Professor; David Eccles Faculty Scholar; Francis A. Madsen Scholar
Dr. Don G. Wardell is Francis A. Madsen Scholar and Professor of Operations and Information Systems (OIS) at the University of Utah David Eccles School of Business.
He received BS and MS degrees in Metallurgical Engineering from the U, and a Ph.D. from Purdue University’s Krannert Graduate School of Management.
Wardell has taught at both the undergraduate and graduate levels, including teaching classes in Spanish at INCAE in Costa Rica. He has been honored with the University of Utah’s Calvin S. and JeNeal N. Hatch Prize in Teaching, the U’s Distinguished Teaching Award, the Eccles School’s Masters Teaching Excellence Award, the Brady Superior Teaching Award, and the Marvin J. Ashton Award for Excellence in Undergraduate Teaching.
His research interests are mainly in the area of quality management, and especially statistical process control. He has also done recent work in service operations management. He has served as an associate editor for Technometrics and as a member of the editorial review boards of Production and Operations Management and IIE Transactions on Quality and Reliability.
Most importantly, he is happily married and a proud father of four and grandfather of six (as of June 2020).
Ph.D., Management Science, Purdue University. Project: Control Charts in the Presence of Autocorrelation, 1990
MS, Metallurgical Engineering, University of Utah. Project: Estimation of Media Wear in Semi-Autogenous Grinding (SAG) Mills, 1987
BS, Metallurgical Engineering, University of Utah, 1985
The majority of the research that I pursue falls under the broad umbrella of quality management, with emphasis on statistical process control (SPC). Much of my work has questioned some of the traditional assumptions of SPC and what should be done if those assumptions are not met. I investigate the underlying probability distributions of statistics plotted on SPC charts, including the derivation of new probability distributions and/or their first and second moments.
OIS 4650 – Principles of Quality Management
Introduction to the principles of quality management, with an emphasis on cross- functional problem solving. Topics include customer driven quality, leadership, employee participation and training, continuous process improvement, design quality and error prevention, management by fact, and strategic quality planning.
OIS 6041 – Data Analysis and Decision Making II
This course is a continuation of Data Analysis and Decision Making I. Course topics will include advanced regression, simulation, Bayes theorem and the value of information in decision analysis.
OIS 6040 – Data Analysis and Decision Making I
This course will develop decision making abilities with data-analysis and decision models. Applications will be in the business functional areas. Students will use computers to solve business problems. Course topics will include advanced statistical analysis, regression models, decision analysis basics, and portfolio management.
OIS 6140 – Statistics for Executive MBAs
Statistics provides an overview of basic statistical concepts and methods for managers. The emphasis is on understanding the concepts and their application to the real world business data. The conceptual material focuses on the importance of statistical thinking to make sound business decisions. The statistical methods are implemented using a computer to analyze business and economic data sets, with emphasis on interpreting the output. Topics covered include descriptive statistics (how to organize data and display it graphically), probability theory, distributions (empirical, mathematical and sampling), statistical inference (hypothesis testing), and the study of relationships (regression and correlation).
OIS 6420 – Quality Management
Introduction to the principles of quality management, with an emphasis on cross-functional problem solving. Topics include system design to control the quality of products and services, customer driven quality, leadership, employee participation and training, and strategic quality planning.
OIS 6610 – Practical Management Science
This course takes a practical approach to management science by using popular business software (e.g., Microsoft Excel) to solve analytical models. Management-decision problems covered in the course may include linear and integer programming, goal programming, nonlinear programming, transportation models, specialized network models, inventory models, critical-path method/project management networks, queuing theory. Where applicable, the course will build on topics at a more advanced level than models covered in OIS 6040 and 6041, Data Analysis and Decision Making. Tools taught in this class are applicable in finance, marketing, operations management, information systems and more.
