Lean Six Sigma Expert

1.01 Introduction to Six Sigma and Organizational Goals

2.01 Introduction
2.02 Value of Sigma
2.03 Meaning of Six Sigma and Quality
2.04 DMAIC Process
2.05 Key Terms
2.06 Sigma Conversion Table
2.07 Goals and Six Sigma Projects
2.08 Structure of a Six Sigma Team
2.09 Organizational Drivers and Metrics
2.10 Four-box Model vs. Strategy Maps
2.11 Key Takeaways
Knowledge Check

3.01 Introduction
3.02 Lean Concepts
3.03 Principles and Three Ms of Lean
3.04 Lean Wastes
3.05 Lean Tools and Techniques
3.06 Theory of Constraints (TOC)
3.07 Value Stream Mapping (VSM)
3.08 Key Takeaways
Knowledge Check

4.01 Introduction
4.02 Roadmap for Design for Six Sigma (DFSS)
4.03 Quality Function Deployment (QFD)
4.04 Failure Modes and Effects Analysis (FMEA)
4.05 Key Takeaways
Knowledge Check

1.01 Introduction to Define Phase

2.01 Introduction
2.02 Steps in Project Selection
2.03 Benchmarking, Types of Benchmarking, and Best Practices
2.04 Process Elements
2.05 SIPOC Process
2.06 Owners and Stakeholders
2.07 Key Takeaways
Knowledge Check

3.01 Introduction
3.02 Customer Identification
3.03 Tips for Effective VoC Questions
3.04 VoC and Data Collection
3.05 Data Collection Methods
3.06 Proactive Data Sources
3.07 Proactive Data Sources
3.08 Proactive Data Sources
3.09 VoC Proactive Data Collection Methods
3.10 Customer Requirements
3.11 Critical-to-Quality Factor
3.12 CTQ Example
3.13 Quality Function Deployment
3.14 Sections of House of Quality (HOQ)
3.15 Post HOQ Matrix
3.16 Key Takeaways
Knowledge Check

4.01 Introduction
4.02 The Project Charter
4.03 Problem Statement and Project Objectives
4.04 Project Scope and Its Interpretation
4.05 Project Scope and Its Interpretation
4.06 Project Metrics and Its Types
4.07 Project Planning and Documentation
4.08 Project Plan
4.09 Project Plan Schedule
4.10 Project Plan Risks
4.11 Benefits of Risk Analysis and Project Closure
4.12 Key Takeaways
Knowledge Check

5.01 Introduction
5.02 Affinity Diagram and Interrelationship Diagram
5.03 Tree Diagram and Matrix Diagram
5.04 Prioritization Matrices, Activity Network Diagrams, and Process Decision Program Chart (PDPC)
5.05 Key Takeaways
Knowledge Check

6.01 Introduction
6.02 Process Performance
6.03 Defects per Unit (DPU)
6.04 Throughput Yield (TPY) and Rolled Throughput Yield (RTY)
6.05 Defects per Million Opportunities (DPMO) and Sigma Level
6.06 Cost of Quality (COQ) and Process Capability
6.07 Key Takeaways
Knowledge Check

7.01 Introduction
7.02 Team Dynamics
7.03 Negative Team Dynamics
7.04 Team Stages
7.05 Types of Challenges
7.06 Team Roles and Responsibilities
7.07 Team Communication
7.08 Team Tools
7.09 Key Takeaways
Knowledge Check

8.01 Case Study

1.01 Introduction to Measure Phase

2.01 Introduction
2.02 Process Maps
2.03 Flowcharts
2.04 Process Documentation
2.05 Key Takeaways
Knowledge Check

3.01 Introduction
3.02 Roles of Probability and Statistics
3.03 Addition Rules and Multiplication Rules
3.04 Permutation and Combination
3.05 Key Takeaways
Knowledge Check

4.01 Introduction
4.02 Data, Its Types, and Selection
4.03 Measurement Scales, Collection Methods, and Sampling
4.04 Data Collection Plan and Data Coding
4.05 Measures of Central Tendency and Dispersion
4.06 Microsoft Excel for Descriptive Statistics
4.07 Frequency Distribution
4.08 Stem and Leaf Plots and Box and Whisker Plots
4.09 Scatter Diagrams
4.10 Other Visualization Tools and Normal Probability Plots
4.11 Key Takeaways
Knowledge Check

