A little background and motivation for the material in this course.
Instructor Introduction / Background
Course Format / Materiasl / Software
The material was very clearly articulated. Allise is very knowledgeable about the subject as well as very excited about it. She does a good job explaining the topics and putting them into real-world applications.
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. Minitab or other statistical software is utilized in the class.
Allise Wachs, Course Instructor
She is the President of Integral Concepts, Inc. where she assists engineers and scientists in the application of statistical and optimization methods to reduce the time and cost to develop new products and optimize manufacturing processes. She also helps her clients to resolve complex engineering, R&D, and manufacturing problems quickly and thoroughly. Allise has facilitated hundreds of designed experiments, and regularly consults with companies in numerous industries. Her communication/training skills are rated as outstanding.
I got more in three days than from 20+ years in manufacturing. Allise has excellent command of the material and has been able to share some of her knowledge with us.
— former DOE course student
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.
Without question, this class was the most beneficial training I have received for my particular job function. I will be able to utilize the information on the job immediately.
— former DOE course student
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
Product and Process Engineers
Personnel involved in product development and validation