In any business environment, profit or cost savings is unreachable until you’re able to understand and influence the underlying variables that drive results. If there was only one variable or even two impacting your result, management would be easy; unfortunately, in the real world, there are often many variables, all interacting to create uncertain results.
Design of Experiments (DOE) can be used to help understand how variables interact and how to manage and control them for the desired results you’re looking for. Whether the variables are new product features, staffing levels in a call center or hospital, equipment used to manufacture components, or ingredients used to improve the taste and texture of your favorite candy bar, DOE can help increase the cost savings of an existing process improvement and your speed to market for new products or services, while decreasing the investment in and anxiety of your planned experiments.
You’ll learn to:
- Understand and apply the basic and important statistical techniques that give you confidence in the validity of your experimental results
- Explain the principles of experimental design
- Develop an experimental design plan and build a predictive model from the experiment's results
- Understand experimental analysis, including main and interactive effects, experimental error, and residual analysis
- Perform scientific analysis of multi-variants, measurement system, graphical, means, and variance
- Apply experimental design for Lean Six Sigma tools and techniques using Microsoft Excel
- Measurement system analysis
- Paired comparison analysis
- Multi-vari analysis
- Component search
- Factorial experiments and analyses
- Graphical analysis
- Analysis of variance
- Regression analysis
- Have a theoretical understanding of Dorian Shainan, Genichi Taguchi, and classical design of experiments
- Be able to apply DOE concepts to process improvement and product/ service development activities
Pre-requisites:
- Professional Development Certificate: Six Sigma Green Belt
or
- Completion of the course Six Sigma Data Measurement, Analysis, and Tools
or
- Completion of the course Quantitative Methods for Process Improvement - Online (Self-Paced)
Online learning environment and course flow:
Students will need a computer with Internet and Web-browsing capability and the ability to play streaming video similar to those seen on YouTube. The course is divided into three weeks. All students begin each week by learning together on Monday, but each individual may take as much time as they need during the week to complete that week's self-paced module. Each module consists of reading and application assignments followed by a self-help summary. The application assignments may be a combination of individual and team activities. There will be a final multiple-choice quiz and a self-help summary for each module to help you assess your learning progress. The self-help summaries and quizzes will be the foundation for the final examination questions, and the final examination results will be assessed by the instructor. In the event that a passing grade is not achieved, you will be given feedback on the areas of concern and a make-up examination will be provided.
Instructor interaction:
Because of the self-paced nature of the course, all instructor lectures and examples have been previously recorded and are to you at any time during the week of the course that the class is currently immersed in. Instructor office hours will be scheduled for each week based on the class’s preference. During these office hours, you will have real-time access to the instructor. In the event your questions haven’t been answered during the regularly scheduled office hours, you'll have the opportunity to post your question on the course’s Web-based learning management system. A response will be made within 48 hours from the time a question is posted.
Who should attend:
- Product/process engineers or managers
- Quality managers
- Operations managers
- Business systems analysts
- R&D personnel
- Six Sigma practitioners
Week 1
Module 1-1: Getting Started
Module 1-2: The Experimental Design Roadmap
Module 1-3: Statistics Basics Review
Module 1-4: Statistical Analysis Review
Module 1-5: Measurement System Analysis
Module 1-6: Multi-vari Analysis
Module 1-7: Paired Comparisons
Week 2
Module 2-1: Factorial Experiments Basics
Module 2-2: 2x2 Full Factorial Experiments
Module 2-3: Component Search
Module 2-4: Factorial Analysis and Interaction
Week 3
Module 3-1 Fractional Experiments
Module 3-2: Developing and Implementing the Experimental Plan
Module 3-3: Review, Clarifications, Exam, Evaluations, and Close