Modern DOE for Process Optimization (MDOE), $1695

In this 3-day workshop participants learn to set up, analyze, and interpret DOE's ranging from fractional factorials to response surface designs. First discover the vital few effects along with unknown interactions, and then transition to RSM to optimize your process. Learn how to use the latest small-run DOE techniques to solve your real-world problems!


Attend the first 2 days at a reduced rate. Please inquire for this option.


Price includes a $95 fee for workshop materials which is subject to state and local taxes.


A 10% Early Bird discount will be applied to registrations made 6 weeks prior to the workshop date.

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Price: $1,535.00

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Modern DOE for Process Optimization (MDOE) (3 days)

 

Use State-of-the-Art DOE Tools to Make Breakthrough Improvements and Optimize the Final Results

This updated revision of our factorial and response surface methods workshops combines the topics and brings new insights into one fast-paced course! Find out how to make breakthrough improvements using powerful design of experiments (DOE) techniques. Start by learning about using factorial designs for finding which factors you need to focus on. Discover previously unknown interactions that often prove to be the key to success. Learn how to best use modern small-run designs to save time and money experimenting. Transition from factorials to response surface methods to optimize your products and processes. Tie all of this together by learning how to use powerful ANOVA analysis methods that give you confidence in your findings. 

Apply Tried and True Techniques

This workshop covers the practical aspects of DOE. (Students may purchase the optional "DOE Simplified" book for reference.) You learn all about simple but powerful two-level factorial designs. During this DOE workshop, you will discover how to effectively:

  • Understand the motivation for factorial designs
  • Implement the DOE planning process
  • Interpret analysis of variance (ANOVA)
  • Discover hidden interactions
  • Capitalize on efficient small-run fractional designs for screening or characterization
  • Use power to properly size designs
  • Determine when to use transformations
  • Explore multilevel categoric factors
  • Set up modern split-plot designs - both factorial and RSM
  • Follow the strategy of experimentation from screening to response surface methods
  • Set up central composite (CCD), Box-Behnken and Optimal RSM designs
  • Select appropriate regression models
  • Optimize multiple responses numerically
  • Add multilinear constraints and categoric factors to optimal designs


Choose the Best Strategy

The "Modern DOE for Process Optimization" workshop helps you plan your DOE by selecting the appropriate designs. It guides you through your experiment and strategic analysis.

"Practical. Good mix of theory and application." 
—Chris Easter, Metallurgist

Simulations Provide Practice

Use Design-Expert® software to practice designing and analyzing experiments throughout the workshop. The software provides easy-to-use graphical tools to find key variables and view results.  You will be given a path to all simulation and data files used in class, which are posted to a special Internet site where you can also link to a free fully-functional, but time-limited, copy of Design-Expert software for use after class.
 
"Gives you the 'hands-on' that puts it all together."
—Matt Hanken, Senior Manufacturing Engineer

Course Outline

Day 1 Section 1—Introduction to Factorial Design
 
  • Background and motivation for factorial designs
  • Factorial design planning process
  • Basics of factorial design: Case study
    • Selecting effects—Half-normal plot and Pareto chart
    • ANOVA and residual diagnostics
    • Main effects, interaction, contour and 3D surface plots
    • Introduction to multiple-response optimization
  Section 2—Enhancements for Design and Analysis of Factorials
 
  • Replicated 23full factorial: Case study
  • Explanation of power
  • 24 full factorial: Exercise
  • Transformations: Case study
    • Dangers of deleting outliers
    • Details of using transformations
    Section 3—Blocking and Fractionating Factorials
 
  • How to set up optimal blocking: Case study
    • Factors interacting vs three-factor interactions (3FIs)
  • How to set up fractional factorials
  • Understanding aliases
  • 25-1 fractional factorial: Exercise
Day 2 Section 4—Modern Small-Run Designs
 
  • Minimum-run characterization (MR5) design: Case study
  • Minimum-run screening (MR4) design: Case study
  • Definitive screening (DSD) design
  • Guide to using small-run designs
  Section 5—Multilevel Categoric Design (General Factorial)
 
  • Multilevel categoric design with replication: Case study
  • Fractionating via optimal (custom) design: Case study
    • Introduction to optimal (custom) design
    • Model graphs for multilevel categoric designs
  Section 6—Split-Plot Designs
 
  • Restriction randomization
  • Split-plot design: Case study
  Section 7Factorial with Center Points and RSM Introduction
 
  • Factorial with center points: Case study
  • Introducing response surface methods (RSM)
  • Augmenting to central composite design (CCD): Case study
 Day 3 Section 8Response Surface Methods - Central Composite Designs
 
  • "Good" response surface designs
  • Response surface methods: Case study
    • Key differences in RSM analysis
  • Customized CCD's
  • Model reduction exercise
  Section 9—Optimization and Confirmation
 
  • Optimization exercise
    • Numerical
    • Graphical
  • Exercise with confirmation runs
  Section 10—Response Surface Designs
 
  • Box-Behnken design: Case study
  • Face-centered CCD (FCD)
    • Design Evaluation
    • Exercise
  • Modern composite design based on small-run core
  Section 11Optimal Design
 
  • Multiple linear constraints (MLCs): Case study
    • Optimal designs
    • Sizing for precision via fraction of design space
  • Categoric factors: Case study
  • RSM design summary
  Section 12Split-Plot Design for RSM
 
  • Review split-plot concepts
  • Central composite split plot
  • Optimal split plot
       Summary
  • Optimize your process exercise
  • Mixture design overview
  • Next steps
  Section 13—Optional RSM Tools (as time allows)
 
  • Sizing designs to detect a difference
  • User-defined candidate set
  • Design augmentation
  • MLCs revisited - more complex example

Prerequisites

Math skills, knowledge of basic statistics, and exposure to simple comparative experiments (e.g. two-sample t-test) are recommended. If you aren't ready for the Modern DOE for Process Optimization workshop, take the online PreDOE course first (a $95 value you get for free! It takes 2-3 hours to complete. You can work at your own pace). Access the PreDOE here.

Before attending class, please download a trial of DX10 (if you do not already have access to it) and work through the General Multilevel-Categoric One-Factor tutorial.

Additional Information

PDHs 24 (equals 2.4 CEUs)
Additional Information

Workshop location details will be provided with your confirmation letter, which you will receive once the minimum enrollment requirements are met.

Here are addresses we have used in the past:

Minneapolis, MN (Stat-Ease headquarters): 2021 East Hennepin Ave, Suite 480, Minneapolis, MN 55413

Edison, NJ: Raritan Plaza III, 105 Fieldcrest Avenue, Suite 201, Edison, New Jersey 08837, Website

San Diego, CA: 350 10th Avenue, Suite 950, San Diego, California 92101, Website 

San Jose, CA: 2025 Gateway Place, Suite 390, San Jose, California 95110, Website 

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