Welcome and Plenary Session (8:00-9:00AM)
Gerald J. Hahn Achievement Award
Wayne Nelson, Wayne Nelson Statistical Consulting
ABSTRACT: The Hahn Award includes an invitation for the awardee to give a plenary talk. This is the most difficult talk I have had to develop. The Conference attendees are all talented, accomplished professionals. What could I say to enlighten them that they don’t already know? The Award recognizes my contributions to my clients’ applications, but old war stories have limited appeal. So I’ve decided to recognize my clients, friends, and colleagues contributions to me and have put together uncensored descriptions of key collaborators and clients and some work we have done. I will acknowledge them for their ideas which I published, and I will reveal those who really deserve the credit and what they did. Also, I will point out the dangers of publishing journal articles and books and discuss the power of data plots, life as an independent consultant, and the benefits of good clients and colleagues.
BIO:Dr. Wayne Nelson is a leading expert on analysis of reliability and accelerated test data. He consults on applications, gives training courses, and works as an expert witness. For 24 years he consulted across the General Electric Co. and received the Dushman Award of GE Corp. R&D for developments and applications of product reliability data analysis. He was elected a Fellow of the Amer. Statistical Assoc. (1973), the Amer. Soc. for Quality (1983), the IEEE (1988) for his innovative developments. He was awarded the 2003 Shewhart and 2010 Shainin Medals of ASQ, the 2005 Lifetime Achievement Award of the IEEE Reliability Society, and now the 2018 Hahn Award of ASA,for outstanding applications and developments of reliability methodology and contributions to reliability education. He authored three highly regarded books Applied Life Data Analysis (Wiley 2004), Accelerated Testing (Wiley 2004), Recurrent Events Data Analysis (SIAM 2003), two ASQ booklets, and 130 journal articles. He spends his free time dancing Argentine tango with angel partners.
Jerry Tarnacki, Senior VP (Ret.), Space Business Unit, Aerojet Rocketdyne
ABSTRACT: While Florida is commonly known as a vacation destination, most people are unaware of the aerospace industry in Florida outside of Cape Canaveral, especially in Palm Beach County. A strong industrial base developed in this area as a consequence of World War II when many technology firms and defense contractors expanded here creating an aerospace technology cluster that still exists today. Aerojet Rocketdyne is a core member of this cluster, which also includes jet engine manufacturer Pratt & Whitney and helicopter manufacturer Sikorsky – A Lockheed Martin Company. The cluster has a strong heritage of and focus on high quality and continuous improvement incorporating statistics and data-based decision-making, driven by the defense industry’s rigorous customer requirements and business practices. These companies design, manufacture, assemble and test high-tech products such as jet engines, rocket propulsion systems and helicopters in Palm Beach County. These key products will be discussed as well as current research and development activity that is being conducted in areas such as hypersonics and additive manufacturing. Thoughts about the future of the aerospace industry in Florida will also be shared.
BIO:As Senior Vice President of Aerojet Rocketdyne’s Space Business Unit (now retired), Jerry Tarnacki led the company’s development and production activities supporting NASA, defense and commercial launch systems, and advanced space and in-space propulsion programs. Mr. Tarnacki joined AR as vice president of Quality & Mission Assurance. He previously served with United Technologies Corporation’s (UTC) Pratt & Whitney Division as vice president of its Maintenance Repair and Overhaul organization, where he was responsible for a $2.5 billion business with 5,500 employees and 18 plants in eight countries. During his tenure with UTC, he also served as VP in the areas of Manufacturing, Quality, Environmental, Health & Safety, and Continuous Improvement. Additionally, he earned distinction as a UTC Achieving Competitive Excellence Certified Gold Assessor and Six Sigma Master Black Belt with General Electric.
Youden Address (4:00-5:00PM)
L. Allison Jones-Farmer, Miami University
ABSTRACT: We are in the midst of a “data revolution” that is transforming our economy. This revolution is as large and profound as other major economic shifts from the introduction of the printing press to the industrial revolution. In this talk, we will briefly explore how the role of the “Data Scientist” has emerged and evolved and compare this to the evolution of the role of the “Industrial Statistician”, discussing the similarities and differences in these two fields. We will highlight a few disruptive technologies that are changing our roles as statisticians, including open source software like R and Python, Application Program Interfaces (APIs), GitHub, and Artificial Intelligence. We will discuss how we can leverage these modern technologies to transform our roles and better meet the needs of the data economy. Finally, we will discuss our duty to ethically and responsibly implement these tools, as well as to educate the next generation so that we are addressing important problems and so that the opportunities of the future belong to us all.
BIO: Dr. L. Allison Jones-Farmer is the Van Andel Professor of Analytics in the Department of Information Systems and Analytics and the Founder of the Center for Analytics and Data Science at Miami University in Oxford, Ohio. Her research focuses on developing practical methods for analyzing data in industrial and business settings. She is on the editorial review board of Journal of Quality Technologyand is a former Associate Editor of Technometrics. In 2014 she was awarded the Lloyd Nelson Award for the paper in Journal of Quality Technologywith the most immediate impact to practitioners. Dr. Jones-Farmer enjoys developing innovative curricula and teaching analytics and statistics to both undergraduate and graduate students. She has served on the faculty at the University of Miami in Coral Gables, Florida and at Auburn University in Auburn, Alabama. She has consulted with numerous organizations and enjoys working with companies to improve their analytics capabilities.
