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On-line/On-Demand DOE Training Now Available

Design of Experiments (DOE) Training (On-site or Virtual)

The objective of Design of Experiments Training is to provide participants with the analytical tools and methods necessary to:

  • Plan and conduct experiments in an effective and efficient manner
  • Identify and interpret significant factor effects and 2-factor interactions
  • Develop predictive models to explain process/product behavior
  • Check models for validity
  • Apply very efficient fractional factorial designs in screening experiments
  • Handle variable, proportion, and variance responses
  • Avoid common misapplications of DOE in practice

Participants gain a solid understanding of important concepts and methods to develop predictive models that allow the optimization of product designs or manufacturing processes. Many practical examples are presented to illustrate the application of technical concepts. Participants also get a chance to apply their knowledge by designing an experiment, analyzing the results, and utilizing the model(s) to develop optimal solutions (in the 4-days DOE Training program). Minitab or other statistical software is utilized in the class.

Seminar Content (3 or 4 Days)

  1. Introduction to Experimental Design
    • What is DOE?
    • Definitions
    • Sequential Experimentation
    • When to use DOE
    • Common Pitfalls in DOE
  2. A Guide to Experimentation
    • Planning an Experiment
    • Implementing an Experiment
    • Analyzing an Experiment
    • Case Studies
  3. Two Level Factorial Designs
    • Design Matrix and Calculation Matrix
    • Calculation of Main & Interaction Effects
    • Interpreting Effects
    • Using Center Points
  4. Identifying Significant Effects
    • Variable & Attribute Responses
    • Describing Insignificant Location Effects
    • Determining which effects are statistically significant
    • Analyzing Replicated and Non-replicated Designs
  5. Developing Mathematical Models
    • Developing First Order Models
    • Residuals /Model Validation
    • Optimizing Responses
  6. Fractional Factorial Designs (Screening)
    • Structure of the Designs
    • Identifying an “Optimal” Fraction
    • Confounding/Aliasing
    • Resolution
    • Analysis of Fractional Factorials
    • Other Designs
  7. Proportion & Variance Responses
    • Sample Sizes for Proportion Response
    • Identifying Significant Proportion Effects
    • Handling Variance Responses
  8. Intro to Response Surface Designs
    • Central Composite Designs
    • Box-Behnken Designs
    • Optimizing several characteristics simultaneously
  9. DOE Projects (Project Teams)
    • Planning the DOE(s)
    • Conducting
    • Analysis
    • Next Steps

Why is DOE Training Important?

Experimentation is frequently performed using trial and error approaches which are extremely inefficient and rarely lead to optimal solutions.  Furthermore, when it’s desired to understand the effect of multiple variables on an outcome (response), “one-factor-at-a-time” trials are often performed.  Not only is this approach inefficient, it inhibits the ability to understand and model how multiple variables interact to jointly affect a response.  Statistically based Design of Experiments provides a methodology for optimally developing process understanding via experimentation.

In this course, participants gain a solid understanding of important concepts and methods in statistically based experimentation.  Successful experiments allow the development of predictive models for the optimization of product designs or manufacturing processes.  Several practical examples and case studies are presented to illustrate the application of technical concepts.  This course will prepare you to design and conduct effective experiments.  You will also learn how to analyze the data from experiments to understand significant effects and develop predictive models utilized to optimize process behavior.

DOE has numerous applications, including:

  • Fast and Efficient Problem Solving (root cause determination)
  • Shortening R&D Efforts
  • Optimizing Product Designs
  • Optimizing Manufacturing Processes
  • Developing Product or Process Specifications
  • Improving Quality and/or Reliability
  • Ensure designs are robust against uncontrollable sources of variation

 

Typical Attendees:

  • Scientists
  • Product and Process Engineers
  • Design Engineers
  • Quality Engineers
  • Personnel involved in product development and validation
  • Laboratory Personnel
  • Manufacturing/Operations Personnel
  • Process Improvement Personnel
  • Six Sigma professionals
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