Topic: A Statistical Engineering Approach to Problem-Solving

Speaker: Dr. Roger W. Hoerl

Date: Tuesday April 21, 2026 at 8 am PST /11 am EST / 5pm Central European Time

Abstract

The design, monitoring, improvement, and control of processes of all types creates a continual flow of problems that must be solved for processes to perform as designed, and effectively and efficiently serve customers. As a result, various types of problems arise, and numerous problem-solving methods have been developed to address these problems. Using the principles of statistical engineering, we develop a framework that maps problem types to problem-solving strategies. The proposed framework introduces a structured decision logic based on several dimensions: the fundamental intent of intervention (fixing, improving, or creating), whether the solution direction is known or must be discovered, the degree of complexity, whether process requirements are fixed or fluid, and the availability of sufficient problem-relevant data. The framework is designed to help practitioners choose the most effective problem-solving methodology for each unique challenge. This work emphasizes that the problem and its characteristics should drive the selection of tools, not the other way around. The framework is illustrated using real problems from the authors' collective experience. This talk is based on a recent publication of the same name in the journal Quality Engineering.

Speaker Bio

Dr. Roger W. Hoerl is Brate-Peschel Professor of Statistics at Union College, in Schenectady, NY. Previously, he led the Applied Statistics Lab at GE Global Research. Dr. Hoerl has been named a Fellow of the American Statistical Association and the American Society for Quality, and has been elected to the International Statistical Institute and International Academy for Quality. He has received the Brumbaugh and Hunter Awards, as well as the Shewhart Medal, from the American Society for Quality, and the Founders Award and Deming Lectureship Award from the American Statistical Association. His article “AI and Statistics: Perfect Together,” with Tom Redman, was included in the “10 AI Must-Reads for 2024” by Sloan Management Review.