Stat-Ease Blog

“Multifactor testing via design of experiments (DOE) is the secret weapon for medical-device developers,” says Stat-Ease Principal Mark Anderson, lead author of the DOE and RSM Simplified series*. “They tend to keep their success to themselves to keep their competitive edge, but one story, that we can share, documents how a multinational manufacturer doubled their production rate while halving the variation of critical-to-quality performance. That’s powerful stuff!”

The case study that Mark is referring to can be found here: RSM for Med Devices.

Stat-Ease has guided many manufacturers in the medical device industry in the streamlining of their products and processes. Now, we have put that experience together into one place, the Designed Experiments for Medical Devices workshop.

In this one-of-a-kind workshop, learn how to optimize your medical device or process. Our DOE experts will show you how to use Design-Expert® software to help you save money or time while ensuring the quality of your product. We welcome scientists, engineers, and technical professionals working in this field, as well as organizations and institutions that devote most of their efforts to research, development, technology transfer, or commercialization of medical devices. Throughout this course you will get hands-on experience while using cases that come directly from this industry.

During a fast-paced two days, explore the use of fractional factorial designs for the screening and characterization of products or processes. Also see how to achieve top performance via response surface designs and multiple response optimization. Practice applying all these DOE tools while working through cases involving medical device design, pacemaker lead stress testing, and typical processes that may be used in the testing or production of these devices such as seal strength, soldering, dimensional analysis, and more.

For dates, location, cost, and more information about this workshop, visit: www.statease.com/training/workshops/class-demd-adin.html

About Stat-Ease and DOE
Based in Minneapolis, Stat-Ease has been a leading provider of design of experiments (DOE) software, training, and consulting since its founding in 1982. Using these powerful statistical tools, industrial experimenters can greatly accelerate the pace of process and product development, manufacturing troubleshooting and overall quality improvement. Via its multifactor testing methods, DOE quickly leads users to the elusive sweet spot where all requirements are met at minimal cost. The key to DOE is that by varying many factors, not just one, simultaneously, it enables breakthrough discoveries of previously unknown synergisms.

* DOE Simplified is a comprehensive introductory text on design of experiments
RSM Simplified is a simple and straight forward approach to response surface methods

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 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.

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!

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.

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 left) asks these questions, guiding you through the decision-making process, ultimately leading you to a recommended starting design.

Give it a whirl – Happy Experimenting!