9.5 Control Chart Analysis: Common Cause, Special Cause, and Run Rules

ASQ Certified Six Sigma Black Belt CSSBB

Track your progress as you read

Finished sections get a check mark.

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

9.5 Control Chart Analysis: Common Cause, Special Cause, and Run Rules

Key Takeaways Explain This Topic AI

Common cause variation is inherent to the process and requires a system change; special cause variation is external and requires investigation of the specific event.

Tampering means adjusting a stable process in response to common-cause variation, and it increases variation rather than reducing it.

The most commonly used run rules are the Western Electric rules, with rule 1 being a single point beyond three sigma.

Each additional run rule raises sensitivity to real shifts and simultaneously raises the false alarm rate.

A chart with no reaction plan is a decoration; every rule must map to a documented out-of-control action.

Last updated: August 2026

The two kinds of variation

Common cause Special cause
Also called Chance, random, systemic Assignable, sporadic
Source Inherent in the process design and system An event outside the usual system
Pattern on the chart Random within the limits A point outside a limit, or a non-random pattern
Share of total variation Typically 80% to 95% in a stable process The remainder
Who can fix it Management, through a system change Local personnel, at the process

Correct response Do not react to individual points; change the system Investigate the specific event and prevent recurrence

The distinction matters because the two require opposite responses, and mixing them up produces the

two classic errors:

Tampering (Deming's Rule 1 error) : treating common-cause variation as if it were special and

adjusting the process. This is not merely wasted effort -- it demonstrably increases variation.

The funnel experiment demonstrates it: adjusting after every observation roughly doubles the standard

deviation of the output compared with leaving the process alone.

Ignoring a signal (Rule 2 error) : treating a genuine special cause as ordinary noise, so the event

recurs.

A control chart's entire purpose is to make this distinction objectively rather than by judgment.

The Western Electric run rules

Zones are defined by dividing the distance between the centre line and each control limit into thirds:

zone C is within 1 σ 1\sigma 1 σ , zone B between 1 σ 1\sigma 1 σ and 2 σ 2\sigma 2 σ , zone A between 2 σ 2\sigma 2 σ and 3 σ 3\sigma 3 σ .

Rule Condition Detects
1 1 point beyond zone A (outside 3 σ 3\sigma 3 σ ) Large sudden shift, outlier
2 2 of 3 consecutive points in zone A or beyond, same side Moderate shift
3 4 of 5 consecutive points in zone B or beyond, same side Small to moderate shift
4 8 consecutive points on one side of the centre line Small sustained shift

Nelson's rules extend the set with four more patterns:

Rule Condition Detects
5 6 consecutive points steadily increasing or decreasing Trend: tool wear, fouling, drift
6 15 consecutive points in zone C (both sides) Stratification, or limits computed too wide
7 14 consecutive points alternating up and down Over-adjustment, or two alternating sources
8 8 consecutive points on both sides with none in zone C Mixture of two distinct processes
Reading the unusual ones
Rule 6, hugging the centre line , is counter-intuitive because the chart looks excellent. It usually
means either the control limits were computed from data containing an extra source of variation (so they
are too wide), or the subgroups are stratified -- for example, each subgroup deliberately contains one
part from each of four machines, so between-machine variation is averaged away inside the subgroup

instead of appearing between subgroups.

Rule 8, avoiding the centre line , is the mirror image and usually indicates a mixture : two
processes with different means being charted together, such as two shifts, two suppliers, or two

cavities of a mould.

Rule 7, systematic alternation , most often indicates over-adjustment -- an operator correcting after

every measurement, which is tampering made visible.

The false alarm trade-off
Each rule has its own false alarm probability, and adding rules multiplies the risk of a false signal on

a perfectly stable process.

Rule set Approximate in-control ARL Comment
Rule 1 only 370 The classic 3-sigma chart
Rules 1-4 about 92 Considerably more sensitive to small shifts
Rules 1-8 about 60 Very sensitive; frequent false alarms
Average run length of 60 means a stable process signals roughly every 60 subgroups for no reason. On a

chart plotted hourly, that is a false alarm every day and a half.

The practical guidance:

Use rule 1 alone plus rule 4 on high-volume charts where false alarms are expensive to

investigate.

Add rules 2, 3, and 5 where small shifts matter and investigation is cheap.

Apply the full set only on critical characteristics.

Decide the rule set in advance and write it into the control plan. Adding rules after seeing a

pattern you dislike is not statistical process control.

The out-of-control action plan
A control chart without a documented reaction plan is a decoration. The OCAP specifies, for each

signal:

Element Content
Trigger Which rule fired, on which characteristic
Immediate containment Stop, quarantine, segregate since the last good check
Who reacts Named role, with the authority to stop
Investigation steps An ordered checklist of the likely causes to check
Disposition What happens to the material produced since the previous in-control point
Documentation Where the reaction, the cause, and the corrective action are recorded
Escalation Who is called and when, if the cause is not found within a defined time
Interpreting a chart in practice

Work through the questions in order:

Is the range or sigma chart in control? Always read the variation chart first. If the spread is
unstable, the estimate of sigma used to build the X-bar limits is invalid, so the X-bar chart cannot

be interpreted at all.

Are there points outside the limits? Rule 1.

Are there non-random patterns? Runs, trends, cycles, mixtures.

Was the process stable over the period the limits were computed from? Limits calculated from

unstable data are too wide and hide subsequent signals.

Are the limits current? After a deliberate process change, recompute rather than continuing

against limits that describe the old process.

Two final cautions. Specification limits never belong on a control chart -- the chart describes what
the process does, the specification describes what is required, and the distribution of subgroup means
is narrower than the distribution of individuals by a factor of n \sqrt{n} n . And a process in control is
not necessarily capable : stability and capability are separate questions, answered by the control chart

and the capability index respectively.