Data Analysis And Decision Making
Quality Management and Six Sigma
As a professor at the University of Utah, I am interested in teaching students to develop critical and creative thinking. As a professor of management, I am involved in teaching students how to solve problems and make better decisions. This is especially true in my case, since I teach classes in statistics, quality management and problem solving. Decision making and problem solving in a business environment can be very complex. Uninformed managers can make poor decisions that affect the lives of many individuals. Therefore, my most important goal as a professor of management is to teach students to be informed and effective decision makers.
In order to effectively teach these technical classes, I need to stay current on business literature and research. Technology is bringing more challenges. Computers make it possible to collect and analyze billions of pieces of information. Telecommunication is revolutionizing the way we do business. I must read of these advances, and I must participate in my own basic and applied research. I strongly believe that scholarly research and teaching effectiveness are intertwined and inseparable.
I feel very strongly that to be an effective teacher, I need to treat individual students with respect. I must attempt to learn each student’s name, and his or her strengths and weaknesses. I must try to accommodate questions at any time, not just during class and office hours. I also believe that I can sacrifice the syllabus and schedule if students are not understanding. My job is not to show them what I know, but to teach them what they need to know, and more importantly to facilitate their learning.
Finally, I hope to be able to instill in students a love of learning. I remember a university math class which I attended as an undergraduate student. The professor had just finished an elegant proof of a very useful concept. I could tell that he was excited about the problem and its solution. As he finished, he asked if there were any questions. One student raised his hand and asked, “will this be on the exam?” The light in the professor’s eyes went out immediately. I hope to teach my students that school is more than just exams and grades. I hope that the real value in their education is not found in their grade point average or their resume, but in the knowledge that they take away.
Liana Victorino & RohitVerma (2013). Script Usage in Standardized and Customized Service Encounters: Implications for Perceived Service Quality. Production and Operations Management. Vol. 26, 518–534. Discipline based – refereed, Published, 06/2013.
Liana Victorino, Rohit Verma & Bryan Bonner (2012). Can customers detect script usage in service operations? An experimental video analysis. Journal of Service Research. Vol. 15, 390-400. Discipline based – refereed, Published, 10/25/2012.
Wardell, D., & David Ding, Paul Hu and Rohit Verma. (2010). The Impact of Service System Design and Flow Experience on Customer Satisfaction in Online Financial Services. Journal of Service Research. 13, 96-110. Discipline based – refereed, Published, 02/2010.
Wardell, D., & Liana Victorino, Rohit Verma. (2008). Service Scripting: A Customer’s Perspective of Quality and Performance. Cornell Center for Hospitality Research. 8 No. 20, 4-13. Practice – refereed, Published, 11/2008.
Wardell, D.(2007). Autocorrelated Data. (pp. 145-150). Encyclopedia of Statistics in Quality and Reliability. Discipline based – other, Published, 2007.
Wardell, D., & Weiyu Tsai. (2006). An Interactive Excel VBA Example for Teaching Statistics Concepts. INFORMS Transactions on Education. 7. Teaching – refereed, Published, 07/2006.
Wardell, D., & Xin Ding and Rohit Verma. (2006). An Assessment of Statistical Process Control-Based Approaches for Charting Student Evaluation Scores. Decision Sciences Journal of Innovative Education. 4, 259-272. Teaching – refereed, Published, 07/2006.
Wardell, D., & Weiyu Tsai. (2006). Creating Individualized Data Sets for Student Exercises Using Microsoft Excel and Visual Basic. INFORMS Transactions on Education. 7. Teaching – refereed, Published, 2006.
Wardell, D., & Chesteen, S., Helgheim, B., and Randall, T. (2005). Comparing quality of care in non-profit and for-profit nursing homes: A process perspective. Journal of Operations Management. 23, 229-242. Discipline based – refereed, Published, 02/2005.
Wardell, D., & Pullman, M. and Moore, W. (2002). A Comparison of Quality Function Deployment and Conjoint Analysis In New Product Design. Journal of Product Innovation Management. 19, 354-364. Discipline based – refereed, Published, 2002.
Wardell, D.(2001). ‘Reply’ to Comment by R. L. Berger and B. W. Coutant. (pp. 85). The American Statistician. Discipline based – other, Published, 2001.
