Design and Analysis of Experiments

Learn the best ways to identify variable factors in processes to drive performance excellence

Design and Analysis of Experiments for Continual Improvement of Processes and Products

Other areas of focus will be on how to define process windows and how to look for opportunities to build in robustness. Participants will learn the underlying concepts important to assuring the design of effective experiments and how to assure the most efficient use of resources for running a designed experiment. The course provides plenty of hands-on opportunities to practice in simulated and live processes to assure students walk away with new skills that can immediately be implemented in Six Sigma DMAIC or DFSS projects.

Design and Analysis of Experiments (DOE) are techniques used by designers to determine effects of variable factors that could affect desired output in any design. It helps pinpoint sensitive components and areas in the design that cause the most significant problems. DOE is the ultimate tool for increasing an understanding of cause and effect and applying that learning to design.

At the end of this first portion of the two-course series, participants will understand:

  • How to design, run, analyze and interpret full and fractional two-level factorial designs in order to identify cause and effect and develop solutions to solve problems or improve processes and their outputs
  • Methods for dealing with non-linear cause and effect relationships
  • Strategies for accelerating the attainment of the study goals
  • How to exploit the DOE methodology to assure the effectiveness of day-to-day operations and improvement methods such as Lean and Six Sigma
  • Course reference materials and free post-workshop email and phone support for the design and analysis of designed experiments once you are back on-site

Note: Design Expert Software needs to be installed on your laptop for the course. It can be downloaded a free 45-day trial copy from Stat-Ease.

Day 1

Introduction to Statistically Designed Experiments

  • Origins and objectives of statistically designed experiments (DOE)
  • Workshop 1 — simulation to illustrate the five tenets of a successful DOE
  • DOE protocol — the steps and how they address the five tenets
  • Workshop 2 — part 1 — Manual design and running of a DOE with process simulation with two factors, two levels
  • Workshop 2 — part 2 – Calculation of effects and preliminary determination of significance
  • Intro to basic statistical concepts of signal and noise and the tests to distinguish the two
  • Workshop 2 — part 3 — Determining statistical significance, interaction plots and the model
Day 2

Introduction to the Use of Design Expert Software for the Design & Analysis of Does

  • Tour and demonstration of Design Expert software for the design and analysis of DOEs
  • Workshop 3 — Design and analysis of the simulated DOE from Day 1
  • Workshop 4 — Design and analysis of a simulated process with three factors, two levels with Design Expert software
  • Workshop 5 — Design and analysis of a live DOE with three factors, two levels
Day 3

Introduction to Fractional DOE Designs for Five+ Factors for 2 Levels

  • Introduction to fractional designs, the risks and benefits and how to choose
  • Workshop 6 — Design and analysis of fractional designs for five or more factors and levels
  • Developing process optimization, windows, guidelines, and simulations
  • Workshop to develop optimization, windows, guidelines, and simulations for simulated process
  • Summary of learning for 2-level designs
  • What else is there — Response Surface Methods
  • Conclusions, takeaways, and follow-up

Course Objectives

Upon completion of this three-day course, participants will reinforce their ability to identify variable factors in processes to drive performance excellence

What Career Paths Benefit the Most From Design & Analysis of Experiments for Continual Improvement of Process & Products Workshop?

  • People working with manufacturing or non-manufacturing processes
  • Manufacturing, process and quality managers, and engineers
  • Product and process development and design engineers
  • Continual improvement and process excellence program managers
  • Participants in process and quality improvement teams
  • Six Sigma practitioners

Additional Course Notes

A key objective and strategy with this workshop is that participants leave the workshop feeling comfortable and confident to return home and initiate their own DOE applications. The workshop covers 2-level full and factorial designs in-depth and will typically satisfy at least 75% to 90% of one’s needs for DOE. The workshop introduces the Responses Surface methodology for modeling non-linear cause and effect relationships. In-depth treatment of RSM designs is available in follow-up workshops on DOE.


A background in basic statistical concepts, such as histograms, distributions, standard deviation and mean is helpful but not essential. The agenda includes a brief overview of basic statistical concepts necessary for successfully completing this workshop.

Credit & Follow-Up

“Intermediate Statistical Process Control”, “Design and Analysis of Experiments for Continual Improvement”, “Failure Modes and Effects (FMEA)”, and “Measurement System Analysis (MSA)”.