9.7 Measurement System Reanalysis

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1 Chapter 1: Introduction & CSSBB Exam Strategy

1.1 CSSBB Exam Format, Scoring, and Testing Windows 1.2 Eligibility, Project Affidavits, Application, and Recertification 1.3 Open-Book Strategy, Reference Materials, and a 16-Week Study Plan

2 Chapter 2: Organization-Wide Planning and Deployment

2.1 Fundamentals of Six Sigma and Lean Methodologies 2.2 Selecting the Right Methodology: DMAIC, DFSS, and Lean 2.3 Business Systems, Processes, and Stakeholder Relationships 2.4 Strategic Planning and Deployment of Six Sigma Initiatives 2.5 Six Sigma Roles and Responsibilities 2.6 Organizational Barriers and Common Causes of Six Sigma Failure 2.7 Change Management: Stakeholder Analysis, Readiness, and Communication

3 Chapter 3: Organizational Process Management and Measures

3.1 Impact of Six Sigma Projects on Customers, Suppliers, and Stakeholders 3.2 Benchmarking Types and Performance Goal Setting 3.3 Performance Measures: Balanced Scorecard, KPIs, KBIs, OKRs, and Line of Sight 3.4 Leading and Lagging Indicators and Performance Dashboards 3.5 Financial Measures: Revenue Growth, Margin, NPV, ROI, and Cost-Benefit Analysis 3.6 Cost of Poor Quality and Hard versus Soft Savings

4 Chapter 4: Team Management

4.1 Team Types and Constraints 4.2 Team Roles, Responsibilities, and Member Selection 4.3 Team Success Factors and Ground Rules 4.4 Motivational Techniques and Overcoming Demotivators 4.5 Stages of Team Development and Conflict Resolution 4.6 Team Communication Plans 4.7 Team Leadership Models: Direct, Coach, Support, and Delegate 4.8 Group Behaviors, Conflict Resolution, and Meeting Management 4.9 Facilitation Techniques and Team Decision-Making Methods 4.10 Team Training: Needs Assessment, Delivery, and Evaluation

5 Chapter 5: Define Phase

5.1 Customer Identification and Segmentation 5.2 Customer Data Collection: Methods, Validity, and Reliability 5.3 Customer Requirements: CTX, CTQ Trees, and Operational Definitions 5.4 The Kano Model and Quality Function Deployment 5.5 SIPOC and Process Mapping 5.6 Business Case Justification 5.7 The Project Charter and the Problem Statement 5.8 Project Scope and Boundaries 5.9 SMART Goals and Project Performance Measurements 5.10 Project Charter Reviews and Toll-Gate Governance 5.11 Project Management Tools: Gantt Charts, Toll-Gate Reviews, WBS, and RACI 5.12 Analytical Tools Used Throughout the DMAIC Cycle

6 Chapter 6: Measure Phase

6.1 Process Flow Metrics and the Hidden Factory 6.2 Process Analysis Tools and Value Stream Mapping 6.3 Types of Data and Measurement Scales 6.4 Sampling Concepts, Methods, and Sample Size for Estimation 6.5 Data Collection Plans and Common Pitfalls 6.6 Gage R&R for Variable Measurement Systems 6.7 Attribute Agreement Analysis and Audit MSA 6.8 Measurement Systems Across the Organization, and Metrology 6.9 Population Parameters, Sample Statistics, and Valid Statistical Conclusions 6.10 Descriptive Statistics: Central Tendency, Dispersion, and Shape 6.11 Graphical Methods for Exploratory Data Analysis 6.12 Probability Concepts, Common Distributions, and the Central Limit Theorem 6.13 Additional Distributions: Hypergeometric, Exponential, Lognormal, Weibull, and Bivariate 6.14 Process Capability and Performance Indices 6.15 Capability Studies, Attributes Capability, Yield Metrics, and Long-Term Capability

7 Chapter 7: Analyze Phase

7.1 The Correlation Coefficient, Its Confidence Interval, and Causation 7.2 Linear Regression and Residual Analysis 7.3 Multivariate Tools: Factor Analysis, Discriminant Analysis, and MANOVA 7.4 Hypothesis Testing Terminology, Errors, and Single-Sample Tests 7.5 Statistical Versus Practical Significance 7.6 Sample Size for Hypothesis Tests 7.7 Point Estimates, Confidence, Prediction, and Tolerance Intervals 7.8 Tests for Means, Variances, and Proportions 7.9 Analysis of Variance 7.10 Chi-Square Goodness-of-Fit and Tests of Independence 7.11 Types of Risk and Failure Mode and Effects Analysis 7.12 Gap Analysis and Root Cause Analysis 7.13 Waste Analysis: The Seven Classic Wastes and Resource Under-Utilization

