Design of experiments (DOE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product process design and development. There have not been as many efforts to apply powerful quality improvement techniques such as DOE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DOE in non-manufacturing arena.
See how the diagnostic features of Design-Expert enabled a post-doctoral researcher to achieve a valid analysis, and graduate on time. Lesson learned: Checking diagnostics just might save your next experiment.
The TRW team used a combination of DOE (response surface methodology RSM) and Monte Carlo analysis to optimize a braking system where one of the objectives was to quickly generate pressure when demanded by the vehicle stability control system.
FLSmidth recently installed two turnkey SuperCell flotation machines the world's largest flotation cells at Rio Tinto's Kennecott Utah Copper concentrator near Salt Lake City, Utah. They used DOE to substantially reduce the amount of testing and fine-tuning required after installation.
In this presentation design of experiments (DOE) was applied to a chemical process. DOE together with computer modeling lead to a better understanding of the process and the defining of new conditions.
DOE was used to determine the optimum setting conditions for three components leading to a high Hg Yield at a lower temperature.
RTP Company, which compounds custom engineered thermoplastics, used Design-Expert software to determine which injection molding process conditions would optimize conductive properties for a particular material. Their DOE made it possible to explore the complete processing space and provided users with a formula to calculate the conditions that would deliver the required resistivity levels.
Statistical methods are becoming increasingly important for the pharmaceutical industry. The FDA and other regulatory and standard-setting organizations are moving swiftly to establish Quality by Design (QbD) guidance relevant to the needs of pharmaceutical manufacturing. The FDA suggests the use of design of experiments (DoE) because it provides a structured, organized method for determining the relationship between factors affecting a process and the response of that process.
Via application of response surface methods, Wyeth Pharmaceuticals substantially increased yield of an active ingredient at more robust process conditions.
The statistical design of experiments is an essential ingredient of successful product development and improvement, and provides an efficient and scientific approach to obtaining meaningful information. In contrast to traditional vary one-factor-at-a-time (OFAT) experimentation, variables are changed together, permitting evaluation of interactions. Standard texts give details about the construction of specific test plans, such full and fractional factorial, and response surface designs, and the analysis of the resulting data. This article gives a brief overview. The focus here is on the fundamental elements of experimental design: defining the purpose and scope of the experiment, differentiating between alternative types of experimental variables, understanding the underlying environment and constraints, and conducting stage-wise experimentation. Brief discussions dealing with the statistical analysis tools, multiple response variables, and some historical background are also provided.