Course "Design of Experiments (DOE) for Process and Product Optimization" has been pre-approved by RAPS as eligible for up to 12 credits towards a participant's RAC recertification upon full completion.

Course Description:

This course is designed to help scientists and engineers plan and conduct experiments and analyze the data to develop predictive models used to optimize processes and products and solve complex problems. DOE is an extremely efficient method to understand which variables (and interactions) affect key outcomes and allows the development of mathematical models used to optimize process and product performance. The models also provide an understanding of the impact of variability in controllable and uncontrollable factors on important responses. The concepts behind DOE are covered along with some effective types of screening experiments. Case studies will also be presented to illustrate the use of the methods.

This highly interactive course will allow participants the opportunity to practice applying DOE techniques with various data sets. The objective is to provide participants with the key tools and knowledge to be able to apply the methods effectively in their process and product development efforts.

Learning Objectives:

  • Plan and conduct experiments in an effective and efficient manner
  • Apply good experimental practices when conducting studies
  • Determine statistical significance of main and interaction effects
  • Interpret significant main and interaction effects
  • Develop predictive models to explain and optimize process/product behavior
  • Check models for validity

Seminar Fee Includes:

AM-PM Tea/Coffee
Seminar Material
USB with seminar presentation
Hard copy of presentation
Attendance Certificate
$100 Gift Cert for next seminar

  • Utilize models for one or more responses to find optimal solutions
  • Apply very efficient fractional factorial designs in screening experiments
  • Apply response surface designs for optimization experiments
  • Avoid common misapplications of DOE in practice

Note: Participants are requested to bring a laptop with Minitab Version 17 software installed

Who will Benefit:

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

Course Outline:

Day 1 (8:30 AM – 4:30 PM) Day 2 (8:30 AM – 4:30 PM)
  • 8:30 – 9:00 AM: Registration
  • 9:00 AM: Session Start Time
  • Introduction to Experimental Design
    • What is DOE?
    • DOE vs. One-Factor-at-a-time studies
    • Terminology, Definitions, and Concepts
    • Sequential Experimentation
    • When to use DOE
    • Common Pitfalls in DOE
  • A Guide to Experimentation (Methodology)
    • Planning an Experiment
    • Implementing an Experiment
    • Analyzing an Experiment
    • Case Studies
  • Two Level Factorial Designs
    • Design Matrix and Calculation Matrix
    • Calculation of Main & Interaction Effects
    • Graphing & Interpreting Effects
    • Using Center Points
  • Identifying Significant Effects
    • Describing Insignificant Location Effects
    • Determining which effects are statistically significant
    • Analyzing Replicated and Non-replicated Designs
  • Developing Mathematical Models
    • Developing First Order Models
    • Residuals /Model Validation
    • Solving Models for Possible Solutions
    • Optimizing Response(s)
  • Fractional Factorial Designs (Screening)
    • Structure of the Designs
    • Identifying an “Optimal” Fraction to Run
    • Confounding/Aliasing
    • Resolution
    • Analysis of Fractional Factorial Experiments
    • Other Designs
  • Introduction to Response Surface Designs
    • Central Composite Designs
    • Box-Behnken Designs
    • Optimizing several characteristics simultaneously

Meet Your Instructor

Steven Wachs
VP and Principal Statistician

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty. Steve regularly speaks at industry conferences and provides workshops in industrial statistical methods worldwide.

Credentials and Honors
M.A., Applied Statistics, University of Michigan, 2002
M.B.A, Katz Graduate School of Business, University of Pittsburgh, 1992
B.S., Mechanical Engineering, University of Michigan, 1986

Summa Cum Laude, University of Michigan, 1986
Tau Beta Pi - National Engineering Honor Society, 1986
Associates Fellowship and Highest Honors, University of Pittsburgh 1992
Six Sigma Blackbelt Certification, Ford Motor Company 2001
Ford Education Fellowship, 2001

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