7.13 Waste Analysis: The Seven Classic Wastes and Resource Under-Utilization

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

7.13 Waste Analysis: The Seven Classic Wastes and Resource Under-Utilization

Key Takeaways Explain This Topic AI

The seven classic wastes are overproduction, inventory, defects, over-processing, waiting, motion, and transportation.

Resource under-utilization, sometimes called non-utilized talent, is the eighth waste added to the classic seven.

Overproduction is considered the worst waste because it generates inventory, waiting, transportation, and hidden defects.

Value-added activity must satisfy three tests: the customer would pay for it, it changes the item, and it is done right the first time.

Motion is movement of people; transportation is movement of material, information, or product.

Last updated: August 2026

Value-added and non-value-added

Every activity in a process falls into one of three classes.

Class Test Treatment
Value-added (VA) Passes all three tests below Optimize

Business-non-value-added (BNVA) Fails the customer test but is required by law, regulation, or a business necessity Minimize

Non-value-added (NVA) Fails all tests and is not required Eliminate

The three value-added tests:

Would the customer be willing to pay for this activity?

Does the activity physically change the product, service, or information toward what the customer

wants?

Is it done right the first time ?

An activity must pass all three. Regulatory inspection in a medical device plant fails test 1 but is
mandatory, so it is business-non-value-added: minimize it, do not eliminate it. Rework fails test 3 by

definition and is always waste, however necessary it feels.

In most transactional processes, value-added time is under 10% of total lead time , which is why

attacking the waiting and queuing is worth far more than speeding up the work itself.

The seven classic wastes

Waste Definition Typical symptoms Common causes

Overproduction Producing more, sooner, or faster than the next process needs Finished goods stock, batch running, "keeping the machine busy" Large batch sizes, push scheduling, long changeovers

Inventory Any material or information beyond the minimum needed Raw, WIP, and finished stock; backlogs; queues; email in-trays Overproduction, unreliable supply, long lead times

Defects Output that does not conform, requiring rework, scrap, or repeat Scrap, rework loops, returns, re-entered data Process variation, unclear standards, poor MSA

Over-processing More work, tighter tolerance, or more features than the customer requires Multiple approvals, duplicate data entry, unnecessary precision Unclear requirements, legacy procedures, no CTQ definition

Waiting Idle time for people, material, or information Queues, machine idle time, waiting on approvals or information Line imbalance, unreliable equipment, batching

Motion Unnecessary movement of people Walking for tools, reaching, searching, excessive keystrokes Poor layout, no 5S, badly designed screens

Transportation Unnecessary movement of material, product, or information Forklift trips, handoffs between departments, document routing Poor layout, functional silos, centralized operations

Mnemonics: TIMWOOD (Transportation, Inventory, Motion, Waiting, Over-processing, Overproduction,

Defects), or DOWNTIME when the eighth waste is included.

Motion versus transportation
This distinction is a reliable exam question. Motion moves people; transportation moves things. An
operator walking 30 metres to a tool crib is motion. A forklift carrying a pallet 30 metres to the next
operation is transportation. In transactional processes, an analyst clicking through five screens to

find a customer record is motion; routing a file to another department for approval is transportation.

Why overproduction is the worst

Overproduction is conventionally ranked the most damaging because it generates the others :

It creates inventory , which needs space, handling, and capital.

Inventory creates transportation and motion to move and find it.

It creates waiting , because units queue for the next step.

It hides defects , because a defect made early is not discovered until the batch is consumed, by

which time hundreds of units share it.

The last point is the most consequential. Producing in large batches lengthens the feedback loop between

making a defect and detecting it, which multiplies the cost of every process problem.

The eighth waste: resource under-utilization
The Body of Knowledge names resource under-utilization alongside the classic seven. It covers
unused human capability: skills not applied, ideas not solicited, people assigned below their capability,

and knowledge that stays with an individual.

Symptoms: an improvement suggestion scheme with no responses; operators who know exactly why the process
fails and have never been asked; a trained Green Belt doing no project work; a single person who is the

only one who can run a critical operation.

The waste is real and measurable through its consequences -- rediscovering known problems, longer

problem-solving cycles, and turnover of skilled staff. It also under-utilizes non-human resources:

equipment idle between campaigns, floor space, and licensed software nobody uses.

Conducting a waste analysis

Walk the process at the gemba , following one unit end to end.

Record every activity with its time and its class: VA, BNVA, or NVA.

Tag each NVA activity with its waste category.

Quantify , in time and money. "Motion" is an observation; "operators walk 4.2 km per shift, 38

minutes of paid time per operator per day" is a business case.

Compute process cycle efficiency -- value-added time divided by total lead time -- to size the

opportunity.

Pareto the waste by time or cost, and attack the largest categories.

Two disciplines make the difference. First, time the activities rather than estimating them ; teams
consistently underestimate waiting and overestimate touch time. Second, look for the waste that
generates other waste : eliminating one instance of overproduction often removes inventory,
transportation, and waiting at the same time, which is why the value stream map is the right instrument

rather than a step-by-step activity list.

Waste and the DMAIC phases

Phase Waste-related activity
Define Identify obvious waste in the SIPOC; scope around a value stream rather than a department
Measure Value stream map with VA/NVA classification; compute process cycle efficiency
Analyze Waste analysis proper: categorize, quantify, and prioritize the NVA activity
Improve Apply lean methods -- pull, kanban, 5S, SMED, standard work, poka-yoke
Control Visual controls and standard work that prevent the waste from returning