Case Studies

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  1. How to Handle Hard-to-Change Factors Using a Split Plot

    September 2016
    Carrying out a DOE correctly requires that runs be randomized whenever possible to counteract the bias that may be introduced by time-related trends. If complete randomization proves to be impossible, however, a specialized form of design—called a split plot—is useful because of its ability to effectively group hard-to-change (HTC) factors. It accommodates both HTC and easy-to-change factors in the design.
    Authors: Mark J. Anderson
    Publication: Chemical Engineering
  2. How to Properly Size Response Surface Method Experiment (RSM) Designs for System Optimization

    March 2016
    By sizing experiment designs properly, test and evaluation (T&E) engineers can assure they specify a sufficient number of runs to reveal any important effects on the system. For factorial designs laid out in an orthogonal matrix this can be done by calculating statistical power (Anderson and Whitcomb, 2014). However, when a defense system behaves in a nonlinear fashion, then response surface method experiment (RSM) designs must be employed (Anderson and Whitcomb, 2005). The test matrices for RSM generally do not exhibit orthogonality, thus the effect calculations become correlated and degrade the statistical power. This in turn leads to inflation in the number of test runs needed to detect important performance differences that may be generated by the experiment. A generally acceptable alternative to sizing designs makes use of fraction of design space (FDS) plots. This article details the FDS approach and explains why it works best to serve the purpose of RSM experiments done for T&E.
    Authors: Mark J. Anderson, Wayne F. Adams, Pat J. Whitcomb
    Publication: ITEA Journal
  3. Practical Aspects for Designing Statistically Optimal Experiments

    March 2014
    Due to operational or physical considerations, standard factorial and response surface method (RSM) design of experiments (DOE) often prove to be unsuitable. In such cases a computer-generated statistically-optimal design fills the breech. This article explores vital mathematical properties for evaluating alternative designs with a focus on what is really important for industrial experimenters. To assess “goodness of design” such evaluations must consider the model choice, specific optimality criteria (in particular D and I), precision of estimation based on the fraction of design space (FDS), the number of runs to achieve required precision, lack-of-fit testing, and so forth. With a focus on RSM, all these issues are considered at a practical level, keeping engineers and scientists in mind. This brings to the forefront such considerations as subject-matter knowledge from first principles and experience, factor choice and the feasibility of the experiment design.
    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication: Journal of Statistical Science and Application V2, N3, March, pp 85-92
  4. Employing Power to "Right-Size" Design of Experiments

    March 2014

    This article provides insights on how many runs are required to make it very likely that a test will reveal any important effects. Due to the mathematical complexities of multifactor Design of Experiments (DOE) matrices, the calculations for adequate power and precision (Oehlert and Whitcomb 2002) are not practical to do by 'hand' so the focus is kept at a high level--scoping out the forest rather than detailing all the trees. By example, reader will learn the price that must be paid for an adequately-sized experiment and the penalty incurred by conveniently grouping hard-to-change factors. (The article is not available on the ITEA Journal web site without membership. Click on the Download PDF link below to view the manuscript.)

    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication: The ITEA Journal
  5. Framing a QbD Design Space with Tolerance Intervals

    May 2012

    Given the push for Quality by Design by FDA and agencies worldwide, statistical methods are becoming increasingly vital for pharmaceutical manufacturers. DOE is used to determine the impact of multiple factors and their interaction.

    Authors: Anderson, Mark J.
    Publication: Pharma Qbd
  6. Optimal Experimental Design with R

    May 2012
    BOOK REVIEW: This book provides guidance on the construction of experiments, including sample size calculations, hypothesis testing, and confidence estimation.
    Authors: Adams, Wayne F.; Anderson, Mark J.
    Publication: Technometrics
  7. Design of Experiments for Non-Manufacturing Processes: Benefits, Challenges, and Some Examples

    November 2011
    Design of experiments (DOE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product process design and development.
    Authors: Anderson, Mark J.; Antony, Jiju; Coleman, Shirley Y.; Johnson, Rachel T.; Montgomery, Douglas C.
    Publication: Procedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
  8. DOE It Yourself

    November 2011

    Fun science projects

    Authors: Anderson, Mark J.
    Publication: Stat-Ease, Inc.
  9. What to look for in Statistical Software for the Pharmaceutical Industry

    January 2011

    This article discusses what to look for in DOE software in the pharmaceutical industry.

    Authors: Anderson, Mark J.
    Publication: Pharmaceutical Manufacturing
  10. White Paper on Optimal Design Tools

    December 2010
    Our new version of Design-Expert now offers additional optimal design options not just D-optimal.
    Authors: Anderson, Mark J.
    Publication: Stat-Ease Inc.
  11. Making Use of Mixture Design to Optimize Olive Oil - A Case Study

    August 2009

    Olive oil, an important commodity of the Mediterranean region and a main ingredient of their world-renowned diet (see sidebar), must meet stringent European guidelines to achieve the coveted status of "extra virgin." Oils made from single cultivars (a particular cultivated variety of the olive tree) will at times fall into the lower "virgin" category due to seasonal variation. Then it becomes advantageous to blend in one or more superior oils based on a mixture design for optimal formulation.

    Authors: Anderson, Mark J., Whitcomb, Patrick J.
    Publication: Chemical and Process Industries
  12. Optimal Blending of Rayon Fibers via Statistical Design of Experiments (DOE) Tailored for Formulation

    January 2009

    A statistically based design of experiments (DOE) approach developed specifically for mixtures was used to formulate a blend of rayon fibers that produced maximal tampon absorbency.

    Authors: Anderson, Mark J.; Lazic, Zivorad R.
    Publication:
  13. Response Surface Methods (RSM) Achieve Design for Six Sigma (DFSS) Goals for Medical Device Manufacturing

    October 2008

    Engineers at a major medical device manufacturer used RSM to successfully model a key process for their flagship product. The RSM model then became the foundation for development of robust specifications to ensure quality at six sigma levels.

    Authors: Anderson, Mark
    Publication:
  14. Design of Experiments Reduces Rubber Scrap by 90%

    September 2008

    A custom rubber molder used DOE to uncover a combination of material selection and manufacturing protocol that created unacceptable results. Armed with this process knowledge, they achieved a breakthrough quality improvements.

    Authors: Anderson, Mark J.
    Publication: Rubber & Plastics News
  15. Mixture Design of Experiments (DOE) for Optimal Plasma Etch

    August 2008

    This article demonstrates how to uncover "sweet spots" where multiple fab-process specifications can be met in a most desirable way.

    Authors: Anderson, Mark
    Publication: Fab Engineering & Operations

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