Design o Experiments was the focus of the latest Technically Speaking webinar Experiment Even More Efficiently Using JMP Definitive Screening. More than 700 registrants, including hundreds of new contacts to Salesforce, enjoyed Jeff Upton’s webinar. In the talk, Jeff outlined how he uses definitive screening designs in JMP to develop a plan to simultaneously characterize and optimize a fermentation process with a case study for pDNA production. Jeff also discussed what design of experiments is; the differences among design options; how to pick the “best” design; and how to build these experimental designs in JMP.
To help JMP sales team engage in more meaningful and productive post-event conversations with customers and prospects, we’ve created the following list of recommended resources to position now that the event has concluded. This list pulls appropriate assets from across our web site.
If you have any questions about recommendations related to this event, please feel free to contact me.
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Recommended Resources
Using Definitive Screening Designs to Get More Information from Fewer Trials, Mastering JMP
This Mastering JMP webinar shows why the customer DOE platform in JMP is the platform of choice. JMP SE Tom Donnelly covers a number of topics, including how to:
- Understand the principles and value of Definitive Screening Designs and how they compare to Fractional Factorial Designs
- Use the JMP Definitive Screening Design platform
- Create a Definitive Screening Design for chemical manufacturing example where a 10-factor process is optimized in just 24 trials
- Augment the design economically using the JMP Custom Design platform to support a response surface model in the several important factors
Definitive Screening Designs with Added Two-Level Categorial Factors, White Paper
From Brad Jones and Chris Nachtsheim, this article, reprinted with permission from the Journal of Quality Technology, shows column-augmented DSDs that can accommodate any number of two-level qualitative factors using two methods. The piece is more advanced, and titled as a white paper, but it’s really a shoclarly article.
Class of Three-Level Designs for Definitive Screening in the Presence of Second-Order Effects, White Paper
Another article from Brad Jones and Chris Nachtsheim, this paper shows why screening designs are attractive for assessing the relative impact of a large number of factors on a response of interest. Experimenters often prefer quantitative factors with three levels because it allows for some assessment of curvature in the factor–response relationship. Yet, the most familiar screening designs limit each factor to only two levels. This article is another one reprinted from The Journal of Quality Technology and provides an example of algorithm for design structure and recommendations for analysis.
Design of Experiments Intro Kit. free online resource
You can access this from the Getting Started with JMP page, but you might just want to send them the DOE link. This gated, online resource hits the highlights for DOE basics: What is it?, critical steps, the JMP DOE platform, different designs in JMP, prediction profiler, etc.
Statistical Thinking for Industrial Problem Solving, free online course
This whole course is seven modules, totaling about 30 hours of self-paced learning. All are excellent, but consider highlighting there two modules: Exploratory Data Analysis and Design of Experiments. The DOE module, in particular, gives a good overview – why it’s better than trial and error and one-factor-at-a-time approaches, introduces different types of designs and provides some DoE best practices.
Design of Experiments: An Essential Tool for Discovery and Innovation DOE Statistically Speaking
The event’s keynote address was delivered by Tim Gardner, Riffyn. After his talk, Gardner was joined by Dirk de Bruyn Ouboter, Syngenta; Sara Timp, Alvotech; and Victor Guiller, FUCHS for an in-depth panel discussion. The panelists shared their experiences and gave attendees an idea of how design of experiments could solve problems, improve decision making and help their organization innovate. Panel also provides real-world DOE case studies from prominent organizations and tips on how to overcome challenges implementing a structured approach to experimentation.
The power of structured experimentation in science and engineering Statistically Speaking
This on-demand Stat Speaking event shows how innovative organizations use DOE for efficient, effective discovery of practical insights. Peter Goos provided the keynote, and Tim Gardner served on the post-plenary panel. The panel showed how Riffyn (and Corning) use design of experiments to solve problems, create new products and advance their organizations. Great for Industrial R&D and manufacturing organizations, the panel talks about the benefits of DOE and how progressive organizations use DOE to gain fundamental knowledge of systems, processes and products.
Optimizing Processes with Design of Experiments, White Paper
This white paper is the most popular asset we have in Resource Center and the best at generating registrations in Salesforce. It includes a really good, high-level discussion of DOE and the benefits it can bring any company. This resource is available in several languages: English, French, German and Italian
The JMP Design of Experiments Advantage, White Paper
This is a little more advanced of a resource that takes a different angle, advancing the idea of using custom designs. It also highlights how well JMP does DOE to “tailor the design to the problem,” and explore multifactor opportunities that exist in almost all real-world situations.
This resource is available in several languages: English French, Italian and Spanish
Optimizing Your Process with Design of Experiments, Tech Speaking Event
Pretty popular on-demand webinar; 3rd most popular at generating registrations in Salesforce. Really good at covering DOE from a high-level. Talks about how to use DOE to deliver hard to manufacture products. Good case study. Topics include: Evolution of DOE; Why DOE is important; When to apply DOE; How to Apply DOE.
Cautionary Tales in Designed Experiments, full length e-book
This e-book is long, but it’s by David Salsburg (well known) and well-written. It’s also long; does a great job of explaining the basics of statistical design of experiments. A bit of a history lesson at times, historical examples of early DOE. The second half of the book covers more statistical concepts, such as various designs for experiments, analysis of variance, Bayes’ theorem in DOE, measurement, and when experiments fail. The book concludes by citing the ubiquity of statistical design of experiments.
JMP On-Air “Doctor DOE” series, JOA videos in the JMP Community
These sessions are really pretty targeted, but I like the format so much I feel like it should be included here. Total of five 20-minute segments that tackle topics related to DOE. Various JMP presenters shows the breadth of DOE experts and humanizes our business a bit with these more casual discussions.
Topics include
