Case Studies

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  1. How to Handle Hard-to-Change Factors or Components in a Designed Experiment

    February 2018
    Because interactions abound in the coatings industry, the multifactor and multicomponent test matrices provided by the design of experiments (DOE) approach is very appealing. However, carrying out DOE correctly requires that runs be randomized whenever possible to counteract the bias that may be introduced by time-related trends, such as aging of materials, increasing humidity, and the like. But what if complete randomization proves to be inconvenient or impossible? In this case, a specialized form of design called “split plot” becomes attractive, because of its ability to effectively group hard-to-change (HTC) factors. A split plot accommodates both HTC factors and those factors that are easy to change (ETC).
    Authors: Mark J. Anderson
    Publication: CoatingsTech
  2. 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
  3. Design of Experiments (DoE): Optimizing Products and Processes Efficiently

    November 2014

    Learn how DoE can help save time and money in process design and optimization with this primer. (To read this article, sign up for an account at http://www.chemengonline.com/chemical-engineering-magazine .)

    Authors: Wilhelm Kleppmann
    Publication: Chemical Engineering Magazine
  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. 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
  6. Microscale Analysis and DoE

    June 2013

    Design of experiments (DoE) incorporates statistical methods and multivariate analysis into microscale chemistry. Controlled experiments help analysts evaluate processes with that involve several variables, such as temperature and osmolality in cell culture processes. Often three variables are studied together, with the results expressed in a three-dimensional response-surface graphs

    Authors: Scott, Cheryl
    Publication: BioProcess International
  7. 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
  8. Using DOE with Tolerance Intervals to Verify Specifications

    March 2012

    Statistical methods are becoming increasingly vital for pharmaceutical manufacturers. Design of experiments (DOE) is a primary tool for determining the relationship between the factors that have an effect on a process and the response of that process.

    Authors: Whitcomb, Patrick J., Anderson, Mark J.
    Publication: Stat-Ease, Inc.
  9. DOE It Yourself

    November 2011

    Fun science projects

    Authors: Anderson, Mark J.
    Publication: Stat-Ease, Inc.
  10. 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
  11. Practical Considerations for DoE Implementation in Quality By Design

    June 2010

    This article takes a look at the different things that should be considered when implementing DOE in Quality By Design.

    Authors: Shivhare, Mahesh and McCreath, Graham
    Publication: BioProcess International
  12. 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:
  13. Tabletop Hockey Meets Goals for Teaching Experimental Design

    April 2008

    In this mini-paper, Mark Anderson details an in-class experiment illustrating the power of two-level factorial design. Also learn how to shoot a wicked slap shot!"

    Authors: Anderson, Mark
    Publication: Statistics Division Newsletter
  14. Statistical Design of Experiments on Fabrication of Starch Nanoparticles - A Case Study for Application of Response Surface Methods

    April 2008

    This paper use the fabrication of nanoparticles as an example for showing a statistically-rigorous approach to design and analysis of pharmaceutical experiments.

    Authors: Bukhari, Nadeem Irfan et. al.
    Publication:
  15. Response Surface Methods for Peak Process Performance

    August 2007

    This is the third article of a series on design of experiments (DOE). The first publication provided tools for process breakthroughs via two-level factorial designs. The second article illustrated how to re-formulate rubbers or plastics using powerful statistical methods for mixture design and analysis. The author now brings the focus back to process improvement and shows how to hit the sweet spot of high yield at lowest possible cost.

    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication:

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