This document provides an overview of a training session on Measurement System Analysis (MSA). The session will be led by two trainers and will cover topics such as: - Defining a measurement system and MSA - Understanding variation from measurement systems versus process variation - Accuracy versus precision - Key elements of MSA like bias, linearity, stability, repeatability, and reproducibility - Conditions for performing an effective MSA study - Benefits of MSA like evaluating measurement system reliability and identifying sources of variation The training contents include explanations of these MSA concepts along with examples to illustrate differences between accuracy and precision, potential sources of variation, and how to analyze MSA results.

1. National Quality month celebration - Nov 2023
Quality Knowledge Sharing
Session # 3
“Measurement System Analysis”
Trainers:
1. S. Sasikumar
2. M. Krishnan

2. Contents
1. What is Measurement System
2. What is Measurement System Analysis (MSA)
3. Understanding Variation
4. Accuracy Vs Precision
5. Elements of MSA
a. Bias
b. Linearity
c. Stability
d. Repeatability
e. Reproducibility
6. Conditions for performing MSA study
7. Benefits of MSA

3. 1. What is Measurement System?
Measurement:
A Measurement is a process by which we assign a number
to a characteristic of a product or service.
Measurement System:
# A Measurement system is a complete process used to
obtain the measurement.
# In other words, a measurement system is a combination of
- Equipment/ Instruments
- Personnel
- Methods / Procedures
- Environment
- Software

4. 2. What is Measurement System Analysis?
Measurement System Analysis:
# Measurement System Analysis, or MSA, is a formal statistical study that determines whether
the measurement systems are capable of providing reliable data so that best possible data-
driven decisions can be made

5. 3. Understanding Variation
# Total Variation = Measurement system Variation + Process Variation
# Process Variation:
- Part to Part Variation
- Within Part Variation
# Measurement System Variation:
- Equipment Variation
- Appraiser Variation
- Variation due to interaction
# Variation due to Measurement system has to be kept to minimum to identify the process

6. 3. Understanding Variation
# Too much of salt in Soup due to Taste
checker (Measurement system) variation

7. 4. Accuracy Vs Precision
Accuracy:
It is described as the difference
between the measured value and
actual value or closeness to the
target
Precision:
It is described as the variation in
the inspection due to
measurement system error also
known as measurement variation.

8. Accuracy Vs Precision
Accurate but not Precise
Precise but not Accurate
Neither Accurate Nor Precise
Accurate & Precise

9. 5. Elements of MSA
BIAS Average measured value - True value
STABILITY Bias over a longer period
LINEARITY Bias over entire measurement range
REPEATABILITY the variation within same
appraiser
REPRODUCIBILITY The variation between the
appraiser

10. 5.1 Bias

11. 5.2 Linearity

12. 5.2 Linearity

13. 5.3 Stability

14. 5.3 Stability
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6.45 am 6.44 am 7.30 am
Common
Cause
Variation
Special Cause
Variation
(Accident/
Traffic)

15. 5.4 Repeatability

16. 5.5 Reproducibility

17. 5.5 Reproducibility
0 0 0 W

18. 6. Conditions for Performing MSA study
1. Optimum condition for MSA
- 3 Appraisers
- 3 Trials
- 10 parts
2. The number of parts chosen must represent the entire operating range
3. Appraisers are those who normally operate the gauge
4. One observer should be appointed to record the measurements
5. Each part must be identified
6. Measurement should be done in random order
7. The appraiser should be unaware of which numbered part is being checked
to avoid knowledge bias
8. Each appraiser must use the same method to obtain the measurements

19. 6. MSA Results

20. 7. Benefits of MSA
# Evaluates the reliability of the measurement system
# Equipment variation can be identified
# Appraiser variation can be identified
# It is possible to assess the correctness of the measurement system.
# The method is not only applicable continuous data but also to discrete data type.
# It is possible to compare the existing system with its’ future state in a reliable way

21. 21
Trainers:
1. S. Sasikumar
2. M. Krishnan