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Six Sigma Black Belt (GES 215)

Overview

R.E.P. Logo

The Six Sigma Black Belt Online Training Program integrates Six Sigma with hands-on data analysis. The program material provides you with an in-depth look at the Six Sigma Black Belt problem-solving methodology, DMAIC: Define the problem, Measure key aspects, Analyze data, Improve the process, Control the future process. You'll also learn about deployment and project development approaches.

All materials are included. This online certificate program is offered in partnership with major colleges, universities, and other accredited education providers.

"The Web class was set up very well; very user-friendly. The course was set up to where each topic built upon the previous."
—N.H., Bainbridge College, Fall 2006

PMBOK® Guide, PMI®, PMP®, CAPM®, and the PMI R.E.P. logo are either marks or registered marks of the Project Management Institute, Inc.

The Black Belt Training Program flow follows the DMAIC methodology. You'll learn the appropriate tools and concepts at each stage of project deployment. Since software will be used for data analysis, the program material concentrates on the application and use of the tools, rather than on detailed derivation of the statistical methods.

Your analytical and problem-solving skills will be challenged by workshops, which are incorporated extensively throughout the training. Once you've successfully completed the program, you'll be eligible to work toward a Black Belt Certification through the International Quality Federation (http://www.iqfnet.org). Achieving this certification is at your discretion and dependent upon you meeting the criteria the International Quality Federation sets; certification is not automatic upon completion of this program or tied to the accredited college or university you registered through.

Upon registering, you're given an initial six months to complete the program. Should you need more time, you may request a six-month extension at no additional charge.

By completing this program, you’ll be able to:

Participate in the development of a successful Six Sigma program
Contribute to the definition of project selection criteria and develop project proposals to meet those criteria
Lead a Six Sigma project team using the DMAIC problem-solving methodology and team-building skills
Apply and interpret basic and advanced Six Sigma tools, as necessary, for project definition, process baseline analysis, process improvement, and process control

