Case Studies

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  1. Technology for Corn Ethanol Measurement

    August 2018
    In this application, a supplier of industrial equipment wanted to market an existing product to companies producing ethanol from corn. There was anecdotal evidence indicating that the device increased ethanol yield. However, prior to marketing the device the company wanted to find the best operating conditions and determine what performance ethanol producers could expect from the product. The industrial equipment supplier contracted with SALLC to develop a method for measuring how well the supplier’s product improved performance to the corn ethanol market.
    Authors: Jerry Fireman
    Publication: Laboratory Focus
  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. How Design of Experiments Can Improve Formulation Development

    March 2016
    Formulation development often boils down to determining the optimum combination of ingredients in a mixture, which can make the difference between success and failure in many diverse fields of research, such as materials, pharmaceuticals, adhesives and coatings. The traditional approach to experimentation changes only one process factor at a time (OFAT) or one component in a formulation. However, with this approach, it’s easy to overlook interactions of factors or components, a likely occurrence in developing formulations.
    Authors: Jerry Fireman
    Publication: R&D Magazine
  4. How To Get More Learning With Less Experimentation

    January 2016
    Experimentation informs every part of the biologic lifecycle. But it is costly and time consuming — especially when you are using outdated methods. As you strive for more efficiency from your scientists and engineers, can you streamline your work processes to get more learning with less experimentation? Both multivariate analysis (MVA) and design of experiments (DoE) methods have numerous applications for simplifying the learning from large data sets and experimentation. However, many scientists and engineers still perceive these methods to be complex. Today’s newer, intuitive software applications make these techniques user-friendly for even non-statisticians. Early adopters are seeing decreased time to market, reduced development and production costs, and improved quality and reliability.
    Authors: Frank Westad
    Publication: CAMO Analytics
  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. Automated Optimization of Murine Embryonic Stem Cell Differentiation into Cardiomyocytes

    January 2011

    Pure populations of cardiomyocytes derived from embryonic stem cells offer great promise as potential cell replacement therapies, as well as for use in pharmaceutical studies. The differentiation process, however, is frequently inefficient and nonspecific. We sought to improve upon the current processes by coupling automation and Design of Experiment (DOE). DOEgenerated combinations of pro-cardiomyocyte compounds were converted to dispensing volumes using Automated Assay Optimization (AAO) for BioRAPTR* software, and reagents and cells were dispensed into 384-well plates using the BioRAPTR FRD* Microfluidic Workstation. After five days of culture, the resulting embryoid bodies (EBs) were transferred to 96-well gelatin-coated plates using the Biomek FXP* liquid handler. Over an additional three days, the wells were observed for spontaneously beating regions of cardiomyocytes, and the differentiated cells were further analyzed using flow cytometry to detect myosin heavy chain as a marker of cardiomyocytes. This bulletin illustrates the potential of a system that can both automate and optimize the differentiation of murine embryonic stem cells.

    Authors: Kowalski, Liu, Yoder, Pajak
    Publication: Beckman Coulter
  7. Design-Expert 7.1 software review

    October 2007

    Felix Grant of Scientific Computing World finds that Design Expert 7.1.3 builds on the many improvements from earlier upgrades.

    Authors: Grant, Felix
    Publication: Scientific Computing World
  8. The 10 Most Common Designed Experiment Errors

    June 2007

    This article offers 10 tips for avoiding the most common designed experiment mistakes. It is derived from Jeff Hybarger's article in the December 2006 Stat-Teaser newsletter.

    Authors: Hybarger, Jeff
    Publication: Design Product News
  9. Interpreting Power in Mixture DOE - Simplified

    October 2006

    This mini-paper by Pat Whitcomb answers the question, "I evaluated my planned mixture experiment and found that it had very low power. Could you tell me what is wrong with the design?" (See pages 5-8 in the link above.)

    Authors: Whitcomb, Pat
    Publication: ASQ Statistics Division Newsletter
  10. Interpreting Power in Mixture DOE - Simplified

    July 2004

    Adapted by Mark Anderson from a detailed manuscript by Pat Whitcomb.

    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication: unpublished
  11. Screening Process Factors In The Presence of Interactions

    May 2004

    This article introduces a new, more efficient type of fractional two-level factorial design of experiments (DOE) tailored for the screening of process factors. These designs are referred to as Min Res IV. Presented at AQC 2004 Toronto.

    Italian translation

    Authors: Anderson, Mark J.; Whitcomb, Patrick J.
    Publication: Conference
  12. Success with DOE

    April 2004

    Article in Quality magazine.

    Available upon request.—reprint #50 (LDB-917)

    Authors: Anderson, Mark
    Publication: Quality
  13. How to Use Graphs to Diagnose and Deal with Bad Experimental Data

    November 2003

    This article deals with thorny issues that confront every experimenter how to handle individual results that do not appear to fit with the rest of the data. (baddata.pdf 70KB) November 2003 (A somewhat modified version of this article was published in Quality Engineering. April 2007.)

    Italian Translation

    Authors: Anderson, Mark &Whitcomb, Pat
    Publication: Quality Engineering
  14. The Six Sigma Method and Design of Experiments

    December 2002

    This article by Peter Peterka (www.6sigma.us) explains what Six Sigma is and DOE's role in it. The DOE FAQ Alert.

    Authors: Peterka, Peter
    Publication:
  15. Small, Efficient, Equireplicated Resolution V Fractions of 2^k designs and their Application to Central Composite Designs

    October 2002

    Gary W. Oehlert & Pat Whitcomb present a class of equireplicated, irregular fractions of two-series designs constructed algorithmically using the D-optimality criterion. Presented at the Fall Technical Conference 2002.

    Authors: Oehlert, Gary W.; Whitcomb, Pat
    Publication: Conference

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