think the 10 seconds last. Do this 6 times.
3. The other person will have a watch with a second
hand to actually measure the duration of the estimate.
Record the value where your partner can’t see it.
4. Switch tasks with partner and do it 6 times also.
5. Record all estimates, what do you notice?
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Accuracy
An accurate measurement is the difference between the observed average
of the measurement and a reference value.
– When a metric or measurement system consistently over or under estimates
the value of an attribute, it is said to be “inaccurate”
Accuracy can be assessed in several ways:
– Measurement of a known standard
– Comparison with another known measurement method
– Prediction of a theoretical value
What happens if we don’t have standards, comparisons or theories?
True
Average
Warning, do not assume your
metrology reference is gospel.
Accuracy
Measurement
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Accuracy Against a Known Standard
In transactional processes, the measurement system can consist of
a database query.
– For example, you may be interested in measuring product
returns where you will want to analyze the details of the
returns over some time period.
– The query will provide you all the transaction details.
However, before you invest a lot of time analyzing the data, you
must ensure the data has integrity.
– The analysis should include a comparison with known
reference points.
– For the example of product returns, the transaction details
should add up to the same number that appears on financial
reports, such as the income statement.
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Accuracy vs. Precision
ACCURATE
BOTH
PRECISE
=
+
Accuracy relates to how close the
average of the shots are to the
Master or bull's-eye.
Precision relates to the spread of
the shots or Variance.
NEITHER
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Bias
Bias is defined as the deviation of the measured value from the
actual value.
Calibration procedures can minimize and control bias within
acceptable limits. Ideally, Bias can never be eliminated due to
material wear and tear!
Bias
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Bias
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Stability
Stability of a gauge is defined as error (measured in terms of
Standard Deviation) as a function of time. Environmental conditions
such as cleanliness, noise, vibration, lighting, chemical, wear and
tear or other factors usually influence gauge instability. Ideally,
gauges can be maintained to give a high degree of Stability but can
never be eliminated unlike Reproducibility. Gage Stability studies
would be the first exercise past calibration procedures.
Control Charts are commonly used to track the Stability of a
measurement system over time.
Drift
Stability is Bias characterized as
a function of time!
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Linearity
Linearity is defined as the difference in Bias values throughout the
measurement range in which the gauge is intended to be used. This tells
you how accurate your measurements are through the expected range of
the measurements. It answers the question, "Does my gage have the same
accuracy for all sizes of objects being measured?"
Linearity = |Slope| * Process Variation
Low
Nominal
High
+e
B i a s (y)
% Linearity = |Slope| * 100
-e
0.00
*
*
*
Reference Value (x)
y = a + b.x
y: Bias, x: Ref. Value
a: Slope, b: Intercept
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Types of MSA’s
MSA’s fall into two categories:
– Attribute
– Variable
Attribute
Variable
–
–
–
–
–
–
–
–
–
–
Pass/Fail
Go/No Go
Document Preparation
Surface imperfections
Customer Service Response
Continuous scale
Discrete scale
Critical dimensions
Pull strength
Warp
Transactional projects typically have Attribute based measurement
systems.
Manufacturing projects generally use Variable studies more often, but
do use Attribute studies to a lesser degree.
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Variable MSA’s
SigmaXL® calculates a column of variance components (VarComp) which are used to
calculate % Gage R&R using the ANOVA Method.
True Value
Measured Value
Estimates for a Gage R&R study are obtained by calculating the variance
components for each term and for error. Repeatability, Operator and Operator*Part
components are summed to obtain a total Variability due to the measuring system.
We use variance components to assess the Variation contributed by each source of
measurement error relative to the total Variation.
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Cheat Sheet
Contribution of Variation to the total
Variation of the study.
Use % Study Var when you are interested in
comparing the measurement system Variation to
the total Variation.
% Study Var is calculated by dividing each value in
Study Var by Total Variation and Multiplying by 100.
% Contribution, based on variance
components, is calculated by dividing each
value in VarComp by the Total Variation then
multiplying the result by 100.
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Study Var is calculated as 5.15 times the Standard
Deviation for each source.