8 Chapter 8: Improve Phase

8.1 Design of Experiments: Objectives, Terminology, and Effects 8.2 DOE Design Principles: Randomization, Blocking, Replication, and Resolution 8.3 Planning and Evaluating Designed Experiments 8.4 One-Factor Experiments: Completely Randomized, Randomized Block, and Latin Square Designs 8.5 Full Factorial Experiments and Design Selection 8.6 Two-Level Fractional Factorial Experiments and Confounding 8.7 Response Surface Methodology and Optimization 8.8 Waste Elimination: Pull Systems, Kanban, 5S, and Poka-Yoke 8.9 Cycle-Time Reduction: Continuous Flow, SMED, and Heijunka 8.10 Kaizen, Kaizen Blitz, Theory of Constraints, and OEE 8.11 Implementation Planning, Piloting, and Solution Selection

9 Chapter 9: Control Phase

9.1 SPC Objectives, Variable Selection, and Rational Subgrouping 9.2 Control Charts for Variables Data 9.3 Control Charts for Attributes Data 9.4 Short-Run SPC and Moving Average Charts 9.5 Control Chart Analysis: Common Cause, Special Cause, and Run Rules 9.6 Total Productive Maintenance and Visual Controls 9.7 Measurement System Reanalysis 9.8 Control Plan Development and Process Owner Handoff 9.9 Lessons Learned, Documentation, and Training for Sustainment 9.10 Ongoing Evaluation of the Improved Process

10 Chapter 10: Design for Six Sigma (DFSS)

10.1 DFSS Frameworks: DMADV, DMADOV, and Design Transfer 10.2 Design for X (DFX) 10.3 Robust Design, Parameter Design, and Verification 10.4 Tolerance Design and Statistical Tolerancing

9.7 Measurement System Reanalysis

Key Takeaways Explain This Topic AI

Observed variance is the sum of true process variance and measurement system variance, so shrinking the process makes the gauge a larger share of what remains.

Percent study variation compares measurement variation with total observed variation; percent tolerance compares it with the specification width.

The AIAG guidance is that under 10% is acceptable, 10 to 30% is conditionally acceptable, and above 30% is unacceptable.

The number of distinct categories should be at least 5, computed as 1.41 times the part standard deviation divided by the gage R&R standard deviation.

Measurement system reanalysis is triggered by improved capability, gauge or fixture changes, new operators, tightened tolerances, and at a defined periodic interval.

Last updated: August 2026

Why capability improvement degrades the measurement system

Observed variation always contains both the process and the gauge: σ o b s e r v e d 2 = σ p r o c e s s 2 + σ m e a s u r e m e n t 2 \sigma^2_{observed} = \sigma^2_{process} + \sigma^2_{measurement} σ o b ser v e d 2 ​ = σ p rocess 2 ​ + σ m e a s u re m e n t 2 ​

A successful project reduces σ p r o c e s s 2 \sigma^2_{process} σ p rocess 2 ​ and leaves σ m e a s u r e m e n t 2 \sigma^2_{measurement} σ m e a s u re m e n t 2 ​ untouched. The

gauge's share of what remains therefore rises, sometimes dramatically.

Worked example. Baseline: σ o b s e r v e d = 0.50 \sigma_{observed} = 0.50 σ o b ser v e d ​ = 0.50 , of which σ m e a s u r e m e n t = 0.15 \sigma_{measurement} = 0.15 σ m e a s u re m e n t ​ = 0.15 .

σ p r o c e s s 2 = 0.50 2 − 0.15 2 = 0.2500 − 0.0225 = 0.2275 \sigma^2_{process} = 0.50^2 - 0.15^2 = 0.2500 - 0.0225 = 0.2275 σ p rocess 2 ​ = 0.5 0 2 − 0.1 5 2 = 0.2500 − 0.0225 = 0.2275 , so σ p r o c e s s = 0.477 \sigma_{process} = 0.477 σ p rocess ​ = 0.477 .

Measurement share of observed variance = 0.0225 / 0.2500 = 9.0 % = 0.0225 / 0.2500 = 9.0\% = 0.0225/0.2500 = 9.0% , and

%Study Variation = 0.15 / 0.50 = 30 % = 0.15/0.50 = 30\% = 0.15/0.50 = 30% -- marginal but workable.

The project halves the process standard deviation to σ p r o c e s s = 0.2385 \sigma_{process} = 0.2385 σ p rocess ​ = 0.2385 :

New σ o b s e r v e d 2 = 0.2385 2 + 0.15 2 = 0.05688 + 0.0225 = 0.07938 \sigma^2_{observed} = 0.2385^2 + 0.15^2 = 0.05688 + 0.0225 = 0.07938 σ o b ser v e d 2 ​ = 0.238 5 2 + 0.1 5 2 = 0.05688 + 0.0225 = 0.07938 , so σ o b s e r v e d = 0.2818 \sigma_{observed} = 0.2818 σ o b ser v e d ​ = 0.2818 .