Lisa LaVange, ASA President
ABSTRACT: Statistics is a critical component of data science – enough so to cause ASA to seriously consider accreditation of the data science discipline. And data science is, practically speaking, a skill set that statisticians are more and more in need of for today’s marketplace. I will make the case for statisticians to assume a leadership role in navigating alternative data sources and structures, selecting appropriate data mining and machine learning tools, and visualizing and interpreting the results of data mining efforts. I will draw from examples in the clinical research area to illustrate how statistical leaders are best able to understand and explain the uncertainty arising from the use of real-world data in novel applications, but the general principles apply to other research areas as well. Training statisticians to lead, formally and informally, is essential for the future of our profession, and I will offer my views on ways this can be accomplished, including highlighting the planned initiatives of the ASA Statistical Leadership Institute.
BIO: Lisa LaVange, PhD, is Professor and Associate Chair of the Department of Biostatistics in the Gillings School of Global Public Health at the University of North Carolina at Chapel Hill. She is also director of the department’s Collaborative Studies Coordinating Center (CSCC), overseeing faculty, staff, and students involved in large-scale clinical trials and epidemiological studies coordinated by the center. From 2011 to 2017, Dr. LaVange was director of the Office of Biostatistics in the United States Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER). There, she oversaw more than 200 statisticians and other staff members involved in the development and application of statistical methodology for drug regulation. She was a leader in developing and assessing the effectiveness and appropriateness of innovative statistical methods intended to accelerate the process from drug discovery to clinical trials to FDA approval and patients’ benefit, with a particular focus on rare diseases. Prior to her government and academic experience, she spent 16 years in non-profit research and 10 years in the pharmaceutical industry. Dr. LaVange is an elected fellow of the American Statistical Association (ASA) and is the 2018 ASA President.
Wine & Cheese Reception with SPES Special Panel Session (3:15-5:15PM)
Statistical Engineering; What Is It and Where Is It Going
The panel highlighted content shared at the September 2018 ENBIS meetings in Nancy, France.
Roger Hoerl (Union College)
ABSTRACT: Most problems discussed in statistics textbooks, journals, and conference sessions tend to be well-defined, fairly narrow in scope, and have a single “correct” analysis. In the words of Xiao-Li Meng, they “…correspond to a recognizable textbook chapter.” However, real problems faced by statisticians, and other professionals utilizing statistics, are often large, complex, and unstructured. For example, there may not be agreement on exactly what the real problem actually is. Further, these problems are usually too complex to be solved with one statistical method, and require a sequential approach integrating multiple methods in an overall problem solving strategy. If individual tools, no matter how powerful, are not effective at addressing such problems, what approaches should practitioners use? Can these approaches be studied, researched, and perfected over time? I argue that the discipline known as statistical engineering, which focuses on creative integration of multiple methods, is a viable approach for attacking such problems. It is perhaps the only viable approach. This will be illustrated with a large, complex, unstructured problem from GE Global Research. In addition, the current state of the theory and practice of statistical engineering will be presented.
William Brenneman (Procter and Gamble)
ABSTRACT: The skills to solve large unstructured problems in industry are critical to the success of many projects faced by practicing statisticians. Often these skills are learned on the job informally through observing experienced statisticians or semi-formal through mentoring and coaching. Statistical engineering provides a theory, framework and process to codify the experience of practicing statisticians and provides a mechanism for improving the process over time. I will provide some examples of statistical engineering and challenge the academic community to enhance their statistics curriculum to include statistical engineering methods so their graduates can have greater impact as practicing statisticians.
Geoff Vining (Virginia Tech)
ABSTRACT: There is a compelling need to create a new discipline, Statistical Engineering, to advance the theory and practice of solving large, complex, unstructured, problems. The issue is not the tools required; the basic and even advanced statistical/analytical methodologies are well developed. People in diverse disciplines are beginning to clearly understand the need for interdisciplinary teams and the fundamental issues regarding team dynamics, as well as other aspects from organizational psychology. For example, Lean Six Sigma clearly demonstrates the importance of proper project management in the context of problem solving. The “missing link” is how to put the tools to the most effective use, especially when multiple tools are needed, based on what we have learned from previous solutions to other large, unstructured, complex problems.
The proper model for this new discipline is chemical engineering and its systems approach for creating chemical processes based on the concept of “unit operations,” such as distillation, chemical reactor design, and heat transfer. Chemical engineering uses a systems approach to put the proper unit operations together in novel ways to build new chemical process, as well as improve existing processes, efficiently and effectively. Obviously, chemical engineering does not replace chemistry, but is rather complementary to it, figuring out how to best utilize the science of chemistry to develop large-scale chemical processes.
The statistical engineering analogs of unit operations are: data acquisition, data exploration, analysis/modeling, inference (back to the original problem), deployment of a tentative solution, and solution confirmation. The engineering challenge is how to put these tools together based on previous experience, and the unique nature of the problem at hand. Statistical engineering must combine the tools taught in university statistics curricula with the practical subject-matter knowledge based, and experience on previous successful solutions. The scientific method, properly understood, is an important key to success.
The International Statistical Engineering Association (ISEA) is a new global professional society dedicated to advancing the theory and practice of statistical engineering. The ISEA membership model is based on ENBIS. We are always looking for other people who share our vision and passion for this new discipline.