Six Sigma ( Black Belt)

COURSE OUTLINES

Why Do Six Sigma

  1. Definition and graphical view of Six Sigma.
  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

DEFINE: Project Definition

  1. Tasks.
  2. Project Charters.
  3. Work Breakdown Structure.
  4. Pareto Diagrams.
  5. Process Maps.
  6. SIPOC.
  7. Reporting.

DEFINE: Metrics & DeliverablesCTC, CTQ, CTS Parameters

  1. CTx Flow-down Model (Big Y's, Little y's)
  2. Measurement & Feedback
  3. Throughput Yield, DPMO, Sigma Level Calculations
  4. Process Cycle Efficiency, Lead Time, Velocity, OEE Calculations.

DEFINE: Project Financials

  1. Quality Cost Classifications.
  2. Quantifying Project Benefits
  3. EBIT, NPV, IRR Calculations.

DEFINE: Project Scheduling

  1. Critical Path Analysis / Activity Network Diagram.
  2. PERT Analysis.
  3. GANNT Chart.

DEFINE: Change Management and Six Sigma Teams

  1. Problems with Change
  2. Achieving Buy-In
  3. Team Formation, Rules & Responsibilities
  4. Consensus Building

MEASURE: Tools

  1. Measure Stage Objectives
  2. Process Definition (Flowcharts, Process Maps)
  3. Metric Definition.
  4. Enumerative vs. Analytic Statistics.
  5. Process Variation (Deming's Red Bead) Benefits of Control Charts.
  6. Requirements vs. Control (Tampering)
  7. Control Chart as Process Baseline Tool.

MEASURE: Distributions

  1. General Probability Rules.
  2. Binomial Distribution: Uses, Assumptions, Excel & Minitab.
  3. Hyper geometric Distribution: Uses, Assumptions, Excel & Minitab.
  4. Poisson distribution: Uses, Assumptions, Excel & Minitab.
  5. Normal Distribution: Uses, Assumptions, Excel & Minitab.
  6. LogNormal Distribution: Uses, Assumptions, Excel & Minitab.
  7. Exponential Distribution: Uses, Assumptions, Excel & Minitab.
  8. Weibull Distribution: Uses, Assumptions, Excel & Minitab.
  9. Probability Plots
  10. Goodness of Fit Tests

MEASURE: X-Bar Charts

  1. Uses / Assumptions
  2. Construction & Calculations.
  3. Rational Subgroups & Sampling Considerations.
  4. Interpretation.
  5. Run Test Rules.

MEASURE: Individuals Data

  1. Uses.
  2. Construction & Calculations.
  3. Assumptions & Sampling Considerations.
  4. Interpretation. Overview of Other Individuals Charts: Run Charts; Moving Average Charts; EWMA Charts.
  5. MEASURE: Process Capability
  6. Histograms.
  7. Capability & Performance Indices (Interpretation; Estimating Error)

MEASURE: Attribute Charts

  1. Uses.
  2. Selection.
  3. Construction & Calculations.
  4. Sampling Considerations.
  5. MEASURE: Short Run SPC
  6. Uses.
  7. Calculations.
  8. Nominals chart.
  9. Stabilized Chart.

MEASURE: Measurement Systems Analysis

  1. Stability Studies.
  2. Linearity Analysis.
  3. R&R Analysis.
  4. Range Method Calculations.
  5. Interpretation.
  6. Using Control Charts.
  7. Destructive Tests.
  8. ANOVA Method.

ANALYZE: Value Stream Analysis

  1. Definition of Waste.
  2. Analyzing Process for NVA using VSA.
  3. Analyzing Lead Time and Velocity

ANALYZE: Sources of Variation

  1. Multi-vari Plots.
  2. Confidence Intervals on Means & Percents.
  3. Hypothesis Testing Method, Assumptions and Uses.
  4. Hypothesis Tests on Mean, Two Sample Means, Paired Samples.
  5. Hypothesis Tests on Variance, Two Sample Variances.
  6. Contingency tables.
  7. Power & Sample Size Considerations.
  8. Non-parametric Tests.

ANALYZE: ANOVA

  1. Assumptions & Bartlett’s Equality of Variance Test.
  2. One-way ANOVA.
  3. Two-way ANOVA.
  4. Multi-factor ANOVA.
  5. Tukey’s HSD Test.

ANALYZE: Regression Analysis

  1. Cause & Effect Diagrams.
  2. Scatter Diagrams.
  3. Correlation, Stratification.
  4. Linear Model. Interpreting the ANOVA Table.
  5. Confidence & Prediction Limits.
  6. Residuals Analysis.
  7. Overview of Multiple Regression Tools
  8. DOE vs. Traditional Experiments & Data Mining

ANALYZE: Multiple Regression

  1. Multivariate Models.
  2. Interaction Plots.
  3. Interpreting ANOVA Tables.
  4. Model Considerations.
  5. Stepwise Regression.
  6. Residuals Analysis.

ANALYZE: DOE Introduction

  1. Terminology
  2. DOE vs. Traditional Experiments
  3. DOE vs. Historical Data
  4. Design Planning.
  5. Selecting Responses.
  6. Selecting Factors and Levels.
  7. Complete Factorials.
  8. Fractional Factorials.
  9. Aliasing.
  10. Screening Designs.

ANALYZE: DOE Analysis Fundamentals

  1. Estimating Effects and Coefficients. Significance Plots.
  2. Estimating Error & Lack of Fit.
  3. Extending Designs.
  4. Power of Design.
  5. Tests for Surface Curvature.

ANALYZE: Design Selection

  1. Desirable Designs.
  2. Performance: Balance, Orthogonality, Resolution.
  3. Other Design Models.
  4. Saturated Designs.
  5. Placket Burman Designs.
  6. Johns 3/4 Designs.
  7. Central Composite Designs.
  8. Box Behnken Designs.

ANALYZE: Transforms

  1. Need for Transformations.
  2. Non-Constant Variance.
  3. Box-Cox Transforms.
  4. Calculated Parameters.
  5. Taguchi Signal to Noise Ratios.

IMPROVE: Tools

  1. Improve Stage Objectives.
  2. Tools to Prioritize Improvement Opportunities.
  3. Defining New Process Flow.
  4. Lean Tools to reduce NVA and Achieve Flow, including 5S.
  5. Defining New Process Levels.
  6. Improving Lead Times and Setup Times.
  7. Tools to Define & Mitigate Failure Modes.

IMPROVE: Response Surface Analysis

  1. Objectives.
  2. Applications.
  3. Sequential Technique.
  4. Steepest Ascent.

IMPROVE: Ridge Analysis

  1. Graphical Method.
  2. Overlaid Contours.
  3. Desirability Function.

IMPROVE: Simulations

  1. Applications.
  2. Examples.
  3. Applying Probabilistic Estimates.

IMPROVE: Evolutionary Operation

  1. Methodology.
  2. Example.
  3. Risks & Advantages.
  4. CONTROL: Tools
  5. Control Stage Objectives.
  6. Control Plans. Training.
  7. Measuring Improvement.

CONTROL: Serial Correlation

  1. Applications.
  2. Estimating Autocorrelation.
  3. Interpreting Autocorrelation.
  4. Batch Control Charts.

Design for Six Sigma Overview

  1. Methodology.
  2. Tools for DFSS.
  3. System, Parameter and Tolerance Designs.