Stat-Ease Blog

  • 2019 Analytics Solutions Conference: You Know You Want to Go!

    The upcoming 2019 Analytics Solutions Conference is your opportunity to discover how others are using statistical tools [design of experiments (DOE), multivariate analysis (MVA) and process analytical technology (PAT)] to dramatically impact the bottom line.

    And registration is open for the conference!

    Reason #1 to be there: Keynote Speaker Dr. Geoff Vining. Jump-start your education by learning about leading-edge methodology with Geoff’s talk on “Solving Complex Problems”.

    These days, organizations need solutions for increasingly complex problems that are critical to their operation. Statistical engineering is the discipline dedicated to the art and science of solving these complex problems. These problems almost always are unstructured and typically large, crossing several disciplines. Typically, the data associated with them come from a wide variety of sources and thus usually look like a “mess.” The key is how to provide enough definition, data analysis, and structure to create a reasonable path to a truly sustainable solution.

    Statistical engineering provides a structure to determine which statistical and analytic tools/methods are appropriate depending on the circumstances, and it outlines how to create sustainable solutions efficiently and effectively. Ultimately, it is the discipline that helps practitioners determine “the right tool for the right job at the right time, properly applied.”

    This talk introduces this new discipline at a high level. It then outlines the important roles that both DOE and data analytics play in the solution of complex problems. In the process it emphasizes the importance of strategy and understanding exactly what the tools can and cannot do.

    Reason #2 to be there: Cost. At $495 (with the early bird special rates, in place until April 15), this is a truly inexpensive conference. With the tremendous value to be had in technical content for all audiences entry-level to advanced, and the great price point, this is a no brainer.

    Need more reasons to attend? More keynote speakers, fun networking opportunities, and summer in Minnesota!

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    Links:
    Registration: https://www.statease.com/asc-2019/asc-2019-registration
    Speakers: https://www.statease.com/asc-2019/asc-2019-speakers
    General Info: https://www.statease.com/asc-2019

  • Design of Experiments (DOE): The Secret Weapon in Medical Device Design

    “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.

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

  • Mixture Design of Experiments (DOE) to the Rescue!

    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.

    [If the link above does not work for you, copy and paste this into your browser: https://www.statease.com/media/productattachments/files/f/o/formsimp-chap_03_-_olive_oil.pdf]

    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 the link above does not work for you, copy and past this into your browser: https://www.statease.com/training/workshops/class-mixc.html]

    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

    ASC2018KeynoteLogo-1200x628
    Click for a larger image

    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

    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

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

    Click image above for larger (and sharper) version Click image above for larger (and sharper) version
    Click on the options in the wizard to read more about each choice.

    Give it a whirl – Happy Experimenting!

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  • Building a Stronger Team at Stat-Ease

    Greg-Headshot

    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!

  • A Winning Strategy for Experimenters!

    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.

    Stat-Ease SCO Flowchart Stat-Ease SCO Flowchart

     

    Our starting point is the Screening design. Screening designs provide a broad, but shallow, search for previously unknown process factors. TIPdon’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 Optimization workshop 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.

  • Energize Two-Level Factorials - Add Center Points!

    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!)

    Picture1 Cpts Continue reading

  • Stat-Ease, Inc: We've Moved!

    Stat-Ease Office on Broadway Place West

    Stat-Ease has moved approximately 1 mile north up to Broadway Place West. Continue reading

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