(5.15 is used because when data are normally
distributed, 99% of the data fall within 5.15
Standard Deviations.)
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Cheat Sheet
SigmaXL® Report:
When the process tolerance is entered in the
system, SigmaXL® calculates % Tolerance
which compares measurements system
Variation to customer specification. This allows
us to determine the proportion of the process
tolerance that is used by the Variation in the
measurement system.
Distinct Categories
0.186980
1.41
0.031861517
 5.8685 1.41

8
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(Rounded Down )
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Number of Distinct Categories
The number of distinct categories tells you how many separate
groups of parts the system is able to distinguish.
Unacceptable for estimating
process parameters and
indices
Only indicates whether the
process is producing
conforming or
nonconforming parts
1 Data Category
Generally unacceptable for
estimating process
parameters and indices
Only provides coarse
estimates
2 - 4 Categories
Recommended
5 or more Categories
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AIAG Standards for Gage Acceptance
Here are the Automotive Industry Action Group’s
definitions for Gage acceptance.
% Tolerance
or
% Study Variance
% Contribution
10% or less
1% or less
Ideal
10% - 20%
1% - 4%
Acceptable
20% - 30%
5% - 9%
Marginal
30% or greater
10% or greater
Poor
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System is…
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SigmaXL® Graphic Output Cheat Sheet
Components of Variation
The SigmaXL® report breaks down the variation in the measurement system into specific
sources. The bar chart shown was created using Excel’s Clustered Column Bar Chart to
graphically display the Components of Variation. Each cluster of bars represents a source of
variation.
In a good measurement system, the largest component of Variation is Part-to-Part variation. If
instead you have large amounts of variation attributed to Gage R&R, then corrective action is
needed.
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SigmaXL® Graphic Output Cheat Sheet
SigmaXL® provides an R Chart and Xbar Chart by Operator. The R chart consists of the following:
- The plotted points are the difference between the largest and smallest measurements on each part for each
operator. If the measurements are the same then the range = 0.
- The Center Line, is the grand average for the process.
- The Control Limits represent the amount of variation expected for the subgroup ranges. These limits are
calculated using the variation within subgroups.
If any of the points on the graph go above the upper Control Limit (UCL), then that operator is having problems
consistently measuring parts. The Upper Control Limit value takes into account the number of measurements by
an operator on a part and the variability between parts. If the operators are measuring consistently, then these
ranges should be small relative to the data and the points should stay in control.
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SigmaXL® Graphic Output Cheat Sheet
SigmaXL® provides an R Chart and Xbar Chart by Operator. The Xbar Chart compares the part-to-part variation to
repeatability. The Xbar chart consists of the following:
- The plotted points are the average measurement on each part for each operator.
- The Center Line is the overall average for all part measurements by all operators.
- The Control Limits (UCL and LCL) are based on the variability between parts and the number of measurements in each
average.
Because the parts chosen for a Gage R&R study should represent the entire range of possible parts, this graph should
ideally show lack-of-control. Lack-of-control exists when many points are above the Upper Control Limit and/or below the
Lower Control Limit.
In this case there are several points out of control which indicates the measurement system is adequate.
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SigmaXL®’s Gage R&R Multi-Vari Output
The Multi-Vari Charts show each Part as a separate graph. Each Operator’s response readings are denoted as a vertical
line with the top tick corresponding to the Maximum value, bottom tick is the Minimum, and the middle tick is the Mean. The
horizontal line across each graph is the overall average for each part.
Ideally the connected means red line should be horizontal (i.e., small reproducibility) and the vertical lines should be short
(small repeatability).
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SigmaXL® Graphic Output Cheat Sheet
Means…
Pattern
Lines are virtually identical
Operators are measuring the
parts the same
One line is consistently
higher or lower than the
others
That operator is measuring
parts consistently higher or
lower than the others
Lines are not parallel or they
cross
The operators ability to
measure a part depends on
which part is being
measured (an interaction
between operator and part)
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Using the SigmaXL® two-way ANOVA tool
creates an interaction chart that shows the
average measurements taken by each operator
on each part in the study, arranged by part. Each
line connects the averages for a single operator.
Ideally, the lines will follow the same pattern and
the part averages will vary enough that
differences between parts are clear.
