Study guide
7.12 Gap Analysis and Root Cause Analysis
ASQ Certified Six Sigma Black Belt CSSBB
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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
7.12 Gap Analysis and Root Cause Analysis
Key Takeaways Explain This Topic AI
The 5 Whys methodology conducts iterative root cause identification, drilling down past surface symptoms to underlying systemic failures, and each answer must be backed by evidence rather than opinion.
Ishikawa (Fishbone) diagrams organize potential causes into standard categories: the 6 Ms for manufacturing (Man, Machine, Method, Material, Measurement, Mother Nature) and the 4 Ps/8 Ps for service environments.
The Cause & Effect (C&E) Matrix prioritizes process inputs (Xs) by scoring their impact against customer output requirements (Ys) weighted by CTQ importance.
Fault tree analysis is top-down and deductive, propagating probabilities through AND gates (multiply) and OR gates (1 minus the product of complements); single-element minimal cut sets are single-point failures and are fixed first.
Gap analysis quantifies how far performance sits from a target with a named source, and splitting the gap at entitlement separates the part achievable by reducing variation from the part needing a capability change.
Last updated: August 2026
Root Cause Analysis (RCA) encompasses qualitative, semi-quantitative, and structured risk analysis techniques designed to identify the underlying failure mechanisms driving process variation, defects, and inefficiencies. Moving from high-level brainstorming down to rigorous input prioritization, RCA methods ensure that improvement efforts target fundamental systemic root causes rather than superficial symptoms.
5 Whys Analysis
The 5 Whys methodology is an iterative, qualitative problem-solving technique pioneered by Sakichi Toyoda for the Toyota Production System. It repeatedly asks "Why?" to drill down through layers of superficial symptoms until the true root cause of a defect or process failure is revealed.
Implementation Guidelines & Rules
Focus on Process, Not People : Avoid attributing failures to human error; inquire why the system, procedure, or training allowed the error to occur or pass undetected.
Fact-Based Logic Chains : Each "Why" response must be backed by empirical evidence, physical observation, or process data.
Verification of the Chain : Test the logic chain in reverse using "Therefore" statements (e.g., Root Cause → \rightarrow → Therefore Factor 4 → 4 \rightarrow 4 → Therefore Factor 3 ⋯ → 3 \dots \rightarrow 3 ⋯ → Therefore Problem Statement).
Stopping Criteria : Stop asking "Why" when an actionable failure mechanism is reached that is within control of the process team and prevents recurrence when remedied.
Cause & Effect (Ishikawa / Fishbone) Diagram
The Cause and Effect diagram (developed by Kaoru Ishikawa) visually organizes potential causes into structured categories branching off a central problem spine toward a "fish head" problem statement.
- Categorization Frameworks
- 1. The 6 Ms for Manufacturing Operations
Man / Mindpower (Personnel) : Training, skill level, fatigue, operational adherence, ergonomics.
Machine (Equipment) : Machine wear, tooling state, maintenance history, speed settings, vibration.
Method (Process) : Work instructions, standard operating procedures (SOPs), sequence of steps, cycle timing.
Material (Raw Materials) : Material grade, supplier variability, chemical composition, moisture content.
Measurement (Inspection) : Gauge calibration, measurement error, operator inspection technique, GR&R.
Mother Nature / Environment : Temperature, relative humidity, dust/particulates, ambient lighting, static.
2. Service & Transactional Frameworks (4 Ps / 8 Ps)
Policies : Systemic rules, management guidelines, performance incentives.
Procedures : Documented workflow rules, billing steps, approval hierarchies.
People : Staffing levels, domain expertise, customer interaction scripts.
Plant / Place : Facilities layout, IT infrastructure, software responsiveness.
Cause & Effect (C&E) Matrix
The Cause & Effect Matrix is a semi-quantitative tool used to narrow down a large list of potential process inputs ( X X X s derived from process maps and fishbone diagrams) by evaluating their impact on key customer output metrics ( Y Y Y s / CTQs).
