Chapter 9: Measurement System Analysis (MSA)

Prof.(Dr.) D G Mahto, Founder & CEO -

Abstract:

Measurement System Analysis (MSA) is a statistical method to evaluate the quality, accuracy, and precision of a measurement system (gauges, methods, software, personnel, environment) to ensure it provides reliable data for decisions, identifying variation sources like repeatability, reproducibility, bias, linearity, and stability, and confirming it's fit for use before impacting product quality
. MSA helps separate measurement error from actual product variation, preventing costly mistakes in quality control, 

This video provides a detailed overview of Measurement System Analysis (MSA):
Key Components & Concepts
  • Accuracy (Bias): Difference between the average measurement and the true value.
  • Precision: How close repeated measurements are to each other.
  • Repeatability: Variation when the same operator uses the same gauge on the same part.
  • Reproducibility: Variation when different operators use the same gauge on the same part.
  • Stability: Consistency of measurements over time.
  • Linearity: How bias changes across the measurement range.
  • Resolution/Discrimination: The smallest unit the gauge can detect, ideally 1/10th of the tolerance or process spread. 
Why MSA is Important
  • Data Integrity: Ensures data used for quality decisions isn't just noise from the measuring tool.
  • Risk Reduction: Prevents misidentifying good parts as bad (or vice versa).
  • Cost Savings: Reduces scrap, rework, and downtime by avoiding errors.
  • Process Improvement: Isolates measurement problems from actual process issues. 
Watch this video to see how MSA helps in the measure phase of quality improvement:
How it's Done (Gauge R&R)
A common method is Gauge Repeatability & Reproducibility (Gage R&R), a structured experiment where multiple parts are measured multiple times by different operators to quantify variations. The analysis determines if the measurement system accounts for an acceptable percentage of the total variation, ensuring it's "fit for use

So let's dive into the Chapter 9 Measurement System Analysis (MSA) for scientific knowhow

9.1 Introduction

In Statistical Process Control, decisions are made based on data. If the measurement system is unreliable, even the best SPC tools will lead to incorrect conclusions. Measurement System Analysis (MSA) evaluates the accuracy, precision, and consistency of measurement systems to ensure that data used for quality control and improvement are trustworthy.

This chapter introduces the concepts, sources of measurement variation, and commonly used MSA techniques.

9.2 Importance of Measurement System Analysis

MSA is essential because:

  • Measurement error contributes to observed process variation

  • Poor measurement systems can cause false out-of-control signals

  • Capability and SPC results depend on measurement quality

A general rule:

Measurement system variation should be much smaller than process variation.

9.3 Components of Measurement Variation

Total observed variation consists of:

  • Part-to-part variation

  • Measurement system variation

Measurement system variation includes:

  • Repeatability

  • Reproducibility

9.4 Accuracy and Precision

9.4.1 Accuracy

Accuracy is the closeness of a measured value to the true value.

Components of accuracy:

  • Bias: Difference between observed average and reference value

  • Linearity: Change in bias across measurement range

  • Stability: Consistency of bias over time

9.4.2 Precision

Precision refers to the consistency of repeated measurements.

Precision includes:

  • Repeatability: Variation when the same operator measures the same part using the same instrument

  • Reproducibility: Variation due to different operators measuring the same part

9.5 Repeatability and Reproducibility (Gage R&R)

Gage R&R studies quantify measurement system variation.

9.5.1 Repeatability

  • Equipment variation

  • Influenced by instrument resolution and condition

9.5.2 Reproducibility

  • Operator variation

  • Influenced by training and measurement method

9.6 Gage R&R Study Methods

9.6.1 Average and Range Method

  • Simple and widely used

  • Suitable for manual calculations

  • Common in UG and diploma courses

9.6.2 ANOVA Method

  • More accurate and statistically rigorous

  • Separates interaction effects

  • Common in PG and Six Sigma applications

9.7 Interpretation of Gage R&R Results

Gage R&R is expressed as a percentage of total variation:

% Gage R&RInterpretation
≤ 10%Excellent measurement system
10% – 30%Acceptable (depends on application)
> 30%Unacceptable

9.8 Number of Distinct Categories (NDC)

NDC indicates the ability of the measurement system to distinguish between different parts.

[
NDC = 1.41 \times \frac{\text{Part variation}}{\text{Gage R&R}}
]

Guideline:
NDC ≥ 5 is desirable.

9.9 Attribute Measurement System Analysis

Attribute MSA evaluates:

  • Consistency of inspection decisions

  • Agreement between inspectors

Common methods:

  • Attribute agreement analysis

  • Kappa statistics

9.10 Measurement Resolution and Discrimination

Resolution refers to the smallest unit the instrument can measure.

Rule of thumb:
Measurement resolution should be at least 1/10 of the tolerance.

9.11 Impact of Poor Measurement Systems

  • Incorrect SPC signals

  • Misleading capability indices

  • Poor improvement decisions

  • Increased cost and rework

9.12 Learning Objectives

After studying this chapter, the learner will be able to:

  • Explain components of measurement variation

  • Differentiate between accuracy and precision

  • Conduct and interpret Gage R&R studies

  • Understand importance of NDC

9.13 Review Questions

  1. What is Measurement System Analysis?

  2. Differentiate between repeatability and reproducibility.

  3. What is bias in measurement systems?

  4. Explain Gage R&R.

  5. What is NDC?

9.14 Short Answer Questions (Exam Oriented)

  1. Define accuracy.

  2. What does %Gage R&R indicate?

  3. State one cause of poor reproducibility.

  4. What is measurement resolution?

9.15 Summary

This chapter emphasized the importance of reliable measurement systems in SPC. By analyzing accuracy, precision, and Gage R&R, organizations ensure that decisions based on SPC and capability analysis are valid and effective.

📌 Chapter 10: SPC Implementation, Case Studies, and Future Trends is the ideal concluding chapter.

Please reply “Proceed with Chapter 10”
or let me know if you want:

  • numerical examples added

  • Six Sigma certification-oriented notes

  • industry case studies (manufacturing/services)

I will continue accordingly, sir.

Prof. (Dr.) D. G. Mahto: A Visionary Director, Professor, Leader & Educator With over 25 years of excellence, Prof. (Dr.) D. G. Mahto is a Director, Professor, Philanthropist, HEI Builder, writer, consultant, reviewer, and career advisor at "Career Education for Success—Discover, Apply, Succeed!" He has empowered individuals, brands, and institutions through insightful content, innovation, and mentorship. Holding a Ph.D. in Engineering from NIT Jamshedpur, he has pursued Post-Doctoral Programs at IITs, NITs, and IIITs, reinforcing his expertise. A member of prestigious global organizations like NPA (USA), IET (UK), ASME, IAENG, and MRSI, he actively contributes to engineering, research, and education. An accomplished writer and researcher, he shares knowledge via Career Education Success Now, Career Edus, and Discover Knowledge Wisely. His vision integrates technology, innovation, and leadership, bridging gaps between aspirations and achievements. Connect with him for insights into education, research, and professional growth.

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