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.

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!

A winning business strategy lays out a path with small steps that allows for changes in direction along the way. Our “SCO” flowchart for experimenters is a prime example of such a template for success. Its tried-and-true* core is screening (“S”), characterization (“C”) and optimization (“O”). However, we added one last, but perhaps most important, step: Confirmation. Let’s dive into the Stat-Ease strategy for experimenters and find out what makes it work so well.

Our starting point is the Screening design. Screening designs provide a broad, but shallow, search for previously unknown process factors. TIP – don’t bother screening factors that are already known to affect your responses! Newly discovered factors—the “vital few” carry forward into the next phase of experimentation, with the “trivial many” being cast aside. By using medium-resolution (Res IV) designs—color-coded yellow in the primary two-level factorial builder in Design-Expert® software (DX), you can screen for main effects even in the presence hidden interactions. If runs must be closely budgeted, take advantage of the unique Minimum-Run Screening designs in DX.

Moving ahead to Characterization with the vital-few screened factors plus the big one(s) you set aside, the identification of two-factor interactions becomes the goal. This necessitates a high-resolution design (Res V or better)—the green ones in DX’s main builder. To save runs, consider a Minimum-Run Characterization design. Either way, be sure to add center points at this stage so you can check curvature. If curvature is NOT significant, then your mission is nearly complete—all that remains is Confirmation!

If curvature does emerge as being significant and important, then move on to Optimization using response surface methods (RSM). The beauty of RSM is that, with the aid of DX and its modeling and graphics tool, you can see by contour and 3D maps where each response peaks. Also, via numerical tools, DX can pinpoint the setup of factors producing the most desirable outcome for multiple responses. Then it lays out a compelling visual of the sweet spot—the window where all specifications can be achieved.

Last, but not least, comes Confirmation, during which you do a number of runs to be sure you can reproduce the good results. Use the special tool for confirmation that DX provides to be confident of this.

In conclusion, DOE does not provide a single template that you can repeat over and over. You must apply a strategy, such as the one outlined here, that adapts at each stage of your journey to a new and improved process that saves money at an improved quality level. Why not go after it all!

Learn more about the Stat-Ease strategy for experimenters by attending the Modern DOE for Process Optimizationworkshop or by reading the DOE Simplified textbook.

*Strategy of experimentation: Break it into a series of smaller stages, Mark Anderson, StatsMadeEasy blog, 6/20/11.

Two-level factorial designs are highly effective for discovering active factors and interactions in a process, and are optimal for fitting linear models by simply comparing low vs high factor settings. Super-charge these classic designs by adding center points!
(Read to the end for a bonus video clip!)

There is an underlying assumption that the straight-line model also fits the interior of the design space, but there is no actual check on this assumption unless center points (the mid-level) are added to the design. Figure 1 illustrates how the addition of center points helps you detect non-linearity in the middle of the experimental space.

A center point is located at the exact mid-point of all factor settings. The example in Figure 2 shows a cookie baking experiment where the center point is replicated four times at the mid-point of 10 minutes and 350 degrees.

Multiple center points (replicates) should be randomized throughout the other experimental conditions to get an adequate assessment of whether the actual values measured at this point match what is predicted by the linear model. This is called a test for curvature. If the curvature test is significant, this is considered evidence that a quadratic or higher order model is required to model the relationship between the factors and the response. If the curvature test is not significant, then it is okay to assume that the linear model fits in the middle of the design space.

In Design-Expert® software, version 11, the curvature test is placed in front of the ANOVA when you have included center points in the design. This immediately shows you if the model is significant, and if the curvature is significant. As illustrated by the screen shot below (Figure 3), advice is provided to guide your next steps.

New to DX11, is the “Remove Curvature Term” button. If curvature is significant and you click on this button, then the regression modeling is done by using all the data, including the center points. Because the actual center points are not sitting in the middle of the design space, it is highly likely that the resulting model will be poorly fit and the lack of fit statistic will be significant. Then, click on “Add Curvature Term” to put the curvature effect back into the model, thus accounting for the information in the middle of the design space.

Ultimately, if curvature is significant, the recommendation is to augment the design to a response surface design to better model the relationship between the factors and the response. If curvature is NOT significant, then proceeding with the analysis is acceptable.

For further details on curvature, check out the DOE Simplified textbook, or enroll in an upcoming Modern DOE for Process Optimization workshop.

Bonus: Check out Mark’s 1-minute video on this topic: MiniTip 2 - Center points in factorials