Case Studies


Published: June 2014
Author: OMG Borchers

OMG Borchers used the design of experiments method to develop a mixture experiment to screen the effects of four candidate additives. The challenge was to find a second source for an associative thickener used in a family of waterborne coatings that matched the properties of the incumbent thickener in three different classes of products with a different chemical composition than the incumbent.

Publication: Paint & Coatings Industry

Published: June 2014
Author: Ron Stites

An industrial equipment supplier wanted to find the best operating conditions, as well as determine what performance its product could deliver for ethanol producers, before putting the device on the market. A DOE was run to successfully identify and validate a measurement method that has enabled the supplier to accurately evaluate the performance of the new product in a large number of plants under a wide range of operating conditions.

Publication: Ethanol Producer Magazine

Employing Power to "Right-Size" Design of Experiments

Published: March 2014
Authors: Mark Anderson, Patrick Whitcomb

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" link to view the manuscript.)

Publication: The ITEA Journal

Published: March 2014
Authors: Mark Anderson, Patrick Whitcomb

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.

Publication: Journal of Statistical Science and Application

Improved Copper and Gold Recovery at KGHM International’s Robinson Mine

Published: January 2014
Authors: Lorin Redden, Chase Stevens, Mark O'Brien, Thomas Bender

In an effort to recover additional copper and gold at KGHM International's Robinson Mine located near Ruth, Nevada, an in-plant study was undertaken to quantify potential flotation recoveries from the concentrator's final tailings stream. Tests were conducted by passing a small continuous sample of final tailings through a single 1.5 m3 FLSmidth XCELL™ demonstration flotation machine. This paper reviews the results obtained from the in-plant testing with the single 1.5 m3 flotation cell and provides a comparison to the subsequent operational performance of multiple 160 m3 flotation machines

Published: May 2012
Authors: Mark Anderson, Wayne Adams

BOOK REVIEW: This book provides guidance on the construction of experiments, including sample size calculations, hypothesis testing, and confidence estimation.

Publication: Technometrics

Design of Experiments for Non-Manufacturing Processes: Benefits, Challenges, and Some Examples

Published: November 2011
Authors: Mark Anderson, Jiju Antony, Shirley Coleman, Rachel Johnson, Douglas Montgomery

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. There have not been as many efforts to apply powerful quality improvement techniques such as DOE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DOE in non-manufacturing arena are gathered.

Publication: Journal of Engineering Manufacture

White Paper on Optimal Design Tools

Published: December 2010
Author: Mark Anderson

Our new version of Design-Expert now offers additional optimal design options not just D-optimal. The most popular of these new options is likely to be the 'IV ' optimal design, which makes use of an integrated variance criterion that minimizes the average variance for responses throughout a region of interest. An IV-optimal design tends to place fewer runs at the extremes of the experimental region than D-optimal.

Published: March 2010
Author: DE Editors

To accelerate their product development, Z Corporation tooled up their engineers with the knowledge and software to do statistical design of experiments (DOE). The company developed a procedure by which every factor with a reasonable chance of affecting product performance is systematically and simultaneously evaluated via these controlled experiments.

Publication: Digital Engineering

Automated Optimization of a Multiplex PCR Using Sagian AAO Software for the Biomek FX Liquid Handling System

Published: October 2007
Authors: Dana Campbell, Lisa Fan, Keith Roby, Graham Threadgill, Patrick Whitcomb

Optimizing biological assays conditions is often a challenging process facing scientists. The demand to produce quality and robust assays that work across a range of biological conditions is often strived for along with a short development timeframe. In addition, automated systems are often required to enable scientists to screen in a high-throughput environment.

Publication: Beckman Coulter