9.6 Total Productive Maintenance and Visual Controls

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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

9.6 Total Productive Maintenance and Visual Controls

Key Takeaways Explain This Topic AI

TPM aims for zero breakdowns, zero defects, and zero accidents through operator ownership of routine equipment care.

Autonomous maintenance transfers cleaning, inspection, lubrication, and tightening to operators, freeing technicians for planned and predictive work.

The eight TPM pillars sit on a foundation of 5S, and OEE is TPM's primary measure.

Visual controls make the standard condition and any deviation from it apparent within seconds, without training or interpretation.

Andon systems signal abnormality and call help immediately, converting a hidden problem into a visible one.

Last updated: August 2026

Total productive maintenance
TPM is a company-wide approach to equipment care aimed at zero breakdowns, zero defects, and zero
accidents . Its distinguishing feature is that equipment care is not solely the maintenance

department's job: operators take ownership of routine care for the equipment they run.

Why it belongs in the Control phase
A Six Sigma project that stabilizes a process and hands over a control plan can be undone entirely by
equipment deterioration. Tool wear, contamination, loosening fasteners, and degraded seals all
reintroduce variation gradually and invisibly. TPM is the control mechanism for that failure mode, and

it is the reason the Body of Knowledge lists it under maintaining and sustaining improvements.

Maintenance strategies

Strategy Trigger Cost profile When appropriate
Reactive (run to failure) Failure Low planned, very high unplanned Non-critical, cheap, redundant equipment
Preventive Elapsed time or usage Moderate, predictable Known wear-out mechanisms, Weibull shape above 1

Predictive (condition-based) Measured condition: vibration, thermography, oil analysis Higher setup, lowest total Critical equipment with detectable degradation

Autonomous Operator's daily routine Very low All equipment, as the foundation
Maintenance prevention Design stage Design effort New equipment specification
Note the connection to reliability distributions: preventive replacement only helps when the failure
rate is increasing (Weibull shape parameter above 1). For a constant failure rate, scheduled

replacement provides no benefit, and for a decreasing rate it actively increases failures.

The eight pillars

TPM is conventionally drawn as eight pillars on a 5S foundation:

Autonomous maintenance -- operators clean, inspect, lubricate, and tighten.

Planned maintenance -- scheduled work based on failure history and condition data.

Quality maintenance -- maintain equipment to the condition required for zero defects, not merely

for running.

Focused improvement -- kaizen on the six big losses.

Early equipment management -- feed maintenance and operability lessons into new equipment design.

Training and education -- build operator and technician capability.

Safety, health, and environment -- zero accidents.

TPM in administration -- apply the same thinking to office processes.

Autonomous maintenance
The most distinctive pillar. Operators take on CILT : cleaning, inspecting, lubricating, and
tightening. The insight is that cleaning is inspection -- an operator wiping down a machine finds

the leak, the loose bolt, and the abnormal wear far earlier than a monthly inspection would.

Practical implementation elements: a one-page visual standard at the machine, shadow boards for tools,
transparent guards so conditions can be seen without disassembly, lubrication points colour-coded and
brought to accessible positions, and an abnormality tag system so operators can flag what they cannot

fix themselves.

Measuring TPM
OEE is TPM's primary measure, decomposing loss into availability, performance, and quality against the
six big losses. Supporting measures: mean time between failures (MTBF), mean time to repair (MTTR), the

ratio of planned to unplanned maintenance hours, and schedule compliance for planned work.

Visual controls
A visual control makes the standard condition and any deviation from it apparent at a glance ,
without training, measurement, or interpretation. The test is simple: could someone unfamiliar with the

area identify an abnormal condition within a few seconds?

Levels of visual management

Level Function Examples
1. Visual indicator Shares information Signs, labels, notice boards
2. Visual signal Attracts attention to a condition Andon lights, alarms, coloured flags

3. Visual control Constrains behaviour toward the standard Marked floor locations with capacity limits, kanban squares, min-max lines

4. Visual guarantee Physically prevents the error Poka-yoke fixtures, shaped connectors that only fit one way
Each level is stronger than the one above it. A sign asking people not to exceed a quantity is weaker
than a marked square that physically holds only that quantity, which is weaker than a fixture that

cannot accept an extra unit.

Common visual controls

Control What it makes visible
Shadow boards and tool outlines A missing tool, instantly
Floor marking and address labels Whether material is in the right place, and how much
Min-max level lines on bins and tanks Whether replenishment is due
Green-band gauge markings Whether a reading is normal, without reading the number
Colour-coded lubrication points and matching containers The correct lubricant, without a manual
Match marks on fasteners and flanges Whether a bolt has loosened
Kanban cards and squares Whether production is authorized
Andon lights and boards Abnormality and the call for help
Production status boards, hour-by-hour Whether the line is on plan right now
Andon
An andon is a signalling system, typically a light column or overhead board, that an operator
activates when an abnormality occurs. Conventional colours: green normal, yellow help needed or minor

issue, red line stopped or serious problem, blue quality issue.

Andon works only if two conditions hold: the operator has the authority to pull it without
permission, and someone responds within a defined time . An andon system where pulls are discouraged

by output targets, or where nobody comes, converts into a system that teaches operators to hide problems.

Visual controls in the control plan
Visual controls are the most durable control mechanism available, because they do not depend on memory,

vigilance, or a chart being read. When designing the Control phase, prefer them in this order:

Eliminate the possibility of the error by design (poka-yoke, level 4).

Make deviation impossible to miss (visual control, level 3).

Signal deviation immediately (andon, level 2).

Monitor statistically (control chart with an OCAP).

Audit and inspect (weakest; detection only, and after the fact).

Most control plans are written starting at level 4 or 5 because those are familiar. A Black Belt who
moves controls up the hierarchy produces a gain that survives the departure of everyone who ran the

project.