Design of Experiments (DOE), also known as Statistical Experimental Planning, is a key Six Sigma tool. It is a methodology that can be effective for general problem-solving, as well as for improving or optimizing product design and manufacturing processes. Specific applications of DOE include identifying proper design dimensions and tolerances, achieving robust designs, generating predictive math models that describe physical system behavior, and determining ideal manufacturing settings.
Quality Managers, Quality Engineers, Manufacturing Engineers, Production Engineers, Project Engineers and Design Engineers.
This course will enable participants to be able to:
- Decide whether to run a DOE to solve a problem or optimize a system
- Set-Up a Full Factorial DOE Test Matrix, in both Randomized and Blocked forms
- Analyze and Interpret Full Factorial DOE Results using ANOVA, (when relevant) Regression, and Graphical methods
- Set-Up a Fractional (Partial) Factorial DOE, using the Confounding Principle
- Analyze and Interpret the results of a Fractional Factorial DOE
- Recognize the main principles and benefits of Robust Design DOE
- Decide when a Response Surface DOE should be run
- Select the appropriate Response Surface Design (either Plackett-Burman, Box-Behnken, Central Composite, or D-Optimal)
- Interpret Response Surface Outputs
- Utilize the Minitab / JMP Software tool to analyze data
- What is DOE?
- DOE Requirements: Before You Can Run an Experiment
- Full Factorial Experiments
- DOE Statistical Analysis
- Fractional (Partial) Factorial Experiments
- Robust Design Experiments
- Response Surface Modeling