Monthly Archives: December 2018

  • 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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