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SigmaXL® Graphic Output Cheat Sheet
The “By Part” Multi-Vari Chart allows us to analyze all of the measurements taken in the study arranged by
part. The measurements are represented by dots; the means by the middle bar. The red line connects the
average measurements for each part.
Ideally,
 Multiple measurements for each individual part have little variation (the dots for one part will be close
together)
• Averages will vary enough that differences between parts are clear
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SigmaXL® Graphic Output Cheat Sheet
The “By Operator” Multi-Vari Chart is created by modifying the X’s from parts to operator. This helps us
determine whether the variability in measurements are consistent across operators.
The by operator graph shows all the study measurements arranged by operator. Dots represent the
measurements; the middle bars represent the means. The red line connects the average measurements
for each operator.
If the red line is …
Then…
Parallel to the x-axis
The operators are measuring the parts similarly
Not parallel to the x-axis
The operators are measuring the parts differently
You can also assess whether the overall Variability in part measurement is the same using this graph. Is
the spread in the measurements similar? Or is one operator more Variable than the others?
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Practical Conclusions
For this example, the measuring system contributes little to the overall Variation, as
confirmed by both the Gage R&R table and graphs.
The Variation due to the measurement system, as a percent of study (Total) Variation
is causing 16.80% of the Variation seen in the process.
By AIAG Standards this gage should be used. By all standards, the
data being produced by this gage is acceptable, and valid for analysis.
% Tolerance
or
% Study Variance
% Contribution
10% or less
1% or less
Ideal
10% - 20%
1% - 4%
Acceptable
20% - 30%
5% - 9%
Marginal
30% or greater
10% or greater
Poor
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System is…
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Repeatability and Reproducibility Problems
Repeatability Problems:
•
•
Calibrate or replace gage.
If only occurring with one operator, re-train.
Reproducibility Problems:
•
•
•
Measurement machines
– Similar machines
• Ensure all have been calibrated and that the standard measurement
method is being utilized.
– Dissimilar machines
• One machine is superior.
Operators
– Training and skill level of the operators must be assessed.
– Operators should be observed to ensure that standard procedures are
followed.
Operator/machine by part interactions
– Understand why the operator/machine had problems measuring some parts
and not others.
• Re-measure the problem parts
• Problem could be a result of gage linearity
• Problem could be fixture problem
• Problem could be poor gage design
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Design Types
Crossed Design
•
A Crossed Design is used only in non-destructive testing and assumes that all the
parts can be measured multiple times by either operators or multiple machines.
– Gives the ability to separate part-to-part Variation from measurement system
Variation.
– Assesses Repeatability and Reproducibility.
– Assesses the interaction between the operator and the part.
Nested Design
•
A Nested Design is used for destructive testing (we will learn about this in MBB
training) and also situations where it is not possible to have all operators or machines
measure all the parts multiple times.
– Destructive testing assumes that all the parts within a single batch are identical
enough to claim they are the same.
– Nested designs are used to test measurement systems where it is not possible
(or desirable) to send operators with parts to different locations.
– Do not include all possible combinations of factors.
– Uses slightly different mathematical model than the Crossed Design.
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Gage R & R Study
Gage R&R Study
– Is a set of trials conducted to assess the Repeatability and
Reproducibility of the measurement system.
– Multiple people measure the same characteristic of the same set of
multiple units multiple times (a crossed study)
– Example: 10 units are measured by 3 people. These units are then
randomized and a second measure on each unit is taken.
A Blind Study is extremely desirable.
– Best scenario: operator does not know the measurement is a part of a
test
– At minimum: operators should not know which of the test parts they are
currently measuring.
NO, not that kind of R&R!