Why Do Six Sigma
1. Definition and Graphical View of Six Sigma
  1. Overview of Business Applications
  2. Example Sigma Levels
  3. Introduction to DPMO and Cost as Metrics
2. Comparisons Between Typical TQM and Six Sigma Programs
3. Origins and Success Stories
How to Deploy Six Sigma
1. Leadership Responsibilities
2. Description of the Roles and Responsibilities
3. Resource Allocation
4. Data-driven Decision Making
5. Organizational Metrics and Dashboards
Six Sigma Projects
1. Project Focus
2. Selecting Projects
3. Overview of DMAIC Methodology
4. Project Reporting
Incorporating Voice of the Customer
1. Goal Posts vs. Kano
2. Customer Focus and the Leadership Role
3. Overview of QFD
4. Customer Data
5. Big Y's, Little Y's
DEFINE: Project Definition
1. Tasks
2. Work Breakdown Structure
3. Pareto Diagrams
4. Process Maps
5. Matrix Diagrams
6. Project Charters
7. Reporting
DEFINE: Project Financials
1. Quality Cost Classifications
2. Quantifying Project Benefits
3. Calculations
DEFINE: Goals and Metrics
1. CTC, CTQ, CTS Parameters
2. CTx Flow-down Model (Big Y's, Little Y's)
3. Measurement & Feedback
4. Calculating Sigma Levels
DEFINE: Project Scheduling
1. Activity Network Diagram
2. PERT Analysis
3. GANNT Chart
DEFINE: Change Management/Teams
1. Problems With Change
2. Achieving Buy-in
3. Team Formation, Rules, and Responsibilities
  1. Stages of Team Development
  2. Overcoming Problems
4. Consensus Building
  1. Affinity Diagram
  2. Nominal Group Technique
  3. Prioritization Matrix
MEASURE: Tools
1. Measure Stage Objectives
2. Flowcharts
3. Process Maps
4. SIPOC
5. Box-Whisker Plots
6. Cause and Effect Diagrams
7. Check Sheets
8. Interrelationship Digraph
9. Stem and Leaf Plots
MEASURE: Establishing Process Baseline
1. Enumerative vs. Analytic Statistics
2. Process Variation
  1. Deming's Red Bead
3. Benefits of Control Charts
4. Requirements vs. Control
  1. Tampering
5. Control Chart Interpretation
  1. Relative to Process Baseline Estimates
MEASURE: X-Bar Charts
1. Uses
2. Construction and Calculations
3. Assumptions
4. Rational Subgroups
5. Sampling Considerations
6. Interpretation
  1. Run Test Rules
MEASURE: Individuals Data
1. Uses
2. Construction and Calculations
3. Assumptions
4. Sampling Considerations
5. Interpretation
6. Overview of Other Individuals Charts
  1. Run Charts
  2. Moving Average Charts
  3. EWMA Charts
MEASURE: Process Capability
1. Histograms
2. Probability Plots
3. Goodness of Fit Tests
4. Capability and Performance Indices
  1. Relative to Process Control
  2. Interpretation
  3. Estimating Error
MEASURE: Attribute Charts
1. Uses
2. Selection
3. Construction and Calculations
4. Sampling Considerations
MEASURE: Short Run SPC
1. Uses
2. Calculations
  1. Nominals Chart
  2. Stabilized Chart
MEASURE: Measurement Systems Analysis
1. Stability Studies
2. Linearity Analysis
3. R&R Analysis
  1. Range Method Calculations
  2. Interpretation
  3. Using Control Charts
  4. Destructive Tests
  5. ANOVA Method
ANALYZE: Lean Thinking
1. Definition of Waste
2. Analyzing Process for NVA
  1. Cycle Efficiencies
  2. Lead Time and Velocity
  3. Methods to Increase Velocity
    1. Standardization
    2. Optimization
    3. Spaghetti Diagrams
    4. 5S
    5. Level Loading
    6. Flow
    7. Setup Reductions
3. ANALYZE: Sources of Variation
  1. Multi-vari Plots
  2. Confidence Intervals on Mean
  3. Confidence Intervals on Percent
  4. Hypothesis Test on Mean
  5. Hypothesis Test on Mean of Two Samples
  6. Power and Sample Size
  7. Contingency Tables
  8. Non-parametric Tests
4. ANALYZE: Regression Analysis
  1. Scatter Diagrams
  2. Linear Model
  3. Interpreting the ANOVA Table
  4. Confidence and Prediction Limits
  5. Residuals Analysis
  6. Overview of Multiple Regression Tools
    1. DOE vs. Traditional Experiments and Data Mining
5. ANALYZE: Multiple Regression
  1. Multivariate Models
  2. Interaction Plots
  3. Interpreting ANOVA Tables
  4. Model Considerations
  5. Stepwise Regression
  6. Residuals Analysis
6. ANALYZE: DOE Introduction
  1. Terminology
  2. DOE vs. Traditional Experiments
  3. DOE vs. Historical Data
  4. Design Planning
  5. Design Specification
    1. Selecting Responses
    2. Selecting Factors and Levels
  6. Complete Factorials
  7. Fractional Factorials
    1. Aliasing
    2. Screening Designs
7. ANALYZE: DOE Analysis Fundamentals
  1. Estimating Effects and Coefficients
  2. Significance Plots
  3. Estimating Error
  4. Extending Designs
  5. Power of Design
  6. Lack of Fit
  7. Tests for Surface Curvature
8. ANALYZE: Design Selection
  1. Desirable Designs
  2. Performance
    1. Balance
    2. Orthogonality
    3. Resolution
  3. Other Design Models
    1. Saturated Designs
    2. Plackett Burman Designs
    3. Johns 3/4 Designs
    4. Central Composite Designs
    5. Box Behnken Designs
    6. Taguchi Designs (Mention)
9. ANALYZE: Transforms
  1. Need for Transformations
  2. Non-constant Variance
  3. Box-Cox Transforms
  4. Calculated Parameters
  5. Taguchi Signal to Noise Ratios
10. IMPROVE: Tools
  1. Improve Stage Objectives
  2. Tools to Prioritize Improvement Opportunities
  3. Tools to Define New Process Flow
    1. Lean Tools to Reduce NVA and Achieve Flow
  4. Tools to Define and Mitigate Failure Modes
    1. PDPC
    2. FMECA
    3. Preventing Failures
  5. Reference to Tools for Defining New Process Levels
11. IMPROVE: Response Surface Analysis
  1. Objectives
  2. Applications
  3. Sequential Technique
  4. Steepest Ascent
12. IMPROVE: Ridge Analysis
  1. Graphical Method
  2. Analytical Method
  3. Overlaid Contours
  4. Desirability Function
13. IMPROVE: Simulations
  1. Applications
  2. Examples
  3. Applying Probabilistic Estimates
14. IMPROVE: Evolutionary Operation
  1. Methodology
  2. Example
  3. Risks and Advantages
15. CONTROL: Tools
  1. Control Stage Objectives
  2. Control Plans
  3. Training
  4. Measuring Improvement
16. CONTROL: Serial Correlation
  1. Applications
  2. Estimating Autocorrelation
  3. Interpreting Autocorrelation
  4. Batch Control Charts
17. Design for Six Sigma Overview
  1. Methodology
  2. Tools for DFSS
  3. System, Parameter, and Tolerance Designs

