Articles authored by Mark J. Anderson.

• Optimal Experimental Design with R
  Authors: Adams, Wayne F.; Anderson, Mark J.
  BOOK REVIEW: This book provides guidance on the construction of experiments, including sample size calculations, hypothesis testing, and confidence estimation.
  Technometrics May 2012

• Framing a QbD Design Space with Tolerance Intervals
  Author: Anderson, Mark J.
  Given the push for Quality by Design by FDA and agencies worldwide, statistical methods are becoming increasingly vital for pharmaceutical manufacturers. Design of Experiments (DOE) is a primary tool because it provides a structured, organized method for determining the relationship between factors affecting a process and the response of that process.
  Pharma QbD May 2012

• Design of Experiments for Non-Manufacturing Processes: Benefits, Challenges, and Some Examples
  Authors: Anderson, Mark J.; Antony, Jiju; Coleman, Shirley Y.; Johnson, Rachel T.; Montgomery, Douglas C.
  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
  Procedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture November 2011

• What to Look for in Statistical Software
  Author: Anderson, Mark J.
  Statistical methods are becoming increasingly important for the pharmaceutical industry. The FDA and other regulatory and standard-setting organizations are moving swiftly to establish Quality by Design (QbD) guidance relevant to the needs of pharmaceutical manufacturing. The FDA suggests the use of design of experiments (DoE) because it provides a structured, organized method for determining the relationship between factors affecting a process and the response of that process.
  Pharma QbD January 2011

• White Paper on Optimal Design Tools
  Author: Anderson, Mark J.
  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.
  Stat-Ease Inc. December 2010

• Design Of Experiments
  Authors: Anderson, Mark J.; Whitcomb, Patrick J.
  The statistical design of experiments is an essential ingredient of successful product development and improvement, and provides an efficient and scientific approach to obtaining meaningful information. In contrast to traditional vary one-factor-at-a-time (OFAT) experimentation, variables are changed together, permitting evaluation of interactions. Standard texts give details about the construction of specific test plans, such full and fractional factorial, and response surface designs, and the analysis of the resulting data. This article gives a brief overview. The focus here is on the fundamental elements of experimental design: defining the purpose and scope of the experiment, differentiating between alternative types of experimental variables, understanding the underlying environment and constraints, and conducting stage-wise experimentation. Brief discussions dealing with the statistical analysis tools, multiple response variables, and some historical background are also provided.
  Kirk-Othmer Encyclopedia of Chemical Technology September 2010

• Making Use of Mixture Design to Optimize Olive Oil - A Case Study
  Authors: Anderson, Mark J.; Whitcomb, Patrick J.
  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.
  CPID Newsletter August 2009

• Mixture DOE for Optimal Plasma Etch
  Author: Anderson, Mark J.
  Design of experiments (DOE) provides statistical tools for fab engineers to improve
  their operations. But they needn't restrict their studies only to process factors:
  Adjustments in formulations may prove to be beneficial as well. This article demonstrates how to uncover "sweet spots" where multiple fab-process specifications can be met in a most desirable way. It offers a reallife, semiconductor manufacturing case study that illustrates how to apply powerful response surface methods (RSM) for mixture design and statistical analysis. The resulting predictive models pinpointed a reformulation of plasma that produced more precise etch specifications (smaller offsets in critical dimensions) at greater throughput (selectivity).
  Unpublished August 2008

• Design of Experiments Reduces Rubber Scrap by 90%
  Author: Anderson, Mark J.
  Powerful interactions affect the performance of many rubber and plastics processes. Unfortunately, these critical effects cannot be revealed by the traditional scientific method, which dictates changing one factor at a time (OFAT). This case study provides inspiration to overcome the limitations of OFAT via a very simple design of experiment (DOE) called a two-level factorial. By employing a multifactor approach, the technical staff at a custom rubber molder uncovered a combination of material selection and manufacturing protocol that created unacceptable results. Armed with this process knowledge, they achieved a breakthrough in quality improvements.
  Rubber & Plastic News August 2008

• Response Surface Methods (RSM) for Peak Process Performance at the Most-Robust Operating Conditions
  Authors: Anderson, Mark J.; Whitcomb, Patrick J.
  Response Surface methods (RSM) provide superb statistical tools for design and analysis of experiments aimed at process optimization. At the final stages of process development, RSM illuminates the sweet spot where high yield of in-specification products can be achieved at lowest possible cost. It produces statistically-validated predictive models and with the aid of specialized software, response surface maps that point the way to pinnacles of process performance.
  Unknown June 2008