Duration 8 Days – 56 hrs.

 

Overview

The 8-Day Lean Six Sigma Green Belt Training Course provides participants with a structured and practical understanding of process improvement using the Define–Measure–Analyze–Improve–Control (DMAIC) methodology.

 

The program combines Lean principles, Six Sigma problem-solving techniques, process mapping, waste analysis, data collection, measurement system analysis, process capability, statistical analysis, risk assessment, solution development, Design of Experiments, and statistical process control.

 

Participants will learn how to identify improvement opportunities, understand customer and business requirements, measure baseline performance, validate root causes, develop and pilot solutions, and establish controls that sustain the improvements. Practical exercises using Microsoft Excel and Minitab reinforce the appropriate application and interpretation of analytical tools.

 

The course is applicable to manufacturing, service, banking, healthcare, information technology, logistics, shared services, government, and other process-driven organizations.

 

Objectives 

• Explain the principles and business value of Lean Six Sigma
• Understand and apply the DMAIC improvement methodology
• Identify, evaluate, and prioritize appropriate improvement projects
• Translate customer and business needs into measurable process requirements
• Develop a complete Lean Six Sigma project charter
• Define process scope, boundaries, stakeholders, inputs, outputs, and performance requirements
• Create SIPOC diagrams, high-level process maps, and value stream maps
• Identify value-added and non-value-added process activities
• Recognize process waste, unevenness, and overburden
• Develop operational definitions, sampling approaches, and data-collection plans
• Assess measurement system reliability using Gage R&R and Attribute Agreement Analysis
• Apply descriptive statistics and appropriate data-visualization techniques
• Evaluate process stability, capability, yield, efficiency, and equipment effectiveness
• Calculate and interpret Cp, Cpk, Pp, Ppk, DPMO, FPY, RTY, PCE, and OEE
• Identify, prioritize, and validate the root causes of performance problems
• Evaluate potential process risks using Process FMEA
• Use appropriate statistical tests to validate relationships and differences
• Apply hypothesis testing, ANOVA, Chi-Square, correlation, and regression
• Develop and prioritize appropriate improvement solutions
• Apply Lean countermeasures such as 5S, Poka-Yoke, standard work, SMED, Kanban, and TPM
• Plan and evaluate a controlled pilot implementation
• Apply basic Design of Experiments concepts to optimize processes
• Develop control plans, control charts, reaction plans, and standard operating procedures
• Track project benefits and prepare a Lean Six Sigma project storyboard

 

Target Audience 

• Process Improvement Specialists
• Quality Assurance and Quality Control Professionals
• Engineers and Technical Specialists
• Operations Managers and Supervisors
• Production and Manufacturing Personnel
• Business Process Analysts
• Project Managers and Project Team Members
• Continuous Improvement and Operational Excellence Teams
• Supply Chain, Logistics, and Warehouse Professionals
• Customer Service and Shared Services Personnel
• IT, Finance, Human Resources, and Administrative Professionals
• Employees assigned to lead or support process improvement projects
• Professionals preparing for Lean Six Sigma Green Belt responsibilities

 

Prerequisites 

• Basic knowledge of workplace or business processes
• Basic proficiency in Microsoft Excel
• Basic understanding of percentages, averages, and simple statistical concepts
• Access to Microsoft Excel with the Data Analysis ToolPak enabled
• Access to Minitab for statistical analysis, control-chart, and DOE exercises
• A potential workplace problem or process improvement opportunity for practical application
• Previous Lean Six Sigma experience is not required. Completion of Yellow Belt training is helpful but not mandatory.