Step-by-Step Construction Procedure
Identify Customer Output Requirements ( Y Y Y s) : Extract key CTQs from the Voice of the Customer (VOC) or charter.
Assign CTQ Importance Weights ( W j W_j W j ) : Rate the importance of each Y j Y_j Y j on a scale of 1 to 10 (10 = critical to customer).
List Potential Process Inputs ( X i X_i X i ) : Enter candidate input variables along the rows of the matrix.
Score Relationship Strengths ( R i j R_{ij} R ij ) : Evaluate the correlation between input X i X_i X i and output Y j Y_j Y j using a standardized non-linear scale:
- 0 : No correlation
- 1 : Weak relationship
- 3 : Moderate relationship
- 9 : Strong direct relationship
Calculate Total Weighted Input Scores ( S i S_i S i ) : S i = ∑ j = 1 m ( R i j × W j ) S_i = \sum_{j=1}^{m} (R_{ij} \times W_j) S i = j = 1 ∑ m ( R ij × W j )
Inputs with the highest weighted scores ( S i S_i S i ) are prioritized for data collection, FMEA evaluation, and hypothesis testing in the Analyze phase.
Fault Tree Analysis (FTA)
The Body of Knowledge names fault tree analysis as a root cause tool alongside the 5 Whys and the cause-and-effect diagram, and it is the one that handles combinations of causes. An FTA is a top-down, deductive graphical model: it starts from a single defined top event -- the undesired outcome -- and works downward through Boolean logic gates to the basic events that can produce it.
Structure and symbols
- Symbol Element Meaning
- Rectangle Top or intermediate event An event resolved further by the gate beneath it
- Circle Basic event A root failure requiring no further development
- Diamond Undeveloped event Real but not analyzed further, usually for lack of data
- AND gate Conjunction The output occurs only if every input occurs
- OR gate Disjunction The output occurs if any input occurs
- Quantifying the tree
With independent basic events, probabilities propagate upward by gate type: P AND = ∏ p i P OR = 1 − ∏ ( 1 − p i ) P_{\text{AND}} = \prod p_i \qquad P_{\text{OR}} = 1 - \prod (1 - p_i) P AND = ∏ p i P OR = 1 − ∏ ( 1 − p i )
Worked example. An unplanned line stop occurs through an OR gate fed by two intermediate events. Pump failure is an AND gate over the primary pump ( p = 0.02 p = 0.02 p = 0.02 ) and the standby pump ( p = 0.05 p = 0.05 p = 0.05 ); control failure is an OR gate over a sensor ( p = 0.01 p = 0.01 p = 0.01 ) and the PLC ( p = 0.004 p = 0.004 p = 0.004 ).
- Pump branch: 0.02 × 0.05 = 0.001 0.02 \times 0.05 = 0.001 0.02 × 0.05 = 0.001
- Control branch: 1 − ( 0.99 ) ( 0.996 ) = 0.01396 1 - (0.99)(0.996) = 0.01396 1 − ( 0.99 ) ( 0.996 ) = 0.01396
Top event: 1 − ( 1 − 0.001 ) ( 1 − 0.01396 ) = 0.01495 1 - (1 - 0.001)(1 - 0.01396) = 0.01495 1 − ( 1 − 0.001 ) ( 1 − 0.01396 ) = 0.01495 , about 1.5%
Redundancy is what makes the pump branch small: two independent failures must coincide. The control branch, with no redundancy, contributes more than ten times as much risk despite having far more reliable components. That comparison is the practical payoff of the method.
Minimal cut sets
A cut set is any combination of basic events that produces the top event; a minimal cut set is one from which nothing can be removed. Here they are { \{ { sensor } \} } , { \{ { PLC } \} } , and { \{ { primary pump, standby pump } \} } . Single-element cut sets are single-point failures and are always the first target for improvement, because one event alone defeats the system.
