Statistical engineering – Part 1: Past and present

Abstract

After more than a decade since the introduction of Statistical Engineering by Roger Hoerl and Ronald Snee, a group of leading applied statisticians from academia, industry, and government were invited to discuss their perspectives on progress made, the current status of this important movement, and what future Statistical Engineering holds on the path forward in a series of two panel discussion papers. In this first article, the invited panelists focus their discussion on the past and present of Statistical Engineering. They discuss notable advances and current obstacles to progress. They also consider the unique value added by Statistical Engineering, and the possible addition of decision making to the body of knowledge. The format of the article consists of the posed questions from the moderators, a summary of key ideas from all the panelists, and then the individual detailed answers. The goal of this series of articles is to inspire statisticians to consider their possible role to advance the adoption of Statistical Engineering to solve important problems.

Keywords:

• Collaboration
• complex problem solving
• data-driven solutions
• data pedigree
• decision making
• effective communication
• interdisciplinary
• problem formulation
• structured processes
• system integration

Disclosure statement

The responses provided by the NIST panelists (Guthrie and Leber) are their own opinions and do not represent that of NIST or the federal government.

Additional information

Notes on contributors

Christine M. Anderson-Cook

Christine M. Anderson-Cook is a statistician and Retired Guest Scientist in the Statistical Sciences Group at Los Alamos National Laboratory. Her research areas include statistical engineering, reliability, design of experiments, multiple criterion optimization, and response surface methodology. She is a Fellow of the American Statistical Association (ASA) and the American Society for Quality (ASQ). [email protected]

Lu Lu

Lu Lu is an Associate Professor of Statistics in the Department of Mathematics and Statistics at the University of South Florida in Tampa. She was a postdoctoral research associate in the Statistics Sciences Group at Los Alamos National Laboratory. Her research areas include statistical engineering, reliability analysis, design of experiments, response surface methodology, survey sampling, and multiple objective/response optimizations.

William Brenneman

William Brenneman is a Research Fellow and Global Statistics Discipline Leader at Procter & Gamble. He is also an Adjunct Professor of Practice at Georgia Tech in the Stewart School of Industrial and Systems Engineering. His research areas include design and analysis of physical and computer experiments, robust parameter design, statistical engineering, reliability and process control. He is a Fellow of ASA and ASQ.

Jeroen De Mast

Jeroen de Mast is professor of Data-Driven Business Innovation at the University of Amsterdam. Before, he was the Academic Director of Professional Education at the Jheronimus Academy of Data Science (JADS) and professor of Statistics at the University of Waterloo. Throughout his career, Jeroen has worked extensively as a management consultant in operations improvement, industrial engineering and data science. His research focuses on analytical problem solving and data-driven business improvement.

Frederick Faltin

Frederick Faltin is an Associate Professor of Practice in the Department of Statistics at Virginia Tech, teaching principally in VT’s Computational Modeling and Data Analytics major. He is also co-Founder of The Faltin Group. His research has centered on issues in process monitoring and control. He is a Fellow of ASA.

Laura Freeman

Laura Freeman is a Research Associate Professor of Statistics and serves as the Deputy Director of the Virginia Tech National Security Institute. Her research leverages experimental methods for conducting research that brings together cyber-physical systems, data science, artificial intelligence (AI), and machine learning to address critical challenges in national security. She develops new methods for test and evaluation focusing on emerging system technology. She is also the Assistant Dean for Research for the College of Science, where she works to shape research directions and collaborations in across the College of Science in the Greater Washington D.C. area. Dr. Freeman has a B.S. in Aerospace Engineering, a M.S. in Statistics and a Ph.D. in Statistics, all from Virginia Tech. Her Ph.D. research was on design and analysis of experiments for reliability data.

William Guthrie

William F. Guthrie is the Chief of the Statistical Engineering Division (SED) at the National Institute of Standards and Technology (NIST). Prior to becoming Division Chief, he spent 25 years as a practicing statistician in the SED working with NIST scientists and engineers on a wide range of applications in metrology, the science of measurement. He is a Fellow of ASA and has received both NIST and Departmental awards over the years for contributions to collaborative projects ranging from development of internationally-recognized reference functions for standard thermocouple types to publication of the NIST/SEMATECH e-Handbook of Statistical Methods (https://www.itl.nist.gov/div898/handbook/).

Roger Hoerl

Roger Hoerl is Associate Professor at Union College in Schenectady, NY. Prior to coming to Union, he led the Applied Statistics Lab at GE Global Research. He is a Fellow of ASQ

Willis Jensen

Willis Jensen leads the HR Analytics team at W.L. Gore & Associates, where he helps solve business problems across the globe with data. He previously worked as a statistician and led the Global Statistics team at Gore. He earned a Ph.D. in Statistics from Virginia Tech University. He is a recipient of the Shewell, Bisgaard and Nelson awards from ASQ and is an ASQ Fellow.

Allison Jones-Farmer

Allison Jones-Farmer is the Van Andel Professor of Business Analytics at Miami University in Oxford, Ohio. She is the current Editor-in-Chief of Journal of Quality Technology. Her research focuses on developing practical methods for analyzing data in industrial and business settings, including statistical process monitoring and business analytics. She is a senior member of ASQ.

Dennis Leber

Dennis D. Leber has spent the past 20 years as a statistician in the Statistical Engineering Division (SED) at the National Institute of Standards and Technology (NIST) where he leads the Statistical Design, Analysis, and Modeling Group. He holds a M.S. degree in statistics from Rutgers University and a Ph.D. in mechanical engineering from the University of Maryland. With the pre-data aspect of statistics being at the forefront of his interests, e.g., problem formulation and experiment design, Dennis’ research focuses on decision analytics and data collection strategies to support selection decisions.

Angela Patterson

Angela Patterson is a Chief Consulting Engineer at General Electric and Professor of Practice in the Virginia Tech Department of Statistics. At VT, she co-leads the Capstone course in the Computational Modeling and Data Analytics major. Her research is in response surface methods with applications to both physical and computer experiments, as well as in process monitoring.

Marcus Perry

Marcus Perry is a Professor of Applied Statistics in the Department of Information Systems, Statistics and Management Science in the Culverhouse College of Business at the University of Alabama. His research interests include statistical process monitoring, design and analysis of experiments, regression analysis, and predictive modeling.

Stefan H. Steiner

Stefan Steiner is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His primary research interests include quality improvement, process monitoring, experimental design and measurement system assessment. He is a Fellow of ASA and ASQ.

Nathaniel T. Stevens

Nathaniel Stevens is an Assistant Professor of Statistics in the Department of Statistics and Actuarial Science at the University of Waterloo. His research interests lie at the intersection of data science and industrial statistics. His publications span topics including experimental design and A/B testing, social network modeling and monitoring, survival and reliability analysis, measurement system analysis, and the development of estimation-based alternatives to traditional hypothesis testing.