Forma Scientific’s cooling unit is a critical component that sits within a blood analyzer being built by Ortho-Clinical Diagnostics, a Johnson & Johnson company. To do its job well, the cooler’s specification called for a constant 6°C temperature with enough capacity to remove heat introduced during operation. Scientists used DOE to identify several factors and interactions that were affecting its performance and prove that cooling coil temperature could be decreased while still providing ample refrigerating capacity. The savings from this finding exceeded $18,000 because an environmentally controlled testing facility was not needed.
Design of Experiments (DOE) is an essential tool for product and process improvement. Good software now makes the set up for design and analysis of experiments very easy, but many engineers and/or non-statisticians feel intimidated by statistical outputs. For that reason, non-statisticians need training in proper designing and conducting of experiments. Ideally the DOE training is best when provided on a just-in-time basis - prior to actually doing an experiment. However, an in-class experiment is a reasonable substitute for real-life experiments. It is important for technicians to gain an understanding of designed experiments so that mistakes made in conducting the experiment are reduced and the data is collected more correctly and accurately from the experiments. Managers read and possibly edit reports, and make decisions based on the experiments run by technicians and engineers. An understanding of design and analysis techniques helps them identify any problems with the experiment or the results. At SEMATECH, there are two general audiences. The first is comprised of project engineers. They are responsible for designing and conducting experiments to determine if the project they work on meets its goals. Most of them take an eight day long course on designed experimentation that covers many basic concepts. The second group is comprised of project managers and technicians. This group does not have as great a need for the details of experimental design as the first group. Project managers manage the project engineers, and the technicians carry out the experimentation under the direction of the project engineers. The technicians also assist the project engineers in running the equipment on which the experiments are conducted. This helps ensure that experimental runs and data collection proceed smoothly. The project managers oversee all aspects of the experiment and must understand the outcomes of the experiments too. So the managers must provide adequate resources for the experiment, must know why certain designs are used, and be able to interpret and critique the analysis intelligently. The emphasis of the exercise is to motivate, illustrate and provide hands on experience with the methodologies and analysis techniques discussed in class.
Case study from Douglas Montgomery's <em>Design and Analysis of Experiments, 4th Edition</em>, John Wiley, Example 5-4 on page 196.
The latest versions of dedicated DOE software exhibit more versatility than ever before to create optimal designs that handle any combination of mixture components, processing factors (such as time or temperature) and categorical variables (such as supplier and material type). These computer programs easily manipulate almost any number of responses in powerful optimization routine that reveal "sweet spots" - the operating windows that meet all specifications at minimal cost. In this paper, we review the basic principles of mixture design. Then we apply state-of-the-art tools for optimal design to the formulation of a coating.
See how statistically-based mixture design of experiments (DOE) make breakthrough improvements in cost and performance of paints and coatings. Dedicated DOE software exhibit make it easy to create optimal designs that handle any combination of mixture components, processing factors (such as time or temperature) and categorical variables (such as supplier and material type). They easily manipulate almost any number of responses in powerful optimization tools that reveal "sweet spots" - the operating windows meeting all specifications at minimal cost.
Quality managers who understand how to apply statistical tools for design of experiments (DOE) are better able to support use of DOE in their organizations. Ultimately, this can lead to breakthrough improvements in product quality and process efficiency.
Fractional two-level factorials are a powerful tool for making significant improvements to product quality and process efficiency. Unfortunately, this approach to design of experiments (DOE) may alias the main effects with their interactions. Then it is no longer clear which factors truly influence the process. In part 1, this paper illustrates the use of graphical technique for the viewing alternative aliased interactions. The graphical procedure enhances, but does not remove, the guesswork required when a highly-fractional design produces significant effects. The only sure way to pin down the actual effects will be to perform follow up experiments, which will be discussed in Part 2. A technique called "foldover" is tailor-made for de-aliasing effects. This sequential approach to DOE offers a great deal of flexibility to the quality engineer.
In this article from Quality Digest, the author explores the basics of design of experiments (DOE) and why industry needs to adopt it. He specifically explores how DOE software can help an experimenter make breakthrough discoveries.
A basic article to build awareness of the benefits of DOE and response surface methods (RSM) for process optimization.
Design of experiment (DOE) tools provide an efficient means for you to optimize your process. But, you shouldn't restrict your studies only to process factors. Adjustments in the formulation may prove to be beneficial, as well. A simple but effective strategy of experimentation involves: 1. Optimizing the formulation via mixture design; and 2. Optimizing the process with factorial design and response surface methods. This article shows you how to apply DOE methods to your formulation. A case study gives you a template for action.