Wardell, D., & Goodale, J. C. (2000). Service level and routing in M/M/s systems. (pp. 169-171). Proceedings of the 2000 Annual Meeting of the Midwest Decision Sciences Institute. Other, 2000.
Wardell, D.(1997). Small Sample Interval Estimation of Bernoulli and Poisson Parameters. The American Statistician. 51, 321-325. Discipline based – refereed, Published, 1997.
Wardell, D., & M. R. Candia. (1996). Statistical Process Monitoring of Customer Satisfaction Survey Data. Quality Management Journal. 3, 36-50. Discipline based – refereed, Published, 1996.
Wardell, D., & H. Moskowitz and R. D. Plante. (1995). ‘Response’ to Yashchin Letter to the Editor. (pp. 243-245). Technometrics. Discipline based – other, Published, 1995.
Wardell, D., & H. Moskowitz and R. D. Plante. (1994). Rejoinder to Discussions by Adams, Woodall and Superville, Fellner, and Lucas of ‘Run Length Distributions of Special-Cause Control Charts for Correlated Processes’. (pp. 23-27). Technometrics. Discipline based – other, Published, 1994.
Wardell, D., & H. Moskowitz and R. D. Plante. (1994). The Use of Run Length Distributions of Statistical Process Control Charts to Detect False Alarms. Production and Operations Management. 3, 217-239. Discipline based – refereed, Published, 1994.
Wardell, D., & H. Moskowitz and R. D. Plante. (1994). Run Length Distributions of Special-Cause Control Charts for Correlated Processes. Technometrics. 36, 3-17. Discipline based – refereed, Published, 1994.
Wardell, D., & H. Moskowitz and R.D. Plante. (1994). Run Length Distributions of Residual Control Charts for Autocorrelated Processes. Journal of Quality Technology. 26, 308-317. Discipline based – refereed, Published, 1994.
Wardell, D., & H. Moskowitz and R.D. Plante. (1992). Control Charts in the Presence of Data Correlation. Management Science. 38, 1084-1105. Discipline based – refereed, Published, 1992.
American Statistical Association (ASA), Member, 04/01/2001 – present
American Society for Quality (ASQ), Senior Member, 10/18/1990 – present
Decision Sciences Institute (DSI), Member, 1990 – present
Production and Operations Management Society (POMS), Member, 1990 – present
Institute for Operations Research and the Management Sciences (INFORMS), Member, 1988 – present
Phi Beta Kappa Honor Society , Member, 1985 – present
Most Influential Paper Award. Production and Operations Management Society’s College of Service Operations, 05/2015
Brady Superior Teaching Award. David Eccles School of Business, 04/2015
Francis A. Madsen Scholar. David Eccles School of Business, 01/2015
Masters Teaching Award. David Eccles School of Business, 04/2012
Student Choice Award. “Every year, students nominate outstanding professors from various departments and colleges on campus to acknowledge their excellence. Out of many nominated professors, U have been selected to celebrate your achievements with a night of tribute and dinner at an awards ceremony.”. ASUU, 03/28/2012
David Eccles Faculty Scholar. David Eccles School of Business, 2008
Distinguished Teaching Award. University of Utah, 2007
Center for Disability Services Teaching Recognition (Certificate says “for distinguished and exemplary service to students with disabilities.). Center for Disability Services, University of Utah, 2007
Masters Teaching Excellence Award. David Eccles School of Business, 2006
Masters Teaching Excellence Award. David Eccles School of Business, University of Utah, 2002
Best Paper Award. Midwest Decision Sciences Institute Meeting, 2000
Brady Superior Teaching Award. David Eccles School of Business, University of Utah, 1999
Marvin J. Ashton Award for Excellence in Undergraduate Teaching. David Eccles School of Business, 1997
Dissertation competition winner. Production and Operations Management Society, 1991
Honorable Mention, Dissertation competition. Decision Sciences Institute, 1991