Six Sigma Black Belt
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Six Sigma Black Belt Six Sigma Black Belt Document Transcript

  • Six Sigma Black Belt 200 hours Course Overview/Description The Black Belt training program integrates online learning with hands-on data analysis. The course material provides an in-depth look at the DMAIC problem-solving methodology, as well as deployment and project development approaches. The course flow follows the DMAIC methodology, with the appropriate tools and concepts taught at each stage of project deployment. Since software will be used for data analysis, the course material concentrates on the application and use of the tools, rather than on detailed derivation of the statistical methods. Workshops are incorporated extensively throughout the training to challenge the student's analytical and problem-solving skills. Upon successful completion of the course, the student will achieve Black Belt Certification issued by their accredited college or university, using industry-acclaimed criteria endorsed by the International Quality Federation ( Upon registering, you are given an initial six months to complete the program. Should you need more time, you may request a 6-month extension at no additional charge. Course Objectives By completing this course, students will be able to: o Participate in the development of a successful Six Sigma program. o Contribute to the definition of project selection criteria and develop project proposals to meet those criteria. o Lead a Six Sigma project team, using the DMAIC problem solving methodology and team building skills. o Apply and interpret basic and advanced Six Sigma tools, as necessary, for project definition, process baseline analysis, process improvement, and process control. Course Outline 1. Why Do Six Sigma a. Definition and graphical view of Six Sigma i. Overview of business applications ii. Example Sigma Levels iii. Introduction to DPMO and cost as metrics. b. Comparisons between typical TQM and Six Sigma Programs. c. Origins and Success Stories. 1
  • 2. How to Deploy Six Sigma a. Leadership responsibilities. b. Description of the roles and responsibilities. c. Resource allocation. d. Data driven decision making. e. Organizational metrics and dashboards. 3. Six Sigma Projects a. Project Focus. b. Selecting Projects. c. Overview of DMAIC methodology. d. Project Reporting. 4. Incorporating Voice of the Customer a. Goal Posts vs. Kano. b. Customer Focus and the Leadership Role. c. Overview of QFD. d. Customer Data. e. Big Y's, Little Y's. 5. DEFINE: Project Definition a. Tasks. b. Work Breakdown Structure. c. Pareto Diagrams. d. Process Maps. e. Matrix Diagrams. f. Project Charters. g. Reporting. 6. DEFINE: Project Financials a. Quality Cost Classifications. b. Quantifying Project Benefits. c. Calculations. 7. DEFINE: Goals & Metrics a. CTC, CTQ, CTS Parameters. b. CTx Flow-down Model (Big Y's, Little y's). c. Measurement & Feedback. d. Calculating Sigma Levels. 8. DEFINE: Project Scheduling a. Activity Network Diagram. b. PERT Analysis. c. GANNT Chart. 2
  • 9. DEFINE: Change Management / Teams a. Problems with Change. b. Achieving Buy-In. c. Team Formation, Rules & Responsibilities. i. Stages of Team Development. ii. Overcoming Problems. d. Consensus Building i. Affinity Diagram. ii. Nominal Group Technique. iii. Prioritization Matrix. 10. MEASURE: Tools a. Measure Stage Objectives b. Flowcharts. c. Process Maps. d. SIPOC. e. Box-Whisker Plots. f. Cause & Effect Diagrams. g. Check Sheets. h. Interrelationship Digraph. i. Stem & Leaf Plots. 11. MEASURE: Establishing Process Baseline a. Enumerative vs. Analytic Statistics. b. Process Variation. i. Deming's Red Bead. c. Benefits of Control Charts. d. Requirements vs. Control. i. Tampering. e. Control Chart Interpretation. i. Relative to Process Baseline Estimates. 12. MEASURE: X-Bar Charts a. Uses. b. Construction & Calculations. c. Assumptions. d. Rational Subgroups. e. Sampling Considerations. f. Interpretation. i. Run Test Rules. 13. MEASURE: Individuals Data a. Uses. b. Construction & Calculations. c. Assumptions. d. Sampling Considerations. 3
  • e. Interpretation. f. Overview of Other Individuals Charts. i. Run Charts. ii. Moving Average Charts. iii. EWMA Charts. 14. MEASURE: Process Capability a. Histograms. b. Probability Plots. c. Goodness of Fit Tests. d. Capability & Performance Indices. i. Relative to Process Control. ii. Interpretation. iii. Estimating Error. 15. MEASURE: Attribute Charts a. Uses. b. Selection. c. Construction & Calculations. d. Sampling Considerations. 16. MEASURE: Short Run SPC a. Uses. b. Calculations. i. Nominals chart. ii. Stabilized Chart. 17. MEASURE: Measurement Systems Analysis a. Stability Studies. b. Linearity Analysis. c. R&R Analysis. i. Range Method Calculations. ii. Interpretation. iii. Using Control Charts. iv. Destructive Tests. v. ANOVA Method. 18. ANALYZE: Lean Thinking a. Definition of Waste. b. Analyzing Process for NVA. i. Cycle Efficiencies ii. Lead Time and Velocity c. Methods to Increase Velocity. i. Standardization ii. Optimization 4
