About The Author
David R. Anderson
Dr. David Anderson is a leading author and Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati (USA). He has served as Associate Dean of the College of Business Administration and also as head of the Department of Quantitative Analysis and Operations Management and He was also coordinator of the college's first Executive Program.
In addition to introductory statistics for business students, David has taught graduate-level courses in multivariate analysis, regression analysis and management science. He also has taught statistical courses at the Department of Labor in Washington, D.C. He has received numerous honors for excellence in teaching and service to student organizations. Dr. Anderson is the co-author of 10+ textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, he earned his B.S., M.S. and Ph.D. degrees from Purdue University.
Dennis J. Sweeney
Dr. Dennis J. Sweeney (late) was Professor Emeritus of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned a BSBA degree from Drake University and his DBA and MBA degrees from Indiana University, where he was an NDEA Fellow. Dr. Sweeney worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Dennis also served as Head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration at the University of Cincinnati. He has published more than 30 articles and monographs in the area of management science and statistics. Sweeney has co-authored 10 textbooks in the areas of management science, statistics, linear programming, and production and operations management.
James J. Cochran
Dr. James J. Cochran is Professor of Applied Statistics and the Rogers-Spivey Faculty Fellow, and Associate Dean for Research, at The University of Alabama. Born in Dayton, Ohio, he got his B.S., M.S., and M.B.A. from Wright State University and his Ph.D. from the University of Cincinnati. Cochran has been at The University of Alabama since 2014 and has been a visiting scholar at Universidad de Talca, the University of South Africa, Stanford University, , and Pole Universitaire Leonard de Vinci. James has published more than 40 papers in the development and application of operations research and statistical methods. He has published in several journals, including The American Statistician, Management Science, Communications in Statistics Theory and Methods, Journal of Combinatorial Optimization, European Journal of Operational Research. He got the 2008 INFORMS Prize for the Teaching of Operations Research Practice, 2010 Mu Sigma Rho Statistical Education Award, and 2016 Waller Distinguished Teaching Career Award from the American Statistical Association.
He was elected to the International Statistics Institute in 2005, was named a Fellow of the American Statistical Association in 2011 and was named a Fellow of INFORMS in 2017. Dr. Cochran received the Founders Award in 2014, the Karl E. Peace Award in 2015 from the American Statistical Association, and the INFORMS President's Award in 2019.
Jeffrey D. Camm
Professor Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Business Analytics in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, Dr. Camm was on the faculty of the University of Cincinnati. He has also been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College.
Jeff has published over forty papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in Science, Operations Research, Management Science, Interfaces, and other professional journals. He was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice.
Jeffrey Ohlmann
Dr. Jeffrey W. Ohlmann is an Associate Professor of Business Analytics and a Huneke Research Fellow in the Tippie College of Business at the University of Iowa. He got a B.S. from the University of Nebraska and earned his M.S. and Ph.D. degrees from the University of Michigan. He has been teaching at the University of Iowa since 2003.
His research on the solution and modeling of decision-making problems has produced more than two dozen research papers in journals such as Operations Research, INFORMS Journal on Computing, Mathematics of Operations Research, Transportation Science, and the European Journal of Operational Research. Professor Ohlmann has collaborated with organizations such as LeanCor, Cargill, Transfreight, the Hamilton County Board of Elections, and three National Football League franchises. Because of the relevance of his work to industry, he was bestowed the George Dantzig Dissertation Award and was recognized as a finalist for the Daniel Wagner Prize for Excellence in Operations Research Practice.
Michael J. Fry
Dr. Michael J. Fry is the Academic Director of the Center for Business Analytics in the Carl H. Lindner College of Business and a Professor of Operations, Business Analytics, and Information Systems (OBAIS) and at the University of Cincinnati. He got a B.S. from Texas A&M University, and M.S.E. and Ph.D. degrees from the University of Michigan. He has been at the University of Cincinnati since 2002, where he served as Department Head from 2014 to 2018, and has been named a Lindner Research Fellow. He has also been a visiting professor at the University of British Columbia and Cornell University.
Dr. Fry has published more than 25 research papers in journals such as Manufacturing & Service Operations Management, Operations Research, Naval Research Logistics, IIE Transactions, Transportation Science, Critical Care Medicine, and INFORMS Journal of Applied Analytics. He serves on editorial boards for journals such as INFORMS Journal of Applied Analytics (formerly Interfaces), Production and Operations Management, and Journal of Quantitative Analysis in Sports.
Professor Fry's research interests are in applying analytics to the areas of sports, supply chain management, and public policy operations. He has worked with many different organizations for his research, including Starbucks Coffee Company, Great American Insurance Group, Dell, Inc., the State of Ohio Election Commission, the Cincinnati Fire Department, the Cincinnati Bengals, and the Cincinnati Zoo & Botanical Gardens. In 2008, he was named a finalist for the Wagner Prize for Excellence in Operations Research Practice and he has been recognized for both his teaching and research excellence at the University of Cincinnati. In 2019, he led the team that was awarded the INFORMS UPS George D. Smith Prize on behalf of the OBAIS Department at the University of Cincinnati.
Thomas A. Williams
Dr. Thomas A. Williams is a very well-respected author and Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology, where he was the first chairman of the Decision Sciences Department. He has taught courses in statistics and management sciences, as well as graduate courses in regression and decision analysis. Before joining the College of Business at RIT, Tom served for 7 years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the undergraduate program in Information Systems and served as its coordinator. The co-author of 11 leading textbooks in the areas of management science, production and operations management, statistics, and mathematics, Professor Williams has been a consultant for numerous Fortune 500 companies and has worked on projects ranging from the use of data analysis to the development of large-scale regression models.
