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Mixture Design of Experiments (DOE) to the Rescue!

posted by Greg on Feb. 7, 2019


So, you formulate products. Yes, you, in the industry of making beverages, chemicals, cosmetics, concrete, food, flavorings, metal alloys, pharmaceuticals, paints, plastics, paper, rubber, and so forth.

You also need to optimize that formulation—quickly. Mixture design of experiments (DOE) to the rescue! “Wait, what?” you ask.

When creating a formulation, you can use mixture DOE to model the blend in the form of a mathematical equation. It also allows you to show the effect each component has on the measured responses, both by itself and in combination with the other components.

This model then leads you to the optimal composition, or “sweet spot”, based on your desired outcome. And mixture design does this fast.

"Yeah, OK," you say. "It's going to take me months to learn how to do that. So, no thanks."

We have software that will do it for you, and a 3-day workshop that shows you how to use it. Not months, 3 days. We get it. You're not a statistician, you don't have the time to learn all the ins and outs of the theory. You just need to get to the information. We'll help you get there.

Ready to know more? Good! Download this white paper that illustrates how mixture design works and how Design-Expert® software will help you.

Ready to know even more? Head over to our Mixture Design workshop webpage and register now. This hands-on workshop will show you how Design-Expert® software can be used to design and analyze mixture experiments. The software provides the power for the generation of optimal designs, as well as sophisticated graphical outputs such as trace plots. You will learn how these methods work and what to look for. No statistical background needed!

If you have any questions regarding the workshop, email us at workshops@statease.com.



Two World-Class Speakers Confirmed for the 2019 Analytics Solutions Conference Keynotes

posted by Greg on Jan. 14, 2019


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Two globally-recognized speakers are committed to keynote the 2019 Analytics Solutions Conference. This premier conference on the applications of industrial analytics, featuring DOE, MVA, and PAT, is being held in Minneapolis, MN June 18-20. The speakers are:

  • Dr. Geoff Vining is an acclaimed statistics researcher, internationally known for his expertise in the use of experimental design for quality, productivity, and reliability improvement and in the application of statistical process control. Geoff will present his views on "Solving Complex Problems".
  • Dr. R. Dennis Cook is known worldwide for his pioneering work on linear and nonlinear regression, experimental design and statistical graphics, but best known as the inventor of the Cook's distance statistic that plays a prominent role in model diagnostics. Dennis will provide "A Primer on Partial Least Squares Regression".

For more information on these talks, head to the ASC 2019 Speakers Page

Would you like to speak? Submit an abstract! All the details can be found on the 2019 Analytics Solutions Conference webpage.



2019 Analytics Solutions Conference Announcement

posted by Greg on Dec. 26, 2018


Discover how data-driven tools can transform your business from R&D to production

Stat-Ease and Camo Analytics are partnering together to host the premier conference on the practical applications of industrial analytics. Topics feature industrial analytics methods; in particular design of experiments (DOE), multivariate analysis (MVA) and process analytical technology (PAT).

The 2019 Analytics Solutions Conference is the perfect venue to discover how others are using these statistical tools to dramatically impact the bottom line. It's a great place to make connections and get inspired. Industries include, but are not limited to: pharmaceutical, medical device, electronics, food science, oil and gas, chemical processes, and aerospace.

It will be held June 18-20 near the Stat-Ease headquarters in Minneapolis, MN — a great time to visit Minnesota. The conference will include workshops, keynote speakers, and fun evening events.

If you have a success story that can be shared, please submit an abstract by February 15. All the details can be found on the Call for Speakers page.

We hope to see you here this coming year!



Four Questions that Define Which DOE is Right for You

posted by Shari on Dec. 13, 2018


Do you ever stare at the broad array of DOE choices and wonder where to start? Which design is going to provide you with the information needed to solve your problem? I’ve boiled this down to a few key questions. Each of them may trigger more in-depth conversation, but the answers are key to driving your design decisions.

  1. What is the purpose of your experiment? Typical purposes are screening, characterization, and optimization. The screening design will help identify main effects (it’s important to choose a design that will estimate main effects separately from two-factor interactions (2FI)). Characterization designs will estimate 2FI’s and give you the option to add center points to detect curvature. Optimization designs generally estimate non-linear, or quadratic effects. (See the blog “A Winning Strategy for Experimenters”.)
  2. Are your factors actually components in a formulation? This leads you to a mixture design. Consider this question – if you double all the components in the process, will the response be the same? If yes, then only mixture designs will properly account for the dependencies in the system. (Check out the Formulation Simplified textbook.)
  3. Do you have any Hard-to-Change factors? An example is temperature – it’s hard to randomly vary the temp setting higher and lower due to the time required to stabilize the process. If you were planning to sort your DOE runs manually to make it easier to run the experiment, then you likely have a hard-to-change factor. In this case, a split-plot design will give a more appropriate analysis.
  4. Are your factors all numeric, or all categoric, or some of each? Multilevel categoric designs work better with categoric factors that are set at more than 2 levels. A final option - Optimal designs are highly flexible and can usually meet your needs for all factor types and require only minimal runs.
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These questions, along with your budget for number of runs, will guide your decisions regarding what type of information is important to your business, and what type of factors you are using in the experiment. Conveniently, the Design Wizard in Design-Expert® software (pictured below) asks these questions, guiding you through the decision-making process, ultimately leading you to a recommended starting design.

Give it a whirl – Happy Experimenting!



Building a Stronger Team at Stat-Ease

posted by Shari on Nov. 28, 2018


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I would like to introduce our newest Stat-Ease team member: Greg Campbell.

Greg is tackling his role as Marketing Manager with energy and a determination to make Stat-Ease® shine! I wanted to learn more about Greg and his thoughts for our company’s future, so I asked him a few questions. Here’s what he had to say:

Tell us a little about your background:
It’s varied. I started with a BA in Biology and worked in a biochemistry lab. I then got my MBA in Marketing and Computer Information Systems and have worked in sales and marketing at different companies over the years (mostly in publishing and manufacturing). Most recently, I founded a company with a partner that sold parts for industrial machinery. All of these items pushed me down the path towards Stat-Ease.

What attracted you to joining the Stat-Ease team?
First, I liked the people I met here; friendly and smart. Next, this company sells a robust piece of software that has been around for years. It’s a great combination that allowed me to get back to concentrating on sales and marketing, it’s what I do best!

You’ve attended a Stat-Ease DOE class – what did you learn about our clients?
That sample size is small and insignificant (see what I did there?), but interesting. Most of the clients I met were engineers and cringed at the mere mention of the word ‘statistics’. But they picked up the concepts quickly and started to run with them! They began planning and using what they had just learned to think about how to run some experiments.

What strikes you as something that people should know about DOE?
You don’t have to like, or completely understand, the statistical theories that form the foundation of DOE (look at my example above). We’ve done all the heavy lifting and programming, now they just need to use the software to get their answers and move on to the next step in their job.

Tell us something about your personal story.
Well in my spare time I like to coach “diamond sports”, baseball and softball. I spent several years coaching baseball for my oldest son. He’s now aged out of those community programs. Now, it’s all about softball with my youngest daughter. Many of the basics of the two sports are the same, but there are lots of differences as well.

Music is also a big part of who I am, both listening to it and playing it. I’m a guitarist and listen to all types of music.

On behalf of the Stat-Ease team, I welcome Greg to Stat-Ease and look forward to working with him!