Measurement share of variance = 0.0225 / 0.07938 = 28.3 % = 0.0225 / 0.07938 = 28.3\% = 0.0225/0.07938 = 28.3% , and

%Study Variation = 0.15 / 0.2818 = 53 % = 0.15/0.2818 = 53\% = 0.15/0.2818 = 53% -- now unacceptable.

• Nothing about the gauge changed. The project's own success made it inadequate, and every subsequent
• capability figure computed with it will be understated , because measurement noise is being counted as

process variation.

Acceptance criteria

Metric Formula Compares against

%Study Variation (%SV) 100 × σ R & R / σ t o t a l 100 \times \sigma_{R\&R} / \sigma_{total} 100 × σ R & R ​ / σ t o t a l ​ Total observed variation

%Tolerance (P/T) 100 × 6 σ R & R / ( U S L − L S L ) 100 \times 6\sigma_{R\&R} / (USL - LSL) 100 × 6 σ R & R ​ / ( U S L − L S L ) The specification width

%Contribution 100 × σ R & R 2 / σ t o t a l 2 100 \times \sigma^2_{R\&R} / \sigma^2_{total} 100 × σ R & R 2 ​ / σ t o t a l 2 ​ Total observed variance

Number of distinct categories (ndc) 1.41 × σ p a r t / σ R & R 1.41 \times \sigma_{part} / \sigma_{R\&R} 1.41 × σ p a r t ​ / σ R & R ​ Resolving power

AIAG guidance, applied to %SV or %Tolerance:

• Value Verdict
• Under 10% Acceptable

10% to 30% Conditionally acceptable, based on the criticality of the application, the cost of the gauge, and the cost of repair

• Over 30% Unacceptable; the measurement system must be improved
• ndc should be at least 5, meaning the gauge can distinguish at least five distinct levels of part
• variation. An ndc below 5 signals inadequate resolving power even when the percentage figures look

borderline acceptable.

• Note that %Contribution uses variance while %SV uses standard deviation , so the two numbers differ
• substantially for the same study: 30% study variation corresponds to 9% contribution. Quoting one and

comparing it against the other's threshold is a common error.

• Which criterion to use when
• Use %Tolerance when the gauge is used to accept or reject product against a specification, because

the question is whether the gauge can distinguish good from bad.

Use %Study Variation when the gauge is used to study and improve the process, because the question

is whether the gauge can see the process variation.

• A gauge can pass one and fail the other. A wide tolerance with a tightly controlled process gives a
• good P/T ratio and a poor %SV, which is precisely the situation a Six Sigma team encounters after a

successful project.

Triggers for reanalysis

• Trigger Reason
• Process capability has improved materially The gauge's share of observed variation has risen
• Tolerance has been tightened The P/T ratio changes even though the gauge did not
• Gauge, fixture, or software has been changed or repaired The system is not the one that was validated
• New operators or a new location Reproducibility may differ
• Out-of-tolerance found at calibration The validated state was not maintained
• Unexplained shift in the control chart The measurement system is a candidate cause
• Defined periodic interval Standard practice, typically annually for critical characteristics
• Before a capability study is used for a customer submission The result must be defensible
• The first row is the one the Body of Knowledge specifically emphasizes and the one teams most often

miss.

What to do when reanalysis fails

Work in order of cost:

• Improve the procedure. Standardize the fixturing, the clamping force, the location of the
• measurement, and the operator's technique. Reproducibility problems are often pure method problems and

cost nothing to fix.

Retrain the appraisers. A large reproducibility component with a small repeatability component

points at people, not the instrument.

• Improve resolution. The gauge discrimination should be no coarser than one tenth of the process
• variation or of the tolerance , whichever is tighter. A gauge reading to 0.01 mm cannot support a

study of a process whose total spread is 0.03 mm.

Average repeated readings. Averaging m m m readings reduces the repeatability standard deviation by m \sqrt{m} m ​ . This is a legitimate short-term measure with an ongoing cost, and it does nothing for

reproducibility or bias.

Replace the gauge or automate the measurement. The last resort by cost, and sometimes the only

real answer.

• Recording the reanalysis
• The Control phase deliverable is not the number but the decision. Record: the date and reason for the
• reanalysis, the study design (parts, appraisers, trials), the resulting %SV, %Tolerance, %Contribution,
• and ndc, the verdict against the criteria, any action taken, and the next scheduled review. Put the
• review interval into the control plan itself, so the reanalysis happens by schedule rather than by

memory.