Professor Steiner's research interests cover the broad area of business and industrial statistics focusing on process improvement. The overall goal of his research is the development of innovative ways to use process data and statistical methods to drive process improvement and variation reduction.One major project was the development of an algorithm for variation reduction called the Statistical Engineering algorithm. This step-by-step algorithm was designed to help practitioners solve chronic problems in existing high- to medium-volume manufacturing and assembly processes. The algorithm has some unique features, including focus on a dominant cause, explicit use of the baseline data to help plan further investigations, a new measurement system assessment plan, explicit consideration of the seven variation reduction approaches, use of the method of elimination to search for a dominant cause, and an emphasis on observational studies rather than experimental studies. The algorithm is described in detail in the Statistical Engineering book by Steiner and MacKay (2005).
Professor Steiner has also developed methods for monitoring medical/surgical outcomes. The rapid detection of deterioration in surgical performance is critical since it will result in prompt investigation of the cause and possible procedural changes to improve performance. In this context, risk adjustment is necessary since patients can be very heterogeneous. Professor Steiner has developed a risk-adjusted cumulative sum (CUSUM) monitoring procedure using a likelihood score based on an assessment of risk prior to surgery. This methodology has been widely applied to a variety of situations such as monitoring cardiac surgery outcomes.
Professor Steiner is also an active researcher in the important area of measurement system assessment. Good measurement systems are essential in all areas of empirical inquiry. For example, in industry, measurements systems are needed both to protect customers and to drive process understanding and improvement. Professor Steiner has developed novel measurement assessment plans and analysis for both continuous and binary responses that greatly improve on existing procedures. The new plans include ideas such as incorporating baseline data, conditional sampling, targeted verification and two-phase assessment.
Professor Steiner joined the Department of Statistics and Actuarial Science at the University of Waterloo in 1995, after receiving a PhD in Management Science from McMaster University.
Prof. Steiner was the chair of the Department of Statistics and Actuarial Science at the University of Waterloo for 8 years, from July 2014 to June 2022. Previously he also served for 6 years as the department’s Associate Chair for Undergraduate Studies.
From 2004-2020 Professor Steiner was the director of the Business and Industrial Statistics Research Group (BISRG) at the University of Waterloo. From 2003-04 he was the director of the predecessor of BISRG, namely the Institute for Improvement in Quality and Productivity. The mandate of BISRG is to encourage the flow of knowledge, ideas, methods, and problems between the university and business communities to their mutual benefit. As a business/industrial consultant, Professor Steiner has worked together with numerous organizations, including Atlantis Aerospace, Bendall Automotive, Blackberry, Chrysler, ComDev, Dana, Eaton Yale, Fisher and Paykel, Katlyn, General Motors Canada, Ford Motor Company, Nortel Networks, Open Text, PetroCanada, Precision Plastics, Seagrams, Toyota, U.S. Army, US Federal Food and Drug Agency, Wescast, Woodbridge Foam, and the State of Utah, and the Counties of Tooele, Utah, and Salt Lake. The consulting projects involved statistical process control/monitoring, design of experiments, quality improvement, data analysis, data science, statistical engineering and process re-engineering.
Professor Steiner is also a past chair of the International Statistical Engineering Association, a global society dedicated to advancing the emerging discipline of statistical engineering. Statistical engineering is a new discipline defined as “the study of systematic integration of statistical concepts, methods, and tools, often with other relevant disciplines, to solve important problems sustainably.”
De Mast J, Steiner SH, Nuijten WPM, Kapitan D (2022), “Analytical Problem Solving based on Causal, Correlational and Deductive Models, The American Statistician, to appear
Winlaw M, Steiner SH, Mackay RJ and Hilal AR (2019), “Using Telematics Data to Find Risky Driver Behaviour,” Accident Analysis & Prevention, 131, 131-136.
- Stevens NT, Steiner SH and MacKay RJ (2017) “Assessing Agreement Between two Measurement Systems: An Alternative to the Limits of Agreement Approach (PDF)," Statistical Methods in Medical Research, 26, 2487-2504.
- Jones-Farmer A, Woodall W, Steiner SH and Champ CW (2014), “An Overview of Phase I Analysis for Process Improvement and Monitoring (PDF),” Journal of Quality Technology, 46, 265-280. (winner of the Statistics Division of the American Society for Quality’s Lloyd Nelson Award)
- Browne R, Steiner SH, MacKay RJ (2010) “,” Technometrics, 52, 294-302 (winner of the American Statistical Association’s W.J. Youden Award in Interlaboratory Testing).
- Steiner SH and Jones M (2010) “Risk Adjusted Survival Time Monitoring with an Updating Exponentially Weighted Moving Average (EWMA) Control Chart (PDF)” Statistics in Medicine, 29, 444-454.
- Steiner SH, MacKay RJ and Ramberg JS (2008). “An Overview of the Shainin SystemTM for Quality Improvement (PDF),” with discussion Quality Engineering, 20:1, 6–19.
- Steiner SH and MacKay RJ (2004) “Scale Counting (PDF)”, Technometrics, 46, 348-354. (winner of the American Society for Quality’s Wilcoxon Prize for the best practical applications paper).
- Steiner SH, Cook R, Farewell V and Treasure T (2000), “Monitoring Paired Binary Surgical Outcomes Using Cumulative Sum Charts (PDF),” Biostatistics, 1, 441-452.
- Steiner SH (1999), “EWMA Control Charts with Time-Varying Control Limits and Fast Initial Response (PDF),” Journal of Quality Technology, 31, 75-86.