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Variable Gage R & R Steps
Step 1: Call a team meeting and introduce the concepts of the Gage R&R
Step 2: Select parts for the study across the range of interest
– If the intent is to evaluate the measurement system throughout the process
range, select parts throughout the range
– If only a small improvement is being made to the process, the range of interest is
now the improvement range
Step 3: Identify the inspectors or equipment you plan to use for the analysis
– In the case of inspectors, explain the purpose of the analysis and that the
inspection system is being evaluated not the people
Step 4: Calibrate the gage or gages for the study
– Remember Linearity, Stability and Bias
Step 5: Have the first inspector measure all the samples once in random order
Step 6: Have the second inspector measure all the samples in random order
– Continue this process until all the operators have measured all the parts one time
– This completes the first replicate
Step 7: Repeat steps 5 and 6 for the required number of replicates
– Ensure there is always a delay between the first and second inspection
Step 8: Enter the data into SigmaXL® and analyze your results
Step 9: Draw conclusions and make changes if necessary
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Gage R & R Study
Part Allocation From Any Population
10 x 3 x 2 Crossed Design is shown
A minimum of two measurements/part/operator is required
Three is better!
Trial 1
Operator 1
P
a
r
t
s
Trial 2
Trial 1
1
2
3
4
5
6
7
8
9 10
Operator 2
Trial 2
Trial 1
Operator 3
Trial 2
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Data Collection Sheet
Create a data collection sheet for:
– 10 parts
– 3 operators
– 2 trials
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The Data Collection Sheet
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Gage R & R
Open the worksheet “Gage AIAG2 - SigmaXL Format”.
Variables:
– Part
– Operator
– Response
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Gage R & R
Use 1.0 for the
tolerance.
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Graphical Output
Looking at the “Components of Variation” chart, the Part to Part Variation needs to be
larger than Gage Variation.
If in the “Components of Variation” chart the “Gage R&R” bars are larger than the “Part-toPart” bars, then all your measurement Variation is in the measuring tool i.e.… “maybe the
gage needs to be replaced”.
Part to Part
Variation needs to
be larger than
Gage Variation
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Graphical Output
The same concept applies to the “Response by Operator” chart. If there
is extreme Variation within operators, then the training of the operators
is suspect.
Operator
Error
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Session Window
I can see clearly now!
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Session Window
If the Variation due to Gage R & R is high, consider:
•
•
•
•
Procedures revision?
Gage update?
Operator issue?
Tolerance validation?
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• 20 % < % Tol GRR < 30%  Gage Unacceptable
• 10 % < % Tol GRR < 20 %  Gage Acceptable
• 1 % < % Tol GRR < 10 %  Gage Preferable
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Signal Averaging
Signal Averaging can be used to reduce Repeatability error when a
better gage is not available.
– Uses average of repeat measurements.
– Uses Central Limit theorem to estimate how many repeat
measures are necessary.
Signal Averaging is a method
to reduce Repeatability error
in a poor gage when a better
gage is not available or when
a better gage is not possible.
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Signal Averaging Example
Suppose SV/Tolerance is 35%.
SV/Tolerance must be 15% or less to use gage.
Suppose the Standard Deviation for one part measured by one person
many times is 9.5.
Determine what the new reduced Standard Deviation should be.
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Signal Averaging Example
Determine sample size:
Using the average of 6
repeated measures will
reduce the Repeatability
component of
measurement error to the
desired 15% level.
This method should be considered temporary!
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Paper Cutting Exercise
Exercise objective: Perform and Analyze a variable
MSA Study.
1. Cut a piece of paper into 12 different lengths that are all
fairly close to one another but not too uniform. Label the
back of the piece of paper to designate its “part number”
2. Perform a variable gage R&R study as outlined in this
module. Use the following guidelines:
– Number of parts: 12
– Number of inspectors: 3
– Number of trials: 5
3. Create a SigmaXL® data sheet and enter the data into the
sheet as each inspector performs a measurement. If
possible, assign one person to data collection.
4. Analyze the results and discuss with your mentor.
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Attribute MSA
A methodology used to assess Attribute Measurement Systems.
Attribute Gage Error
Repeatability
Reproducibility
Calibration
– They are used in situations where a continuous measure cannot
be obtained.
– It requires a minimum of 5x as many samples as a continuous
study.
– Disagreements should be used to clarify operational definitions
for the categories.
• Attribute data are usually the result of human judgment (which
category does this item belong in).
• When categorizing items (good/bad; type of call; reason for leaving)
you need a high degree of agreement on which way an item should
be categorized.