 

Course Outline

 

Day 1 – Lean Six Sigma Foundations and Define Phase

Module 1: Introduction to Lean Six Sigma 

• Overview and evolution of Lean and Six Sigma
• Lean principles and waste reduction
• Six Sigma principles and variation reduction
• Benefits of integrating Lean and Six Sigma
• Process improvement and organizational performance
• Lean Six Sigma roles and responsibilities
• Green Belt responsibilities and competencies

 

Module 2: Understanding the DMAIC Roadmap 

• Define, Measure, Analyze, Improve, and Control
• Purpose and expected outputs of each phase
• DMAIC project structure
• Phase reviews and decision gates
• Linking projects to organizational strategy

 

Module 3: Project Identification and Prioritization 

• Sources of Lean Six Sigma projects
• Voice of the Customer
• Voice of the Business
• Understanding customer and business requirements
• Project selection criteria
• Project prioritization
• Assessing project feasibility and potential benefits
• Stakeholder analysis
• Project scope and governance

 

 

Day 2 – Define Phase and Process Understanding

Module 4: Developing the Project Charter 

• Business case
• Problem statement
• Goal statement
• Project scope and boundaries
• Project deliverables
• Project milestones and timeline
• Project team and responsibilities
• Assumptions, constraints, and risks

 

Module 5: High-Level Process Definition 

• Understanding the end-to-end process
• Defining process start and end points
• Identifying suppliers and customers
• Identifying process inputs and outputs
• SIPOC diagram development
• High-level process mapping
• Major process performance requirements

 

Module 6: Customer and Process Requirements 

• Voice of the Customer collection
• Translating customer needs into process requirements
• Critical-to-Quality characteristics
• Kano Model
• Basic, performance, and excitement requirements
• Key Process Output Variables
• Key Process Input Variables

 

 

Day 3 – Lean Process Analysis and Value Stream Mapping

Module 7: Lean Thinking and Process Value 

• Defining value from the customer’s perspective
• Value-added activities
• Business-value-added activities
• Non-value-added activities
• Understanding process flow
• Identifying opportunities for process simplification

 

Module 8: Muda, Mura, and Muri 

• Muda: process waste
• TIMWOODS waste categories
• Transportation
• Inventory
• Motion
• Waiting
• Overproduction
• Overprocessing
• Defects
• Skills or underutilized talent
• Mura: unevenness
• Muri: overburden

 

Module 9: Value Stream and Flow Analysis 

• Value stream mapping
• Material and information flow
• Current-state process analysis
• Takt time
• Cycle time
• Lead time
• Process Cycle Efficiency
• Spaghetti diagram
• Workplace movement analysis
• Identifying delays and flow constraints
• Microsoft Excel applications for process analysis

 

 

Day 4 – Measure Phase

Module 10: Data Types and Measurement 

• Purpose of the Measure phase
• Data types and sources
• Continuous and discrete data
• Quantitative and qualitative data
• Selecting appropriate performance measures
• Data accuracy and integrity

 

Module 11: Operational Definitions and Data Collection 

• Developing operational definitions
• Data-collection planning
• What, where, when, and how to measure
• Identifying data sources
• Sampling principles and techniques
• Determining an appropriate sample
• Data Collection Plan development

 

Module 12: Measurement System Analysis 

• Understanding measurement error
• Accuracy and precision
• Repeatability and reproducibility
• Gage Repeatability and Reproducibility
• Attribute Agreement Analysis
• Evaluating measurement system reliability
• Responding to an unacceptable measurement system

 

Module 13: Descriptive Statistics and Data Visualization 

• Mean, median, and mode
• Range and standard deviation
• Run charts
• Histograms
• Understanding data distributions
• Establishing baseline process performance
• Data analysis using Microsoft Excel and Minitab

 

 

Day 5 – Measure Phase and Process Capability

Module 14: Process Variation and Stability 

• Understanding process variation
• Common-cause variation
• Special-cause variation
• Process stability
• Specification limits
• Control limits versus specification limits
• Relationship between stability and capability

 

Module 15: Process Capability 

• Introduction to process capability
• Potential versus actual capability
• Cp and Cpk
• Pp and Ppk
• Interpreting capability results
• Identifying centered and off-centered processes
• Limitations of capability analysis

 

Module 16: Yield and Performance Measures 

• First-Pass Yield
• Rolled Throughput Yield
• Defects per Million Opportunities
• Process Cycle Efficiency
• Overall Equipment Effectiveness
• Availability, performance, and quality
• Establishing manufacturing and operational performance baselines
• Capability and performance analysis using Microsoft Excel

