Douglas C. Montgomery

Douglas C. Montgomery is Regent's Professor of Industrial Engineering, and ASU Foundation Professor of Engineering at Arizona State University. He was the John M. Fluke Distinguished Professor of Engineering, Director of Industrial Engineering and Professor of Mechanical Engineering at the University of Washington in Seattle. He was a Professor of Industrial and Systems Engineering at the Georgia Institute of Technology. He holds BSIE, MS and Ph.D. degrees from Virginia Tech. He has industrial experience with Union Carbide Corporation and Eli Lilly and Company and extensive consulting experience. Dr. Montgomery's professional interests focus on industrial statistics, including design of experiments, quality and reliability engineering, applications of linear models, and time series analysis and forecasting. The Office of Naval Research, the National Science Foundation, NASA, the Department of Defense, and private industry have sponsored his research. He has supervised 69 doctoral dissertations and over 40 MS theses and MS Statistics Projects. Dr. Montgomery is an author of thirteen books that have been published in over 50 English language editions, including Design and Analysis of Experiments, 10th edition (2020), and Response Surface Methodology, 4th edition (2016, with R. H. Myers and C.M. Anderson-Cook). He is an author of over 275 archival journal papers. He is currently one of the Chief Editors of Quality and Reliability Engineering International and is a former Editor of the Journal of Quality Technology. He is an Honorary Member of the American Society for Quality, a Fellow of the American Statistical Association, a Fellow of the Royal Statistical Society, a Fellow of the Institute of Industrial Engineers, an Elected Member of the International Statistical Institute and an Academician of the International Association for Quality. His recognition awards include the Shewhart Medal, the Distinguished Service Medal, the William G. Hunter Award, the Brumbaugh Award, the Lloyd S. Nelson Award, and the Shewell Award (twice) from the American Society for Quality, the Deming Lecture Award from the American Statistical Association, the George Box Medal from ENBIS (European Network for Business and Industrial Statistics), the Greenfield Medal from the Royal Statistical Society and the Ellis R. Ott Award. He was named an ASU Outstanding Doctoral Mentor in 2004 and a member of the team that received the ASU President’s Award for Innovation in 2015.

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Response Surfaces, Mixtures, and Model Building (Coursera) Coursera
Arizona State University

Response Surfaces, Mixtures, and Model Building (Coursera)

Dive into the world of optimization and process improvement with our 'Response Surfaces, Mixtures, and Model Building' course. This course is designed for professionals looking to enhance their skills in identifying key factors affecting a system's output and optimizing those factors to achieve the best possible results. Through factorial experiments and response surface methodology, you'll learn how to screen important factors, build predictive models, and ultimately drive process optimization.

Jun 15th 2026
4 Weeks
Experimental Design Basics (Coursera) Coursera
Arizona State University

Experimental Design Basics (Coursera)

Discover the essential principles of Experimental Design Basics with our beginner-friendly online course. Designed for those new to the field, this course will equip you with the knowledge and tools needed to conduct effective experiments and interpret results correctly. Learn from industry experts as you explore planning, execution, and analysis techniques using practical examples and software applications like JMP, Design-Expert, and Minitab.

Jun 15th 2026
5-12 Weeks
Factorial and Fractional Factorial Designs (Coursera) Coursera
Arizona State University

Factorial and Fractional Factorial Designs (Coursera)

Explore the world of multifactor experiments with our introductory course on Factorial and Fractional Factorial Designs. Designed for those in engineering, science, and business, this course teaches you how to effectively design experiments where multiple factors are varied simultaneously. Master the art of analyzing data using ANOVA and unlock the potential to optimize processes and drive innovation.

Jun 15th 2026
4 Weeks
Random Models, Nested and Split-plot Designs (Coursera) Coursera
Arizona State University

Random Models, Nested and Split-plot Designs (Coursera)

Dive into the world of Random Models, Nested and Split-Plot Designs with our expert-led online course. Gain a deep understanding of designing and analyzing experiments where factors are chosen at random, including nested factor studies and split-plot designs. Learn modern methods for estimating variability components crucial for assessing measurement systems' capability.

Jun 15th 2026
3 Weeks
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