Process Optimization

DOE - Design of Experiments - The efficient planning and running of successful statistical tests

Benefits of DOE:

"Design of experiments" is a method for planning statistical tests and experiments in the development and improvement phase of products and processes.

Because every test requires an expense of manpower, material and time, DOE improves the situation by reducing the number of required tests. The goal is to get maximum knowledge about the variables and unknowns with minimum testing.

The approach is to develop test runs by varying multiple factors within one test and then using statistical methods to analyze the results. In this manner, the total number of test runs and expense can be reduced.

Our Range of Services:

We will train your staff in DOE. You can decide whether the course is held in your local facility or in a perfectly equipped conference center (to keep the daily routines at bay). We can also conduct your Design of Experiments as a consulting service. In this case our experts will explain the procedure and the requirements to you and your staff in a step by step manner, defining all the needed activities and parameters. This way you and your staff will gain valuable experience in DOE and in controlling your process parameters.

Content:

The participant will learn the following: how to successfully integrate DOE methods into the daily routines of their company, how to optimize processes by identification of key product parameters, how to design robust products with minimal variation, the scope and limitations of DOE, the practical approach to DOE with theory kept to a minimum, an understanding of the advantages and drawbacks of the method, and best practice approaches with theoretical/mathematical background.

- The Taguchi and Shainin methods
- Ultramax and genetic algorithms including project selection and problem analysis
- Experimental design with orthogonal arrays
- Allocation of signal parameters, noise factors and interactions
- Signal-to-noise-ratio analysis, analysis of variance (ANOVA), dynamic design
- Interpretation of test results and confirmation tests
- Product optimization (product parameters)
- Process optimization (process parameters)