 

 

Day 6 – Analyze Phase and Risk Analysis

Module 17: Root-Cause Identification 

• Purpose of the Analyze phase
• Moving from symptoms to root causes
• Pareto chart
• Fishbone or cause-and-effect diagram
• 5 Whys analysis
• Cause-and-Effect Matrix
• Prioritizing potential causes
• Linking causes to KPIVs and KPOVs

 

Module 18: Relationship and Regression Analysis 

• Scatter plots
• Identifying positive, negative, and absent relationships
• Correlation concepts
• Introduction to linear regression
• Interpreting regression results
• Distinguishing correlation from causation
• Validating potential causes using data

 

Module 19: Process Risk and Bottleneck Analysis 

• Identifying process risks and failure modes
• Process Failure Mode and Effects Analysis
• Effects and causes of potential failures
• Existing process controls
• Severity, occurrence, and detection
• Prioritizing risk-reduction actions
• Bottleneck identification
• Capacity and constraint analysis
• Analysis using Microsoft Excel or Minitab

 

 

Day 7 – Analyze and Improve Phases

Module 20: Hypothesis Testing 

• Introduction to statistical inference
• Null and alternative hypotheses
• Significance levels and p-values
• Type I and Type II errors
• Parametric and non-parametric considerations
• Selecting an appropriate statistical test
• Interpreting statistical results

 

Module 21: Statistical Tests for Cause Validation 

• Two-sample tests
• Proportion tests
• Analysis of Variance
• Chi-Square tests
• Correlation analysis
• Regression analysis
• Validating differences and relationships
• Statistical analysis using Microsoft Excel or Minitab

 

Module 22: Lean Improvement Techniques 

• 5S workplace organization
• Mistake-proofing or Poka-Yoke
• Standard work
• Single-Minute Exchange of Die
• Kanban and pull systems
• Total Productive Maintenance
• Selecting appropriate Lean countermeasures

 

Module 23: Solution Selection and Pilot Planning 

• Generating potential solutions
• Assessing solution feasibility
• Impact–Effort Matrix
• Evaluating expected benefits, costs, and risks
• Selecting the preferred solution
• Developing a pilot plan
• Establishing pilot measures and success criteria
• Evaluating pilot results

 

 

Day 8 – Improve, Design of Experiments, and Control Phase

Module 24: Basic Design of Experiments 

• Purpose and benefits of Design of Experiments
• Controlled experimentation versus trial and error
• Factors, levels, and responses
• Experimental runs
• Main effects
• Interaction effects
• Interpreting experimental results
• Process optimization
• Paper Helicopter DOE exercise
• DOE analysis using Minitab

 

Module 25: Statistical Process Control 

• Purpose of Statistical Process Control
• Common-cause and special-cause variation
• Introduction to control charts
• Selecting the appropriate control chart
• Establishing center lines and control limits
• Identifying out-of-control conditions
• Interpreting control-chart patterns
• Control-chart analysis using Minitab

 

Module 26: Sustaining and Standardizing Improvements 

• Developing a process control plan
• Defining process measures and monitoring responsibilities
• Developing a reaction plan
• Standard operating procedures
• Standard work documentation
• Visual management
• Process ownership and accountability
• Preventing process regression

 

Module 27: Benefits Tracking and Project Closure

 

• Validating operational and financial benefits
• Tracking tangible and intangible improvements
• Comparing results against baseline performance
• Preparing a Lean Six Sigma project storyboard
• Documenting lessons learned
• Project handover and closure
• Identifying further continuous improvement opportunities

 

Green Belt Completion Note

Completion of the eight-day course provides the required training foundation for Green Belt-level process improvement work. Formal certification should normally require:

• Successful completion of a knowledge examination; and/or
• Completion and approval of a workplace Lean Six Sigma improvement project

Certification requirements may vary depending on the organization or certifying body.