This document summarizes an attribute measurement system analysis (MSA) study. It defines MSA and its requirements, describes when to use attribute studies, and outlines the procedure for conducting an attribute MSA study. The document analyzes the results of an attribute MSA study, including effectiveness, false alarm rate, and miss rate. It provides acceptability criteria for these parameters and describes concluding the study by implementing improvements or corrective actions if needed.

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2. Attribute Measurement
System Analysis
Prepared by : VIPUL WADHWA
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3. ◦ Define MSA
◦ Requirement of MSA in IATF 16949
◦ Typical Reasons for MSA Study
◦ What is an Attribute study?
◦ When to use Attribute study?
◦ Define Procedure for conducting attribute MSA
◦ Demonstrate trial for conducting attribute MSA
◦ Types of Errors in Attribute Measurement system
◦ Analysis Technique
◦ Attribute MSA Study Conclusion
Objectives :-
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4. An experimental and mathematical method of determining
the amount of variation that exists within a
measurement process.
A Measurement systems analysis is an evaluation of the
efficiency of a measurement system.
It is applicable to both continuous and attribute data.
The sources of variation in a measurement process can
include the following:
What is MSA?
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5. 7.1.5.1.1 Measurement systems analysis
SHALL be conducted to analyze the variation present in the
results of each type of inspection, measurement, and test
equipment system identified in the control plan.
The analytical methods and acceptance criteria used SHALL
conform to those in reference manuals on measurement
systems analysis. Other analytical methods and acceptance
criteria may be used if approved by the customer.
NOTE: Prioritization of MSA studies should focus on
critical or special product or process Characteristics.
Requirement of MSA in IATF 16949
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6. • There is a new manufacturing process.
• There is a new product to manufacture.
• There is new equipment.
• There are Customer concerns.
• There are internal quality issues
Typical Reasons for an MSA Study
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7. Most problematic measurement system issues come from
measuring attribute data in terms that rely on human
judgment such as good/bad, pass/fail, etc. This is
because it is very difficult for all testers to apply the same
operational definition of what is “good” and what is
“bad.”
What is an Attribute study?
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8. • Used when measurement value is one of the finite
number of categories.
• When, we are not getting any measurement values then
the tool used for this kind of analysis is called Attribute
study.
• Commonly use Attribute study is Go/ No- Go gauge
When to use Attribute study?
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9. • Select at least 20 parts to be evaluated during the study.
• To evaluate product features and make accept/reject decisions (True decision).
• At least 5 of the parts should be defective in some way. If larger
sample sizes are used, include at least 25% defective parts.
• Care should be taken when selecting defective parts – If possible select
parts which are slightly beyond the specification limits. Label each
part with proper identification.
• Three inspectors will evaluate each part thrice (Three trials).
• A fourth person should record the data. Note down the observations
in the form of G or B, G is Good part (OK), B is for Bad part (not ok).
Note: The order of inspections should be randomized after each group of
inspections to minimize the risk that the inspector will remember
previous accept/reject decisions. The inspectors must work independently
and cannot discuss their decisions with each other.
Procedure for conducting Attribute MSA
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10. • The data recorder may use a table similar to the one given below.
G Good
B Bad
PART NO. & NAME :- DATE:- APPRAISER NAME
SPECIFICATIONS:- GAUGE NAME:- A :- Mr. A
No. of parts (n) :- 20 GAUGE NO. :- B:- Mr. B
NO. OF TRIALS(r):- 3 PERFORMED BY:- C:- Mr. C
DATA COLLECTION
PART
NO.
TRUE
Decision
APPRAISER-A APPRAISER-B APPRAISER-C
1 2 3 1 2 3 1 2 3
1 B B B B B B B B B B
2 B B B B B B B B B B
3 G G G G G B G G G B
4 G G G G G G G G G G
5 G G G B G G B G G B
6 G G G G G G G G G G
7 G G G G G G G G B G
8 G G G G G G G G G G
9 B B B B B B B B B G
10 G B G G G G G G G G
11 G G G G G G G G G G
12 G G G G G G G G G G
13 G G G G G G G G G G
14 G G G G G G G G G G
15 B B B B B B B B B B
16 G G G G G G G G G G
17 G G G B G G G G G G
18 B B B B B B B B B B
19 G G G G G G G G G G
20 B B B B B B B B B B
Procedure for conducting Attribute MSA
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11. Type 1 Errors : When a Good part is rejected.
• Type 1 errors increase manufacturing Costs. Incremental labor and material expenses are
necessary to re-inspect, the suspect parts.
• Type 1 errors are also called as “Producer’s Risk” or alpha errors.
Type 2 Errors : When a Bad part is accepted.
• Type 2 errors may occur
• Perhaps the inspector was poorly trained or rushed through the inspection and inadvertently
overlooked a Small defect on the part.
• When Type 2 errors occur, defects slip through the containment net and are shipped to the
customer.
• Because Type 2 errors put the customer at risk of receiving defective parts; customer may
raised the complaint.
• Type 2 errors are sometimes called as “Consumer’s Risk”.
• Type 2 errors are also called as “beta” errors.
Types of Errors in Attribute Measurement system
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12. Theeffectiveness of an inspection process is Correct
Call.
Correct Call (Cc):- The number of times of which
the operator (s) identify a good sample as a good
one and bad sample as a bad one.
Effectiveness (E) = Number of correct evaluations (GG+BB)
Total Number of parts (TN)
What is Effectiveness?
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E-EFFECTIVENESS
GG-GOOD PART INSPECTED AS GOOD
BB- BAD PART INSPECTED AS BAD
TN-TOTAL NO OF PARTS