5.01 Introduction
5.02 Classes of Distribution and Key Terms
5.03 Types of Statistical Distribution
5.04 Binomial and Poisson Distribution
5.05 Normal Distribution
5.06 Chi-Square, T, and F-Distribution
5.07 Central Limit Theorem (CLT)
5.08 Key Takeaways
Knowledge Check

6.01 Introduction
6.02 Measurement System Analysis and Its Properties
6.03 Measurement System Characteristics
6.04 Measurement System Concepts
6.05 Gage R&R Study
6.06 Attribute Gage R&R
6.07 Key Takeaways
Knowledge Check

7.01 Introduction
7.02 Process Stability and Normality
7.03 Process Capability Analysis and Studies
7.04 Process Capability Index and Example
7.05 Process Performance Indices
7.06 Process Mean Shift and Variation
7.07 Process Capability for Attribute Data
7.08 Key Takeaways
Knowledge Check

8.01 Case Study

1.01 Introduction to Analyze Phase

2.01 Introduction
2.02 Hypothesis Testing Basics
2.03 Type 1 and Type 2 Errors
2.04 Sample Size
2.05 P-Value and Test Statistics
2.06 Tests for Means, Variances, and Proportions
2.07 1-Sample Test
2.08 2-Sample Test
2.09 T-Test
2.10 ANOVA and Chi-Square Distribution
2.11 Hypothesis Testing with Nonnormal Data
2.12 Key Takeaways
Knowledge Check

3.01 Introduction
3.02 Multi-Vari Analysis
3.03 Correlation
3.04 Regression Analysis
3.05 Residual Analysis and Linear Regression
3.06 Multiple Regression
Knowledge Check

4.01 Case Study

1.01 Introduction to Improve Phase

2.01 Introduction
2.02 DOE Regression and Basic Terms
2.03 DOE Error
2.04 Key Takeaways
Knowledge Check

3.01 Introduction
3.02 Root Cause Analysis (RCA)
3.03 Key Takeaways
Knowledge Check

4.01 Introduction
4.02 Lean Tools and Techniques
4.03 Cycle Time Reduction, Kaizen, and Kaizen Blitz
4.04 Key Takeaways
Knowledge Check

5.01 Introduction
5.02 Pugh Analysis and Solution Prioritization Matrix
5.03 SCAMPER Tool
5.04 Brainstorming, Cost-Benefit Analysis, Solution Screening, and Piloting
5.05 Key Takeaways
Knowledge Check

6.01 Case Study

1.01 Introduction to Control Phase

2.01 Introduction
2.02 SPC Basics
2.03 Control Charts and Analysis
2.04 Choosing an Appropriate Control Chart
2.05 Xbar Chart and Principles
2.06 I-MR Chart and Principles
2.07 Control Charts for Attribute Data
2.08 np-Chart and P-Chart
2.09 c-Chart and u-Chart
2.10 CUSUM and EWMA Charts
2.11 Key Takeaways
Knowledge Check

3.01 Introduction
3.02 Control Plan and Response Plan
3.03 Cost Benefit Analysis, KPIV, and KPOV
3.04 Control Level and Transactional Control Plan
3.05 Key Takeaways
Knowledge Check

4.01 Introduction
4.02 Total Productive Maintenance
4.03 Visual Factory
4.04 5S
4.05 Key Takeaways
Knowledge Check

5.01 Case Study

1.01 Exam Tips
1.02 ASQ Exam
1.03 IASSC Exam

1.01 Reducing Sealant Waste
1.02 Improving Manufacturing Process

1.01 Course Introduction

2.01 Introduction
2.02 Importance of Minitab
2.03 Demo: Minitab
2.04 Demo: Minitab Menu Bar
2.05 Demo: Graph Tab
2.06 Demo: Stat Tab
2.07 Demo: Other Tabs
2.08 Common Pitfalls in Analyzing Data
2.09 Bias
2.10 Error in Methodology
2.11 Problems in Interpretation
2.12 Avoiding the Common Pitfalls
2.13 Key Takeaways
Knowledge Check

