Six Sigma Green Belt

Training Materials

All Six Sigma training students receive comprehensive courseware.

Software Requirements

Minitab (attendees can download the free 30-day trial).

Outline

• Lean Six Sigma Overview
  • A brief history of both Lean and Six Sigma process improvement methodologies
  • The process improvement structure
  • The DMAIC roadmap
  • Inefficiency and ineffectiveness of processes that are linked to the Lean Six Sigma methodology
• Defining Phase and Project Selection
  • Definition, scoping, and selection
  • Defining projects
  • Defining the problem
    • The Process associated with the Problem
    • Metrics for measuring success
    • The business value of the project
• Six Sigma Organizational Deployment
  • Defining a support structure with various roles and responsibilities
    • The attributes of the various roles
    • Expectations of each role
    • Success factors for effectively completing meaningful projects
• Process Variables Mapping
  • Establishing the existing process flow and the steps involved in the process
  • The contributing factors to the process performance
  • Determining the root cause of the variability in the baseline process
  • Not a standard “flowchart”
• Cause and Effects Matrix 
  • Establishing the factors to address based on their relationships to the process performance measures
• Failure Mode and Effects Analysis (FMEA)
  • An in-depth view of the top-ranked factors to determine if they are susceptible to failure
  • Establishing high-risk environments
  • Mitigating the risk of failure
• Types of Waste
  • Various types of waste
  • How to identify wastes
  • Methods that can be taken to reduce waste
• Basic Statistics
  • Using statistical approaches to solve the underlying performance problems
  • Methods for describing data statistically
    • Shape, center, and spread of the data distribution
  • Normal distribution properties
• Introduction to Statistical Software functions
  • Basic functions, structure, and capabilities
• Basic Quality Tools
  • The graphical and analytical techniques used to conduct basic analysis
  • Analyzing data sets and searching for root causes of variability
    • Dotplot
    • Histogram
    • Normal probability plot
    • Time Series plot
    • Pareto diagrams
    • Fishbone diagram
    • Boxplots
    • Scatter plots
    • Descriptive statistics
• Introduction to Statistical Process Control
  • Identifying key events contributing to the variability of the process to set the direction for process improvement
    • General types of variability
  • Normal distribution to time series data
  • Interpreting a standard control chart
• Measurement Systems Evaluation
  • Using data to determine the root causes of process performance variability.
  • Constructing and completing a Measurement Systems Analysis (MSA) for various measurement situations
• Introduction to Capability Studies
  • Natural variation
  • Determining how the process performs in comparison to the customer expectations (specifications)
  • Short- and long-term variability
• Multi-Vari Studies
  • The root cause of the variability
  • Key elements for designing and conducting a successful passive data collection study
  • Graphical analysis techniques for prioritizing which factors to investigate with advanced analytical methods
• Hypothesis Testing
  • Designing and properly analyzing the various types of hypothesis tests is essential for making well informed decisions.
  • Risk levels for decision errors
  • Determining the proper test to utilize and threats to the practical significance of the evaluation may exist
• Comparing Groups: One Way ANOVA
  • Use the roadmap for conducting a test and for evaluating the various underlying assumptions affecting the method including the Test for Equal Variances
  • Correlation and Regression Analysis
  • Establishing the relationship between two continuous type variables can be accomplished through use of correlation.
  • Evaluating the causal relationship using basic Linear Regression techniques and their use in developing a prediction equation
• Chi-Square Test: Proportion Comparisons
  • Testing the proportions of various groups to establish if there is an unusual relationship that exists
• Multi-Vari Case Study
  • Applying the various Multi-Vari Study data analysis techniques
  • The decision process based on information provided using a simulation tool
• Introduction to Designed Experiments (DOE)
  • The applicability, concepts, methods for designing and analyzing simple designed experiments
  • Insights into tools expanded upon in the Black Belt level training course.
• Mistake Proofing
  • Methods to prevent factors from varying uncontrollably through the use of Mistake Proofing methods
• Process Control Plans
  • Transfer understanding to the process owners and to establish ‘the formula’ for future predictable process performance.
• Project Documentation
  • Providing a history of the methods, assumptions, analysis, and decisions made throughout the process
  • Establish the final impact and value of the project on the business.
• Conclusion