FTA versus FMEA
- FMEA (section 7.11) FTA
- Direction Bottom-up, inductive Top-down, deductive
- Starting point A component failure mode A defined system-level failure
- Handles combinations No -- one failure mode at a time Yes -- AND gates model coincident causes
- Output RPN ranking of failure modes Cut sets and a top-event probability
Use FMEA to sweep broadly for what can go wrong, and FTA when one specific severe outcome must be explained and its combinations of causes quantified.
The A3 report
The A3 is a structured problem-solving report named for the paper size it must fit on (297 x 420 mm). Developed within the Toyota Production System, its constraint is its method: forcing the whole problem onto one page prevents the unread forty-slide deck and compels the team to distinguish evidence from narrative.
- A3 section Content PDCA stage
- Background Why this problem, and why now Plan
- Current condition Measured baseline, usually drawn rather than described Plan
- Goal / target condition The specific target and date Plan
- Root cause analysis 5 Whys, fishbone, or fault tree evidence Plan
- Countermeasures Proposed actions traced to specific causes Do
- Implementation plan Who, what, when Do
- Follow-up / confirmation How the effect was verified, and what remains Check / Act
Two disciplines separate a real A3 from a status form. Every countermeasure must trace to a named root cause -- an action with no cause above it is a guess -- and the follow-up section is completed after implementation with measured results, not filled in optimistically at the start.
Pareto charts , the remaining tool named in this Body of Knowledge topic, are developed with the other graphical methods in section 6.11; in root cause work they serve to select which problem or defect category to subject to the tools above, applying the vital-few principle before any cause analysis begins.
- Gap Analysis
- The Body of Knowledge treats gap analysis as a distinct Analyze-phase method: analyze scenarios to
- identify performance gaps, and compare current and future states using predefined metrics. It is
- complementary to root cause analysis -- gap analysis establishes how far performance is from where it
needs to be, and root cause analysis explains why .
The four elements
- Element Question Artifact
- Current state Where are we now, measured? Baseline with sample size and period
- Future state Where do we need to be, and on what basis? Target with a stated source
- Gap What is the difference, in the metric's own units? Quantified difference
- Cause and closure plan Why does the gap exist and what closes it? Prioritized action list
- The step teams skip is the basis of the future state. A target is only defensible when it comes from
- a named source: a customer requirement or specification, a benchmark with a stated closure fraction, an
- entitlement calculation, a regulatory limit, or a strategic plan commitment. "Best in class" without a
reference is not a future state.
- Predefined metrics
- The phrase "using predefined metrics" in the Body of Knowledge is doing real work. Both states must be
- measured with the same operational definition, the same data source, and the same denominator ,
- defined before the comparison. Gaps that appear when the metric definition changes between the two
- states are measurement artifacts, and they are the most common way a project appears to succeed without
anything improving.
- Entitlement
- Entitlement is the best performance the current process has actually demonstrated -- not a
- theoretical maximum and not a benchmark. It is usually taken as the best observed period, shift, or
- operator, and it is powerful for two reasons: it is unarguably achievable, because the process has
- already done it, and the gap between entitlement and average is pure variation with no capital
requirement.
- Measure Value Gap type
- Current average yield 91.4% --
- Best observed week (entitlement) 96.1% 4.7 points, achievable by reducing variation alone
- Benchmark median 97.5% 6.1 points, needs process change
- Theoretical maximum 100% Not a target
- Splitting the total gap this way tells the team where the work is. The entitlement gap is a control and
consistency problem; the remainder is a capability problem that requires a different kind of solution.
Multi-dimensional gap analysis
Where several dimensions matter, tabulate rather than narrate:
- Dimension Current state Future state Gap Source of target
- First pass yield 91.4% 97.0% 5.6 points Operating plan
- Order lead time 11.4 days 6.0 days 5.4 days Collaborative benchmark median
- Cost per unit $14.20 $12.75 $1.45 Contract renewal requirement
- Capability, Cpk 0.94 1.33 0.39 Customer specification
- Then rank by weighted business impact rather than by gap size, because the largest numerical gap is
rarely the most valuable one to close.