3.01 Introduction
3.02 Basic Statistics
3.03 Case Study: Amazing Inc
3.04 Demo: Bar Chart and Pie Chart
3.05 Demo: Pareto Chart and Histogram
3.06 Demo: Box Plot and Dot Plot
3.07 Demo: Individual Value Plot
3.08 Demo: Run Chart
3.09 Demo: Measuring Central Tendency and Spread
3.10 Demo: Normality Test
3.11 Key Takeaways
Knowledge Check

4.01 Introduction
4.02 Control Charts
4.03 Case Study: Couturier Garments
4.04 Couturier Garments: Six Sigma Approach
4.05 Demo: I-MR Chart
4.06 Demo: X-R Chart
4.07 Demo: X-S Chart
4.08 Demo: C Chart
4.09 Demo: U Chart
4.10 Demo: NP Chart
4.11 Demo: P Chart
4.12 Key Takeaways
Knowledge Check

5.01 Introduction
5.02 Case Study: Couturier Garments
5.03 Measurement System Analysis (MSA)
5.04 Gage R and R
5.05 Demo: Gage R and R
5.06 Attribute Gage R and R
5.07 Demo: Attribute Gage R and R
5.08 Case Study: Conclusion
5.09 Key Takeaways
Knowledge Check

6.01 Introduction
6.02 Process Capability
6.03 Case Study: Couturier Garments
6.04 Demo: Capability Analysis for Normal Data
6.05 Demo: Capability Analysis for Nonnormal Data
6.06 Demo: Poisson Capability Report
6.07 Demo: Binomial Capability Report
6.08 Key Takeaways
Knowledge Check

7.01 Introduction
7.02 Hypothesis Testing
7.03 Case Study: Life Line Inc
7.04 One-Sample t-test
7.05 Two-Sample t-test
7.06 Paired t-test
7.07 One-Sample proportion test
7.08 Two-Sample proportion test
7.09 One-Sample Variance test
7.10 Two-Sample Variance test
7.11 Chi-square: Goodness-of-fit-test
7.12 Chi-square: Test of association
7.13 Chi-square: cross tabulation
7.14 Key Takeaways
Knowledge Check

8.01 Introduction
8.02 Nonparametric tests
8.03 One-Sample sign test
8.04 Two-Sample Mann-Whitney U test
8.05 Friedman test
8.06 Mood's Median test
8.07 One-Sample Wilcoxon Test
8.08 Kruskal Wallis Test
8.09 Key Takeaways
Knowledge Check

9.01 Introduction
9.02 Case Study: Metro Hospital
9.03 Metro Hospital: Six Sigma Approach
9.04 Correlation Coefficient
9.05 Demo: Correlation Coefficient
9.06 Regression Analysis
9.07 Case Study: Life Line Inc
9.08 Simple Linear Regression
9.09 Demo: Simple Linear Regression
9.10 Multiple Linear Regression
9.11 Demo: Multiple Linear Regression
9.12 Key Takeaways
Knowledge Check

10.01 Introduction
10.02 Case Study: Great Investors
10.03 Analysis of Variance (ANOVA)
10.04 Demo: Normality
10.05 Demo: Equal Variation
10.06 Demo: Residuals
10.07 Main Effects Plot
10.08 Demo: Main Effects Plot
10.09 Interaction plot
10.10 Demo: Interaction Plot
10.11 Key Takeaways
Knowledge Check

11.01 Introduction
11.02 Case Study: Jeeshan Automotive
11.03 One Factor at a Time Approach
11.04 Design of Experiments
11.05 Demo: Full Factorial Design
11.06 Demo: Anlayze Factorial Design
11.07 Demo: Factorial Plots
11.08 Demo: Response Optimizer
11.09 Key Takeaways
Knowledge Check