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Attribute MSA Purpose
The purpose of an Attribute MSA is:
–
–
–
–
To determine if all inspectors use the same criteria to determine “pass” from “fail”.
To assess your inspection standards against your customer’s requirements.
To determine how well inspectors are conforming to themselves.
To identify how inspectors are conforming to a “known master,” which includes:
• How often operators ship defective product.
• How often operators dispose of acceptable product.
– Discover areas where:
• Training is required.
• Procedures must be developed.
• Standards are not available.
An Attribute MSA is similar in many ways to the continuous MSA, including the
purposes. Do you have any visual inspections in your processes? In your experience
how effective have they been?
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Visual Inspection Test
Take 60 Seconds and count the number of times “F” appears in this
paragraph?
The Necessity of Training Farm Hands for First Class
Farms in the Fatherly Handling of Farm Live Stock is
Foremost in the Eyes of Farm Owners. Since the
Forefathers of the Farm Owners Trained the Farm Hands
for First Class Farms in the Fatherly Handling of Farm
Live Stock, the Farm Owners Feel they should carry on
with the Family Tradition of Training Farm Hands of First
Class Farmers in the Fatherly Handling of Farm Live
Stock Because they Believe it is the Basis of Good
Fundamental Farm Management.
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How can we Improve Visual Inspection?
Visual Inspection can be improved by:
• Operator Training & Certification
• Develop Visual Aids/Boundary Samples
• Establish Standards
• Establish Set-Up Procedures
• Establish Evaluation Procedures
– Evaluation of the same location on each part.
– Each evaluation performed under the same lighting.
– Ensure all evaluations are made with the same standard.
Look closely now!
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Attribute Agreement Analysis
Attribute MSA (Binary)
Attribute MSA is also known as Attribute Agreement Analysis. The
response must be binary (e.g. Pass/Fail, Good/Bad, G/NG, Yes/No).
1.
Open the worksheet Attribute MSA –
AIAG. This is an example from the
AIAG MSA Reference Manual, 3rd
Edition, page 127. Note that the
worksheet data must be in stacked
column format
2.
Click SigmaXL > Measurement
Systems Analysis > Attribute MSA
(Binary). Ensure that the entire data
table is selected. Click Next.
3.
Select Part, Appraiser, Assessed
Result and Reference as shown.
Check Report Information and enter
AIAG Example, Page 127.
4.
Click OK. The results are shown on
the next slide.
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Attribute MSA (Binary)
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M&M Exercise
Exercise objective: Perform and Analyze an Attribute MSA Study.
•
•
Number
Part
Attribute
1
M&M
Pass
2
M&M
Fail
3
M&M
Pass
You will need the following to complete the study:
–
A bag of M&Ms containing 50 or more “pieces”
–
The attribute value for each piece.
–
Three or more inspectors.
Judge each M&M as pass or fail.
–
The customer has indicated that they want a bright and shiny M&M
and that they like M’s.
•
Pick 50 M&Ms out of a package.
•
Enter results into SigmaXL®'s Attribute MSA Template and
draw conclusions.
•
The instructor will represent the customer for the Attribute
score.
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Summary
At this point, you should be able to:
• Understand Precision & Accuracy
• Understand Bias, Linearity and Stability
• Understand Repeatability & Reproducibility
• Understand the impact of poor gage capability on product quality
• Identify the various components of Variation
• Perform the step by step methodology in Variable and Attribute
MSA’s
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The Certified Lean Six Sigma Green Belt Assessment
The Certified Lean Six Sigma Green Belt (CLSSGB) tests are
useful for assessing Green Belt’s knowledge of Lean Six
Sigma. The CLSSGB can be used in preparation for the ASQ
or IASSC Certified Six Sigma Green Belt (CSSGB) exam or
for any number of other certifications, including private
company certifications.
The Lean Six Sigma Green Belt Course Manual
Open Source Six Sigma Course Manuals are professionally
designed and formatted manuals used by Belt’s during
training and for reference guides afterwards. The OSSS
manuals complement the OSSS Training Materials and
consist of slide content, instructional notes data sets
and templates.
Get the latest products at…
www.OpenSourceSixSigma.com
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