Design of Experiments (DOE) for Engineers

Design of Experiments (DOE) 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. This seminar utilizes hands-on activities to help you learn the criteria for running a DOE, the requirements and pre-work necessary prior to DOE execution, and how to select the appropriate designed experiment type to run. You will experience setting up, running, and analyzing the results of simple-to-intermediate complexity, Full Factorial, Partial Factorial, and Response Surface experiments utilizing manual methods as well as a hands-on computer tool that facilitates experimental design and data analysis. You will also receive an overview of Robust DOE, including the Taguchi DOE Method.

Participants will be given information on how to receive, install and configure a fully-functional 30-day trial version of MiniTab® for their use in class, and/or for their personal evaluation. Attendees are required to bring a laptop computer and/or a calculator to the seminar.

Note: Similar courses available on demand! Design of Experiments (DOE) for Engineers (PD530932) or Introduction to Design of Experiments (DOE) for Engineers (PD530932ON).

What Will You Learn

By attending this seminar, you will 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® Software tool to analyze data

Is This Course For You

This seminar will benefit engineers, designers and quality professionals in research, design, development, testing and manufacturing who are interested or active in one or more of the applications listed above. Individuals should have an engineering degree or equivalent coursework in math, statistics and computers.

Materials Provided

This data is not available at this time

Course Requirements

A laptop is required for this course. 


  • Icebreaker: Team Problem Solving Exercise Using Engineering Judgment
  • What is DOE?
    • Types of Designed Experiments
    • Application Examples
    • Where DOE Fits in with Other Tools/Methods
  • DOE Requirements: Before You Can Run an Experiment
    • Writing Problem and Objective Statements
    • Ensuring DOE is the Correct Tool
    • Selecting Response Variable(s) and Experimental Factors
    • Actual vs. Surrogate Responses
    • Attention to Experiment Logistics
    • Test Set-up and Data Collection Planning
    • Selecting and Evaluating a Gage
  • Full Factorial Experiments
    • Introduction to Cube Plots for 3- or 4-factor 2-level Experiments
    • Experiment Set-Up
    • Factor Levels, Repetitions, and "Right-Sizing" the Experiment
    • Experiment Terms to Estimate (Main Effects and Interactions)
    • High-Level Significance Evaluation
  • DOE Statistical Analysis
    • ANOVA Principles for Simple Full Factorial Experiments -- Statistics Basics; Significance Test Methods; Effect of Non-Random Experiments; Estimating Significance Test "Power"; Confidence Intervals; Estimating Random Error
    • Analysis Plots -- Normal and Half-Normal Plots; Main Effect and Interaction Plots
    • Regression Analysis of Simple Full Factorial Experiments
    • Using MiniTab® for Full Factorial DOE Experiments
  • Fractional (Partial) Factorial Experiments
    • The Confounding Principle -- How it Works; What Information We Lose with Confounding (and why we might not care!)
    • Selecting and Using Generators (Identities) to Set Up Confounding Strings
    • Determining Which Factor Combinations to Run
    • Analyzing Fractional Factorial Experiment Data
    • Using MiniTab® for Fractional Factorial Experiments
  • Robust Design Experiments (Overview)
    • What is Robustness?
    • Control and Noise Factors
    • Classical and Taguchi Robust DOE Set-Up
    • Robustness Metrics
    • Analytical and Graphical Output Interpretation
  • Response Surface Modeling
    • What Response Surface Models do BEST
    • Available Response Surface DOEs (Plackett-Burman, Box-Behnken, etc.) -- Ideal Situation(s) to Use Each Response Surface DOE Type; Cube Plot Set-up of Each Response Surface DOE
    • Analyzing Response Surface Experiment Data
    • Methods for Finding Optimum Factor Values
    • Using MiniTab® for response Surface Experiments
  • Miscellaneous Notes and Wrap-up