13. False Alarm (Fa) – The number of times of which
the operator (s) identify a good sample as a bad
one.
The probability of a false alarm, also known asType I
error or producer’s risk, is given by:
FalseAlarm (Pfa) = Number of false alarms (GBB)
Total Number of Good parts (TG)
What is False Alarm?
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Pfa- POSSIBILITY OF FALSE ALARM
GBB- GOOD BUT BAD
TG- TOTAL NO OF GOOD PARTS

14. What is Miss rate?
A Miss is a defective item that is classified as non-
defective.
Miss rate - The number of times of which the
operator identify a bad sample as a good one.
The probability of a miss, also known as Type II
error or Consumer’s risk, is given by:
Miss rate (Pmiss) =
Number of misses (BBG)
Total No of Bad parts (TB)
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Pmiss-POSSIBILITY OF MISSED
BBG-BAD BUT GOOD
TB-TOTAL NO OF BAD PARTS

15. RESULTS
APPRAISER
CORRECT
(GG)
CORRECT
(BB)
Total
RIGHT
DECISIONS
(GG+BB)
FALSE
ALARMS
(NF)
(GBB)
NO. OF
MISSED
(NM)
(BBG)
TOTAL NO
OF
CHECKED
PARTS
(TN)
EFFECTIVEN
ESS
(E)
GG+BB/TN
Pfa
(NF/TG)
Pmiss
(NM/TB
)
A 39 18 57 3 0 60 0.95 0.071 0
B 40 18 58 2 0 60 0.9667 0.048 0
C 39 17 56 3 1 60 0.9333 0.071 0.056
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GG-GOOD PART INSPECTED AS GOOD BB- BAD PART INSPECTED AS BAD
GBB-GOOD BUT BAD BBG-BAD BUT GOOD
TN-TOTAL NO OF PARTS TG-TOTAL NO OF GOOD PARTS
TB-TOTAL NO OF BAD PARTS E-EFFECTIVENESS OF APPRAISER
NF-NO OF FALSE ALARMS NM-NO OF MISSED
Pfa- POSSIBILITY OF FALSE ALARM Pmiss-POSSIBILITY OF MISSED

16. Acceptability criteria:
If all measurement results agree, the gauge is
acceptable. If the measurement results do not
agree, the gage can not be accepted, it must be
improved and re-evaluated.
Parameter Acceptable Marginal Not Acceptable
EFFECTIVENESS (E) >.90 .80<E<=.90 <=.80
FALSE ALARM
RATE (Pfa) <.05 .10>Pfa>=.05 >=.10
MISS - RATE (Pmiss) <.02 .05>Pmiss>=.02 >=.05
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17. If any of the decisions disagree, the measurement system may
need improvement.
Improvement actions include:
• Reworking the gauge,
• Re‐training the inspectors,
• Clarifying the accept/reject criteria,
• Adding more lighting
After implementing the improvement actions, repeat the study.
If the error cannot be eliminated, Must take appropriate corrective
actions, such as switching to a new measurement system,
adding redundant inspections, or conducting a more
extensive study.
Attribute MSA Study Conclusion
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18. Thank you very much for your attention!
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