  • iii. Spaghetti Diagrams iv. 5S v. Level Loading. vi. Flow vii. Setup Reductions 19. ANALYZE: Sources of Variation a. Multi-vari Plots b. Confidence Intervals on Mean c. Confidence Intervals on Percent d. Hypothesis Test on Mean e. Hypothesis Test on Mean of Two Samples f. Power & Sample Size. g. Contingency tables. h. Non-parametric Tests. 20. ANALYZE: Regression Analysis a. Scatter Diagrams. b. Linear Model. c. Interpreting the ANOVA Table. d. Confidence & Prediction Limits. e. Residuals Analysis. f. Overview of Multiple Regression Tools i. DOE vs. Traditional Experiments & Data Mining 21. ANALYZE: Multiple Regression a. Multivariate Models. b. Interaction Plots. c. Interpreting ANOVA Tables. d. Model Considerations. e. Stepwise Regression. f. Residuals Analysis. 22. ANALYZE: DOE Introduction a. Terminology b. DOE vs. Traditional Experiments c. DOE vs. Historical Data d. Design Planning. e. Design Specification. i. Selecting Responses. ii. Selecting Factors and Levels. f. Complete Factorials. g. Fractional Factorials. i. Aliasing. ii. Screening Designs. 5
  • 23. ANALYZE: DOE Analysis Fundamentals a. Estimating Effects and Coefficients. b. Significance Plots. c. Estimating Error. d. Extending Designs. e. Power of Design. f. Lack of Fit. g. Tests for Surface Curvature. 24. ANALYZE: Design Selection a. Desirable Designs. b. Performance. i. Balance. ii. Orthogonality. iii. Resolution. c. Other Design Models. i. Saturated Designs. ii. Plackett Burman Designs. iii. Johns 3/4 Designs. iv. Central Composite Designs. v. Box Behnken Designs. vi. Taguchi Designs (mention). 25. ANALYZE: Transforms a. Need for Transformations. b. Non-Constant Variance. c. Box-Cox Transforms. d. Calculated Parameters. e. Taguchi Signal to Noise Ratios. 26. IMPROVE: Tools a. Improve Stage Objectives. b. Tools to Prioritize Improvement Opportunities. c. Tools to Define New Process Flow. i. Lean Tools to reduce NVA and Achieve Flow. d. Tools to Define & Mitigate Failure Modes. i. PDPC. ii. FMECA. iii. Preventing Failures. e. Reference to Tools for Defining New Process Levels. 27. IMPROVE: Response Surface Analysis a. Objectives. b. Applications. 6
  • c. Sequential Technique. d. Steepest Ascent. 28. IMPROVE: Ridge Analysis a. Graphical Method. b. Analytical Method. c. Overlaid Contours. d. Desirability Function. 29. IMPROVE: Simulations a. Applications. b. Examples. c. Applying Probabilistic Estimates. 30. IMPROVE: Evolutionary Operation a. Methodology. b. Example. c. Risks & Advantages. 31. CONTROL: Tools a. Control Stage Objectives. b. Control Plans. c. Training. d. Measuring Improvement. 32. CONTROL: Serial Correlation a. Applications. b. Estimating Autocorrelation. c. Interpreting Autocorrelation. d. Batch Control Charts. 33. Design for Six Sigma Overview a. Methodology. b. Tools for DFSS. c. System, Parameter and Tolerance Designs. Prerequisites/Audience Black Belt candidates generally have college degrees in industry-related fields, including business, engineering, or sciences. They are comfortable using mathematics, are experienced problem solvers, have college-level reading comprehension skills, and are proficient in using Windows-based computer software, including MS Office and general statistical software packages. This training is suitable for anyone with the appropriate pre-requisites with the desire to lead teams using the DMAIC methodology and advanced statistical tools. 7
  • PC Requirements/Materials Included This course is compatible with the Windows Vista operating system. To access this course and Study Guide, users need only a web-enabled computer. To run the Green Belt XL software, users need one of the following Microsoft Excel versions running in MS Windows: Excel 97, Excel 2000, Excel 2002 or Excel XP. Black Belts should also have a general statistical software package, such as Minitab. Adobe Flash Player and Adobe Acrobat Reader are required for this course. Go to to download the Acrobat Reader. Go to to download the Flash Player. In addition to the online access, each course includes the following materials for a complete learning experience: o Six Sigma Handbook by Thomas Pyzdek o Six Sigma Demystified by Paul Keller o Green Belt XL software by Quality America o IQF Black Belt Certification Study Guide software o IQF Black Belt Certification Exam software Instructor Bio Paul A. Keller, Vice President, is a Senior Consultant with Quality America. Paul has developed and implemented successful Six Sigma and quality improvement programs in service and manufacturing applications. Paul is author of McGraw Hill's Six Sigma Demystified, providing a practical methodology for deploying Six Sigma and its DMAIC problem-solving approach. His prior publications include Six Sigma Deployment: A Guide for Implementing Six Sigma in Your Organization, as well as numerous articles and book chapters on Quality Improvement and Six Sigma methods. Paul has developed and led well received training and consulting programs in Six Sigma and related topics to numerous clients in diverse industries, including MacDermid Printing Solutions, Boeing Satellite, Dow Corning, Antec, Pfizer, Los Alamos National Labs, Parker Hannifan Fuel Products, Warner Lambert, University of Arizona, Bell Atlantic, Ford Motor Company and many others. 8
  • 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 later served as a Quality Manager for a consumer goods manufacturer, and an SPC Director at an industrial products manufacturer. 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. 9