
Training materials: Texts: Knowledge Based Management; Lean Six Sigma: A Tools Guide; Basic Statistics: Tools for Continuous Improvement; Understanding Industrial Designed Experiments; Software: SPC XL, DOE PRO; Design for Six Sigma Foundations student participant guide.
Course agenda:
Introduction to Design for Six Sigma (DFSS)
• The What and Why of Design for Six Sigma • Design for Six Sigma Master Strategy: IDOV • DFSS Studies and Projects • Key Players and Roles • Course Philosophy and Instructional Approach
Six Sigma Fundamentals
• Basics of Six Sigma and its Key Principles • Defining Processes Using IPO Diagrams • Key Terminology (Distribution, Mean, Median, Standard Deviation, Cp, Cpk, sigma level, first pass yield, defects) • PF/CE/CNX/SOP (the first line of defense against variation)
Measurement System Analysis
• Properties of a Good Measurement System • Impact of Measurement System Variation • How to Set Up, Conduct, and Perform a Measurement System Analysis - Variables Data - Attribute Data • Interpretation of MSA Results and Metrics - Repeatability - Reproducibility - P/Tol ratio - Discrimination (resolution) - Effectiveness, Probability of False Rejects, Probability of False Accepts
Understanding Data Distributions and Their Applications
• Basic Concepts of Probability • Fact that Probability is Often Not Intuitive • Four Common Distributions and Their Application to Problem Solving - Binomial distribution - Poisson distribution - Exponential distribution - Normal distribution • Using SPC XL for Calculating Probabilities
Introduction to Regression Analysis and Design of Experiments (DOE)
• What is Regression and What is it Used For? • Terminology Involved in Simple Linear Regression - Intercept - Slope - Prediction Equation - Residual - R-Squared • Use of SPC XL for Regression Analysis and Interpretation of Output • Introduction to Design of Experiments (DOE)
Foundations of Design of Experiments (DOE)
• Purpose of Design of Experiments • Experimentation Strategies • Key DOE Terminology • Introduction to Basic Graphical and Statistical Analysis of Data • Interactions • Introduction to DOE Pro Software and Hands-On Experimentation Using the Statapult® Catapult
Design and Analysis of Experiments
• Importance of Planning • DOE 12 Step Process • Review and Practice: Graphical and Statistical Analysis of Data • Building Design Matrices • Introduction to Fractional Factorial Designs • DOE Examples • Hands-on Practice with Modeling and Optimization using the Statapult® • Reasons why Experiments May Fail to Confirm and How to Recover
Rules of Thumb for DOE
• Sample Size Guidelines for DOE • Selecting the Best Design • Determining Statistical Significance • Interpreting R-square, Adjusted R-square, Tolerance and p-Values
Two Level Design Summary
• Use and Application of Two Level Designs • Summary of Two Level Design Options - Full Factorial Designs - Fractional Factorial Designs - Screening Designs • Awareness of Situations where Standard Design will Not Apply and KISS Approaches for Dealing with these Situations - Nested Designs - Mixture Designs
Three Level Designs
• Qualitative vs. Quantitative Factors in DOE • Use and Application of Three Level Designs • Full Factorial Designs • Screening Designs • Box Behnken and Central Composite Designs • Setting Up, Conducting, Analyzing, and Confirming a Quadratic Model Using the Statapult® Catapult
Variance Reduction Methods and Robust Design
• Strategies for Variance Reduction • Robust Design and DOE • Setting up and analyzing Robust Design experiments • Reducing Transmitted Variation by Taking Advantage of Interactions and Non- Linearities
References, Glossary of Terms and Course Evaluation Forms
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