Measurement System Analysis (MSA) Concepts

Foundation principles for understanding measurement system capability

What is MSA?

Measurement System Analysis (MSA) is a mathematical method of determining how much the variation within the measurement process contributes to overall process variability.

Why is MSA Important?
  • Ensures measurement data is reliable
  • Distinguishes between part variation and measurement variation
  • Validates measurement systems before use
  • Supports data-driven decision making
  • Required by quality standards (ISO/TS 16949, etc.)
MSA Objectives
  • Assess measurement system capability
  • Identify sources of measurement variation
  • Determine if the system is adequate for its intended use
  • Provide input for process improvement
  • Establish measurement system monitoring

Sources of Measurement Variation

Understanding the sources of variation is crucial for effective MSA. Total observed variation comes from two main sources:

Part Variation

What it is: The actual differences between parts being measured

This is what we want to measure!

  • Manufacturing process variation
  • Material differences
  • Design tolerances
  • Process capability
Measurement System Variation

What it is: Variation introduced by the measurement process itself

This is what we want to minimize!

  • Gage/instrument variation
  • Operator differences
  • Environmental effects
  • Method/procedure variation
The Fundamental Equation

σ²Total = σ²Part + σ²Measurement System

The 5 M's of Measurement Systems

Every measurement system consists of five key components that can contribute to variation:

Machine/Gage
  • Instrument accuracy
  • Resolution/discrimination
  • Calibration status
  • Mechanical condition
  • Stability over time
Man/Operator
  • Training level
  • Experience
  • Technique consistency
  • Reading ability
  • Fatigue/attention
Method
  • Measurement procedure
  • Setup instructions
  • Sampling strategy
  • Data recording
  • Calculation methods
Material/Part
  • Part condition
  • Surface finish
  • Cleanliness
  • Temperature
  • Deformation
Mother Nature
  • Temperature
  • Humidity
  • Vibration
  • Lighting
  • Air quality

Types of MSA Studies

Variable Data Studies

For continuous measurements (length, weight, temperature, etc.)

  • Gage R&R: Repeatability & Reproducibility
  • Bias Study: Accuracy assessment
  • Linearity Study: Accuracy across range
  • Stability Study: Performance over time
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Attribute Data Studies

For pass/fail, go/no-go measurements

  • Attribute Agreement: Operator consistency
  • Kappa Statistics: Agreement measurement
  • Effectiveness: Correct classification rate
  • Bias: Tendency to over/under classify
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MSA Process Flow

1

Plan Study

Define objectives, select parts, operators
2

Collect Data

Execute measurement plan
3

Analyze Results

Calculate %GRR, NDC, P/T
4

Take Action

Improve or approve system

Key MSA Metrics

%GRR

Percentage of total variation due to measurement system

< 10% = Excellent
10-30% = Marginal
> 30% = Unacceptable
NDC

Number of Distinct Categories the system can reliably distinguish

NDC ≥ 5 is generally acceptable
Higher NDC = better discrimination
P/T Ratio

Precision to Tolerance ratio

P/T ≤ 0.1 = Excellent
P/T ≤ 0.3 = Acceptable
P/T > 0.3 = Unacceptable

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