1.01 Course Introduction

1.01 Introduction to Define Phase
1.02 Learning Objectives
1.03 Six Sigma
1.04 Lean
1.05 Sigma Shift
1.06 Yield
1.07 Continuous Improvement Process Evolution
1.08 Six Sigma Deliverables
1.09 Problem Solving Strategy
1.10 VOC Campaign
1.11 VOC Tools
1.12 VOB
1.13 VOE
1.14 KANO Analysis
1.15 Six Sigma Roles and Responsibilities
1.16 Project Champion and Master Black Belt
1.17 Black Belt and Yellow Belt
1.18 Drivers of Six Sigma
1.19 Key Takeaways

2.01 Learning Objectives
2.02 Process
2.03 Project Charter
2.04 Critical to Quality (CTQ)
2.05 Cost of Poor Quality (COPQ)
2.06 Calculating COPQ
2.07 Pareto Analysis (80-20 rule)
2.08 Basic Six Sigma Metrics
2.09 Key Takeaways

3.01 Learning Objectives
3.02 Selecting Lean Six Sigma Projects
3.03 Project Selection Roadmap
3.04 Project Charter: Elements
3.05 Project Charter: Business Case
3.06 Project Charter: Problem Statement
3.07 Project Charter: Goal Statement
3.08 Project Charter: Scope
3.09 Project Charter: Key Milestones
3.10 Project Charter: Team Selection
3.11 Tuckman’s Stages of Team Formation
3.12 The RACI and RASIC Matrix
3.13 Expected Financial Benefits
3.14 Developing Project Metrics
3.15 Key Performance Indicator (KPI)
3.16 Financial Evaluation and Benefits Capture
3.17 Net Present Value (NPV)
3.18 Key Takeaways

4.01 Learning Objectives
4.02 Lean
4.03 Principles of Lean
4.04 Lean Methodology
4.05 Lean and Six Sigma
4.06 3Ms of Lean
4.07 Categories of Waste (TIMWOODS)
4.08 Categories of Waste (DOWNTIME)
4.09 5S
4.10 Steps in 5S: Part One
4.11 Steps in 5S: Part Two
4.12 Key Takeaways
4.13 Activity
4.14 Solution

1.01 Introduction to Measure Phase
1.02 Learning Objectives
1.03 Tools to Define a Process
1.04 Cause-and-Effect Diagram
1.05 Drawing a Fishbone Diagram
1.06 Root Cause
1.07 Process Mapping
1.08 Creating a Process Map
1.09 Process Mapping Levels
1.10 Four Types of Process Maps
1.11 SIPOC Process Map
1.12 Value Stream Maps
1.13 Value Stream Maps: Key Metrics
1.14 X-Y Diagrams or Scatter Plots
1.15 Failure Mode and Effects Analysis (FMEA)
1.16 FMEA Process
1.17 FMEA Template
1.18 Severity, Occurrence, and Detection Table
1.19 Risk Priority Number (RPN)
1.20 Key Takeaways

2.01 Learning Objectives
2.02 Data
2.03 Measurement Scales
2.04 Basic Statistics
2.05 Measures of Central Tendency
2.06 Measures of Dispersion
2.07 Data Collection Plan
2.08 Data Collection Plan: Steps
2.09 Develop a Measurement Plan
2.10 Collect Data
2.11 Sampling
2.12 Sampling Methods
2.13 Graphical Analysis
2.14 Graphical Analysis: Tools
2.15 Demo: Introduction to Minitab
2.16 Demo: Box Plot (One Variable)
2.17 Demo: Box Plot (Three Variables)
2.18 Demo: Time Series Plot
2.19 Normal Distribution
2.20 Standard Normal Distribution
2.21 Demo: Normality Test
2.22 Key Takeaways

3.01 Learning Objectives
3.02 Measurement System Analysis: Overview
3.03 Good and Poor Measurement System Analysis
3.04 Measurement Error Categories
3.05 MSA Sources of Variation
3.06 Repeatability
3.07 Reproducibility
3.08 Accuracy
3.09 Bias
3.10 Stability
3.11 Linearity
3.12 MSA Types
3.13 Gage R&R Guidance in MINITAB
3.14 Gage R&R Ground Rules
3.15 Demo: Gage R&R Continuous Data
3.16 Attribute Agreement Analysis (AAA)
3.17 Attribute Gage Study
3.18 Demo: Gage R&R Attribute Data
3.19 Key Takeaways

