• Improvement Leaders
• Quality Managers
• Quality Engineers
• Operations Managers
• Process Analysts
• Project Managers
• Quality Consultants
• Operational Excellence Leaders

What is Six Sigma?

• Data-driven processes and decisions
• Defining and calculating sigma levels
• Customer-focused continuous process improvement
• Common Six Sigma implementation challenges

Six Sigma History and Application

• Statistical process control development
• Toyota Lean system evolution
• Motorola and General Electric applications
• Certification levels and knowledge application

Other Process Improvement and Quality Methods

• Lean process management approaches
• Total quality management applications
• Business process reengineering methods
• Selecting suitable improvement methodologies

Lean Concepts

• Seven forms of process waste
• Applying the 5S methodology
• Just-in-time manufacturing system principles
• Distinguishing major waste types

Basic Six Sigma Concepts

• Standard deviation and Pareto principle
• Voice of customer methods
• Core process defect metrics
• First and rolled throughput yield

Approaching the Problem

• Process output factor relationships
• Applying the Five Whys
• Building effective problem statements
• Converting problems into objectives

What is a Process?

• Four process definition layers
• Inputs outputs events and tasks
• Process owner responsibilities and data
• Building SIPOC process diagrams

Quality

• Identifying critical quality characteristics
• Converting customer needs into metrics
• Calculating cost of poor quality
• Managing total quality costs

Selecting the Right Projects

• Balancing improvement project portfolios
• Enterprise-level project selection methods
• Evaluating project viability models
• Process-level project selection methods

Basic Six Sigma Team Management

• Building effective Six Sigma teams
• Defining team member roles
• Planning timelines and milestones
• Managing budgets and success measures

Introduction to DMAIC and DMADV

• Comparing DMAIC and DMADV methodologies
• Define measure analyze phases
• Improve and control phases
• Design and verify phases

Define

• Creating comprehensive project charters
• Establishing project ground rules
• Conducting structured stakeholder analysis
• Applying Define tollgate checklists

Measure

• Conducting failure modes analysis
• Collecting and classifying data
• Selecting appropriate measurement systems
• Delivering process baseline metrics

Analyze

• Conducting structured root cause analysis
• Using cause and effect diagrams
• Verifying identified root causes
• Applying graphical and statistical analysis

Improve

• Selecting solutions using matrices
• Conducting solution cost-benefit analysis
• Piloting selected improvement solutions
• Planning implementation training and transition

Control

• Revising failure mode analysis
• Creating effective control plans
• Applying visual management methods
• Using statistical process control charts

Intermediate Graphical Analysis

• Using additional graphical analysis tools
• Building bar and pie charts
• Analyzing scatter diagrams visually
• Creating X-bar control charts

Normal Probability Distributions

• Understanding basic probability distributions
• Creating histograms for analysis
• Testing data for normality
• Calculating normal probabilities practically

Correlation and Regression

• Calculating statistical correlation coefficients
• Interpreting relationship strength and direction
• Applying linear regression analysis
• Using regression in improvement projects

Non-Normal Probability Distributions

• Distinguishing non-normal continuous distributions
• Analyzing lognormal and Weibull distributions
• Using appropriate discrete distributions
• Applying distributions to real situations

Hypothesis Testing

• Understanding hypothesis testing fundamentals
• Selecting appropriate hypothesis tests
• Testing continuous and discrete data
• Interpreting statistical test results

Sample Size

• Reviewing hypothesis testing errors
• Identifying sample calculation information
• Selecting sample size methods
• Calculating samples for different tests

Advanced Control Charts

• Selecting appropriate control chart types
• Creating control charts in Minitab
• Interpreting different variation causes
• Analyzing variable and attribute charts

Applying Statistics to Business Applications through Six Sigma

• Presenting statistical analysis to management
• Simplifying results for target audiences
• Building data-supported business stories
• Avoiding misuse of analytical tools

Introduction to Minitab

• Understanding the Minitab interface
• Using primary Minitab menus
• Performing basic statistical calculations
• Working with probability distributions

Graphs and Quality Tools in Minitab

• Creating scatter plots and histograms
• Building boxplots and interval plots
• Creating Pareto and run charts
• Conducting measurement system studies

The Stat Menu in Minitab

• Generating basic descriptive statistics
• Performing statistical regression analysis
• Running appropriate statistical tests
• Applying hypothesis testing steps

Analysis of Variance (1-Way ANOVA)

• Preparing data for ANOVA
• Running one-way ANOVA tests
• Validating statistical test assumptions
• Handling unequal variance conditions

Design of Experiments

• Identifying need for designed experiments
• Comparing factorial and trial approaches
• Designing structured factorial experiments
• Analyzing factorial experiments in Minitab

Interactions, Multi-Level Factorials, and Creating Experiments

• Understanding interactions among experiment factors
• Comparing two-level and multilevel designs
• Planning successful designed experiments
• Documenting responses and confidence levels

Brainstorming and Process Improvement Tools

• Using activity network diagrams
• Applying affinity and relationship diagrams
• Analyzing driving and restraining forces
• Using diverse brainstorming techniques

Process Maps

• Defining process mapping objectives
• Using standard process map symbols
• Creating basic process flowcharts
• Designing functional swimlane diagrams

Value Stream Mapping

• Understanding value stream mapping
• Using value stream symbols
• Creating current-state value maps
• Developing future-state value maps