ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Statistical Engineering: A Glimpse into the Future

3 September 2018, 12:00 – 12:30

Abstract

Submitted by
Geoff Vining
Authors
Geoff Vining (Virginia Tech Statistics Department)
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.
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