4.01 Learning Objectives
4.02 Process Capability Overview
4.03 RUMBA Analysis
4.04 Process Capabilities
4.05 Data Types
4.06 Baseline Performance: Part One
4.07 Baseline Performance: Part Two
4.08 Components of Variation
4.09 Process Stability
4.10 Process Capability Indices
4.11 Demo: Capability Analysis Continuous Data
4.12 Process Capability Indices: Example
4.13 Demo: Capability Analysis Continuous Data Sigma Level
4.14 Z Score
4.15 Process Baseline
4.16 Defects per Unit
4.17 Defects per Million Opportunities
4.18 Attribute Data: Example
4.19 Short-Term and Long-Term Process Capability
4.20 Key Takeaways
4.21 Activity
4.22 Solution

1.01 Introduction to Analyze Phase
1.02 Learning Objectives
1.03 Frequency Distribution
1.04 Demo: Histogram
1.05 Probability Distribution
1.06 Types of Probability Distributions
1.07 Types of Discrete Probability Distributions
1.08 Types of Continuous Probability Distribution
1.09 Key Takeaways

2.01 Learning Objectives
2.02 Inferential Statistics
2.03 Branches of Inferential Statistics
2.04 Central Limit Theorem (CLT)
2.05 Key Takeaways

3.01 Learning Objectives
3.02 Basics of Hypothesis Testing
3.03 Confidence Interval
3.04 Significant Difference Between Datasets
3.05 Detecting Significance
3.06 Statistical Hypothesis Test
3.07 Hypothesis Testing Risks
3.08 Beta Risk
3.09 Power of a Hypothesis Test
3.10 Sample Size
3.11 Hypothesis Testing Roadmap
3.12 Key Takeaways

4.01 Learning Objectives
4.02 Normal Data
4.03 One-Sample T-test
4.04 One-Sample T-Test: Sample Size
4.05 Demo: One-Sample T-Test
4.06 Two-Sample T-Test
4.07 Two-Sample T-Test Example
4.08 Demo: Two-Sample T-Test
4.09 Demo: Bartlett Test
4.10 Paired T-Test
4.11 Demo: Paired T-Test
4.12 Z-Test for Hypothesis Testing
4.13 ANOVA
4.14 Demo: ANOVA
4.15 Residual Plots
4.16 Key Takeaways

5.01 Learning Objectives
5.02 Non-Parametric Tests
5.03 Mann Whitney Test
5.04 Demo: Mann Whitney Test
5.05 Kruskal Wallis Test
5.06 Demo: Kruskal Wallis Test
5.07 Mood's Median Test
5.08 Demo: Mood's Median Test
5.09 Friedman Test
5.10 Demo: Friedman Test
5.11 One-Sample Sign Test
5.12 Demo: One-Sample Sign Test
5.13 One-Sample Wilcoxon Test
5.14 Demo: One-Sample Wilcoxon Test
5.15 One-Sample Proportion Tests
5.16 Demo: One-Sample Proportion Test
5.17 Two-Sample Proportion Tests
5.18 Demo: Two-Sample Proportion Test
5.19 Chi-Square Tests
5.20 Demo: Chi-Square Test of Independence
5.21 Chi-Square Goodness-of-Fit Test
5.22 Demo: Chi-Square Goodness of Fit
5.23 Chi-Square Cross Tabulation
5.24 Demo: Chi-Square Cross Tabulation
5.25 Demo: Two-Sample T-Test with Levene F-Test
5.26 Key Takeaways
5.27 Exercise One
5.28 Exercise Two

1.01 Introduction to Improve Phase
1.02 Learning Objectives
1.03 Correlation
1.04 Demo: Correlation
1.05 Demo: Correlation Test Using Scatter Plot
1.06 Correlation and Causation
1.07 Predictor Measures and Results
1.08 Correlation Coefficients
1.09 Regression Analysis
1.10 Demo: Regression
1.11 Residual Analysis
1.12 Key Takeaways

