8.9 Cycle-Time Reduction: Continuous Flow, SMED, and Heijunka

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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

8.9 Cycle-Time Reduction: Continuous Flow, SMED, and Heijunka

Key Takeaways Explain This Topic AI

Continuous flow moves one unit at a time between operations, eliminating batch queues that dominate lead time.

SMED separates internal setup, which requires the machine stopped, from external setup, which can be done while running.

The SMED sequence is separate internal from external, convert internal to external, then streamline both.

The economic batch quantity falls as setup time falls, so SMED is what makes small-lot production affordable.

Heijunka levels production by volume and by mix, converting lumpy demand into a repeatable pattern.

Last updated: August 2026

• Why cycle time is mostly waiting
• In most processes the unit spends the overwhelming majority of its lead time waiting, not being worked

on. Little's Law makes the mechanism explicit: Lead time = W I P Throughput \text{Lead time} = \frac{WIP}{\text{Throughput}} Lead time = Throughput W I P ​

Reducing lead time therefore means reducing work in process, and the three biggest sources of WIP are

batching, imbalance, and demand turbulence. The three tools in this section address them in turn.

• Continuous flow
• Continuous flow (one-piece flow) moves each unit directly to the next operation as soon as it is

finished, rather than accumulating a batch.

The arithmetic is worth doing once. Three operations at 1 minute each, batch of 10:

Batch processing : all 10 units complete operation 1 (10 min), then operation 2 (10 min), then

operation 3 (10 min). First unit finished at minute 21; last at minute 30.

One-piece flow : first unit finished at minute 3; last at minute 12.

• Lead time falls from 30 minutes to 12, and the first good unit appears at minute 3 rather than 21, which
• also means a defect introduced at operation 1 is discovered within three minutes rather than after ten

units have been made.

Prerequisites for flow:

• Requirement Why If missing
• Balanced cycle times near takt An unbalanced step starves or blocks the line Queues reappear at the bottleneck
• Reliable equipment A stoppage halts the whole chain Flow lines need TPM
• Quality at the source No buffer to absorb defects Needs poka-yoke and in-station verification
• Short setups Frequent changeovers are required for small lots Needs SMED
• Physical proximity Distance forces batching for transport Cellular layout
• Flow exposes problems that inventory used to hide, which is why lean transformation and Six Sigma

variation reduction have to proceed together.

• SMED: single-minute exchange of die
• Developed by Shigeo Shingo, SMED reduces changeover time. "Single minute" means a single digit of

minutes -- under ten -- not sixty seconds.

The central distinction

• Type Definition Example
• Internal setup Can only be done while the machine is stopped Removing and installing the die

External setup Can be done while the machine is running Retrieving the next die, pre-heating it, staging tools, pre-setting fixtures

• The three stages
• Separate internal from external. Simply identifying which activities do not require a stopped
• machine, and doing those in advance, typically cuts changeover time by 30% to 50% with no capital

expenditure. Most organizations do everything after the machine stops purely out of habit.

Convert internal to external. Pre-heat dies, pre-assemble the next fixture on a cart, use

duplicate tooling, standardize die heights so shimming is unnecessary.

• Streamline both. Replace bolts with quick clamps and cam locks, use functional clamps and
• one-turn fasteners, eliminate adjustment through positive stops and numerical settings, run parallel

operations with two people on opposite sides of the machine.

The largest single time sink in most changeovers is adjustment and trial runs after restart .

Eliminating adjustment through gauge blocks, hard stops, and recorded settings often saves more than

speeding up the physical exchange.

• Why SMED changes the economics
• The economic batch quantity is E B Q = 2 D S H EBQ = \sqrt{\frac{2DS}{H}} EBQ = H 2 D S ​ ​
• where D D D is annual demand, S S S the setup cost, and H H H the holding cost per unit per year. Batch size
• scales with the square root of setup cost , so cutting setup time by a factor of 9 cuts the economic

batch size by a factor of 3.

Worked example. D = 100,000 D = 100{,}000 D = 100 , 000 units, holding cost H = $ 2 H = \text{\textdollar}2 H = $ 2 per unit per year, setup cost S = $ 450 S = \text{\textdollar}450 S = $ 450 (a 3-hour changeover at $150 per hour). E B Q = 2 × 100,000 × 450 2 = 45,000,000 = 6,708 units EBQ = \sqrt{\frac{2 \times 100{,}000 \times 450}{2}} = \sqrt{45{,}000{,}000} = 6{,}708 \text{ units} EBQ = 2 2 × 100 , 000 × 450 ​ ​ = 45 , 000 , 000 ​ = 6 , 708 units

After SMED reduces changeover to 20 minutes, S = $ 50 S = \text{\textdollar}50 S = $ 50 : E B Q = 2 × 100,000 × 50 2 = 5,000,000 = 2,236 units EBQ = \sqrt{\frac{2 \times 100{,}000 \times 50}{2}} = \sqrt{5{,}000{,}000} = 2{,}236 \text{ units} EBQ = 2 2 × 100 , 000 × 50 ​ ​ = 5 , 000 , 000 ​ = 2 , 236 units

• Batch size falls by two-thirds, and with it WIP, lead time, and the delay between making a defect and
• finding it. This is why SMED is the enabler for flow and pull rather than an isolated efficiency

project.

• Heijunka: production levelling
• Heijunka levels production so that the same mix and volume are produced in each short interval,

converting lumpy customer demand into a repeatable internal pattern.

Two dimensions:

Volume levelling : produce a consistent quantity per period rather than following demand spikes.

Mix levelling : produce a repeating sequence of product types rather than long runs of each.

Worked example. Weekly demand is 500 A, 300 B, and 200 C, over 5 days.

Approach Sequence Consequence

Unlevelled campaigns AAAAA... (2.5 days), BBBB... (1.5 days), CCC... (1 day) Long lead time for C; large finished stock of A; 2 changeovers

Levelled by volume only 100 A, 60 B, 40 C each day, in three campaigns Better, but still batched within the day

Levelled by mix (heijunka) Repeating AAAAABBBCC pattern throughout each day Any product available within minutes; minimum finished stock

Levelled mix requires short changeovers, which requires SMED. The three tools form a chain: SMED makes

small lots affordable, heijunka sequences them, and flow moves them.

Heijunka is usually visualized on a heijunka box -- a physical grid of pigeonholes, rows for product

types and columns for time intervals, holding kanban cards that release work in the levelled sequence.

• The demand amplification argument
• The deeper reason for levelling is that unlevelled internal scheduling amplifies demand variation
• upstream. Each stage adds safety stock and reorder lumpiness, so a modest fluctuation in customer demand
• becomes a large swing at the supplier -- the bullwhip effect . Levelling the internal schedule
• dampens the signal instead of amplifying it, which is why heijunka benefits suppliers as much as the

plant that adopts it.

Choosing among them

• Symptom Tool
• Units wait in queues between operations Continuous flow
• Large batches justified by long changeovers SMED
• Production runs in campaigns; some products have long lead times Heijunka
• Demand spikes cause overtime then idle time Heijunka volume levelling
• First good part after changeover takes 40 minutes of adjustment SMED stage 3, eliminate adjustment