In the pursuit of quality and efficiency there is a tool powerful enough that it is not only the core of the Six Sigma Black Belt training, but is also included as part of the ISO 9001 standard. This course can provide you with a working knowledge of Design of Experiments and how to apply these experiments for the benefit of your company.
Goals:
Through this course, we provide you with the tools that will enable you to dramatically improve product design and associated processes. In this course, emphasis is given to the techniques of effective data collection and analysis. In order to present this most effectively, we use real life examples and case studies as well as detailed, realistic simulations by Stat-Ease. The goal is to provide participants with practical statistical tools and the knowledge to use them to optimize their processes, reduce variance, and increase yield.
Description:
We will help you understand the basics of designed experiments including the essentials of experiment design, planning, and set up as well as how to best conduct the experiment, collect data, and analyze the results. In addition, you will learn visual techniques such as how to build and check models and how to use simple graphical techniques to analyze your data.
Through this process you will begin to identify the variables that have the greatest impact on the quality of the end product. We will also help you learn the basic skills for creating more complex, multi-level experiments.
Tools:
Among the tools you will learn, we cover creating and using planning guides for more effective experiments, process evaluation and comparison, comparative studies of process performance, framework for P-Optimizations, factorial selection and coding, two level factorial designs, significant test for non-linear effects, fast screening of factors, linear regression, sum of squares, and F-statistics.
Skills:
- Diffuse problems with long-term solutions
- Improve quality of products by optimizing process variables
- Reduce material waste resulting from quality problems
- Increase productivity by reducing quality problems
- Reduce the cost of production due to ability to detect quality problems faster
- Incorporate Total Quality Management and “Do it right the first time, every time”
- Provide a clear picture of process optimization
- Enhance staff morale through quality improvement by scientific means
- Apply statistical techniques to the creation of experiments
- Utilize Multifactor Studies to address complex issues
- Use efficient screening designs to ensure that the data collected is as useful as possible
- Analyze experiments with Multiple Regression tools.
- Learn Stat-Ease analysis and simulation tools
Format:
This course is conducted over 4 days.
Requirements:
Participants should be able to use high-school level algebra.
Who Should Attend:
Quality Managers, Quality Engineers, SPC Coordinators, Consultants, Design Engineers, R&D Personnel, and Product/Process Engineers
Price:
$3,000 per person plus travel with a minimum of 4 and a maximum of 15 participants.