2.01 Learning Objectives
2.02 Multi-Vari Analysis
2.03 Demo: Multi-Vari Analysis
2.04 Nonlinear Regression
2.05 Multiple Linear Regression
2.06 Demo: Multiple Linear Regression
2.07 Variance Inflation Factor (VIF)
2.08 Variance Inflation Factor (VIF): Example
2.09 Confidence Interval for Multiple Linear Regression
2.10 Box-Cox Transformation
2.11 Demo: Box-Cox Transformation
2.12 Key Takeaways

3.01 Learning Objectives
3.02 Design of Experiments (DOE)
3.03 Phases of DOE Process
3.04 Optimization and Confirmation Phase
3.05 Types of DOE Strategies
3.06 Full Factorial and Fractional Factorial Approaches
3.07 Principles of Experimental Design
3.08 Key Takeaways

4.01 Learning Objectives
4.02 Factorial Designs
4.03 Full Factorial Experiments
4.04 Demo: Full Factorial Experiments
4.05 Quadratic Models
4.06 Types of Response Surface Designs
4.07 Balanced and Orthogonal Designs
4.08 Center Points
4.09 Fractional Factorial Experiment
4.10 Confounding
4.11 Key Takeaways

5.01 Learning Objectives
5.02 Competitor Analysis
5.03 Benchmarking, Types of Benchmarking, and Best Practices
5.04 Team Tools
5.05 Pugh Analysis and Solution Prioritization Matrix
5.06 Process Redesign and Optimization
5.07 Cost Benefit Analysis (CBA)
5.08 Pilot Testing, Implementation, PDCA, and Prototyping
5.09 Project Plan
5.10 Project Plan Schedule
5.11 Project Plan Risks
5.12 Quality Function Deployment (QFD)
5.13 Failure Modes and Effects Analysis (FMEA)
5.14 Change Management in Lean Six Sigma
5.15 Roadmap for Design for Six Sigma
5.16 Key Takeaways

1.01 Introduction to Control Phase
1.02 Learning Objectives
1.03 Control Methods of Five S
1.04 Sort
1.05 Set in Order
1.06 Shine, Standardize, and Sustain
1.07 Kanban
1.08 Kanban Principles
1.09 Six Steps to Implement Kanban
1.10 Poka-Yoke or Mistake Proofing
1.11 Mistake Proofing: Examples
1.12 Key Takeaways

2.01 Learning Objectives
2.02 Statistical Process Control: Purpose
2.03 Control Charts
2.04 Control Charts: Objectives
2.05 Control Charts: Uses
2.06 Control Charts: Types
2.07 Control Charts: Steps
2.08 Subgroup
2.09 Considerations for Rational Subgrouping
2.10 Charts for Attribute Data
2.11 Tests for Special Causes
2.12 Demo: I-MR Chart
2.13 Demo: X-Bar-R Chart
2.14 Demo: XBar-S Chart
2.15 Demo: P-Chart
2.16 Demo: NP-Chart
2.17 Demo: U-Chart
2.18 Demo: C-Chart
2.19 Demo: CUSUM-Chart
2.20 Demo: EWMA-Chart
2.21 Key Takeaways

3.01 Learning Objectives
3.02 Project Cost Benefit Analysis
3.03 Return on Investment (ROI) and Return on Assets (ROA)
3.04 Cost Benefit Analysis
3.05 Cost Benefit Analysis: Steps
3.06 Net Present Value (NPV) and Internal Rate of Return (IRR)
3.07 Selecting the Right Solutions
3.08 Implementation of Proposed Solutions Roadmap
3.09 Control Plan
3.10 Elements of a Control Plan
3.11 Training, Monitoring, and Aligning Systems and Structures
3.12 Response Plan
3.13 Project Closure
3.14 Key Takeaways
3.15 Exercise

HBR Case Study: Akshaya Patra Foundation

HBR Case Study: Telenor

Lean Management

Lean Six Sigma Application in Information Technology
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Lean Six Sigma in Healthcare
Learn how to apply Lean Six Sigma principles and methodologies to healthcare organizations in this course. It provides an industry-specific, practical understanding of how Lean practices can improve critical processes through real-world examples, case studies, and a hands-on project to reinforce your learning.