Black Belt candidates generally have college degrees in industry-related fields, including business, engineering, or sciences. They're comfortable using mathematics, are experienced problem solvers, have college-level reading comprehension skills, and are proficient in using Windows-based computer software, including Microsoft Office and general statistical software packages. You should also have Excel 2000 or any more recent version of Excel in order to complete the program.

This program is for you if you have the appropriate prerequisites and the desire to lead teams using the DMAIC methodology and advanced statistical tools.

This program is compatible with the Windows XP and later operating systems and IE 7 and later browsers.

To access this program and Study Guide, you need only a Web-enabled computer. To run the Green Belt XL software, you’ll need to have Excel 2003, 2007, or 2010 . Black Belts should also have a general statistical software package, such as Minitab. Optionally, they may purchase an upgrade to the Green Belt XL software which includes a untility to construct designed experiments. (The utility alone does not run in 64 bit environments.)

Note: The Green Belt XL, IQF Study Guide software and the certification exam are not compatible with Mac computers.

Adobe Flash Player and Adobe Acrobat Reader are required for this program.

Click here to download the Acrobat Reader.
Click here to download the Flash Player.

This program includes the following materials for a complete learning experience:

Six Sigma Demystified by Paul Keller
Six Sigma Handbook by Thomas Pyzdek and Paul Keller
Black Belt Notebook
Green Belt XL software including MindGenius
IQF Study Guide
IQF Certification Exam

Paul Keller, Vice President and COO, is a senior consultant with Quality America, Inc. Paul has developed and implemented successful Six Sigma and quality improvement programs in service and manufacturing environments. Paul has been with Quality America since 1992, where he has:

Developed and managed systems for overall operations, including quality improvement, product development, partner relations, marketing, sales, order fulfillment, and technical support.
Provided primary statistical expertise to the customer base, as well as internal software development, sales, and technical support teams.
Developed and implemented Six Sigma-related courses, including quality management, statistical process control (SPC), and designed experiments, to hundreds of companies in a wide variety of industries, including Roche Pharmaceuticals, Core3 Inc. Business Process Outsourcing, U.S. Army, MacDermid Printing Solutions, Boeing Satellite, Dow Corning, Antec, Pfizer, Warner Lambert, and many others.

Paul has authored a number of well-received publications, including:

2009: co-authored third edition of Six Sigma Handbook, New York: McGraw Hill
2005: author, Six Sigma Demystified, New York: McGraw Hill (over 35,000 copies sold to date)
2003: technical editor, Quality Engineering Handbook, Tucson: QA Publishing, LLC
2002: contributing author, Manufacturing Handbook of Best Practices, Boca Raton: St. Lucie Press
2001: author, Six Sigma Deployment, Tucson: QA Publishing, LLC
2001: co-author, Six Sigma Study Guide, Tucson: QA Publishing, LLC
2000: contributing author, Handbook for Quality Technicians and Mechanical Inspectors, Tucson: QA Publishing, LLC
2000: contributing author, Handbook for Quality Management, Tucson: QA Publishing, LLC
1992-present: numerous articles in professional trade magazines, conference proceedings, and Quality America's Knowledge Center

Before launching Quality America's training and consulting business in 1992, Paul specialized in quality engineering in the Masters program at the University of Arizona. He applied these techniques as a quality manager for a consumer goods manufacturer (1990-1992) and as an SPC director at an industrial products manufacturer (1987-1990). In these roles, he developed company-wide quality systems to meet the demands of a diverse customer base, including the automotive and aerospace industries.

Paul is currently active in Six Sigma training and consulting through Quality America and ed2go.