Analytics Process

Peter Bell

Ivey Business School at Western University

Creating Competitive Advantage using Analytics

Many claim that the use of analytics can create a competitive advantage but research suggests that any advantage created is often fleeting: most analytics is easily replicated. Further, the evolving nature of analytics providers is shortening the time that the first mover has to enjoy any advantage it might create from an analytics investment. Research tracking major applications over long periods of time, however, can find examples of firms that have created advantage through analytics and in some cases have sustained a competitive advantage created using analytics for years, even decades. How have these firms done this? What are the lessons for analytics practitioners and leaders? In addition, this presentation will discuss evolving trends in the business of analytics and provide examples that illustrate how firms might compete effectively using analytics. References: “Creating competitive advantage using big data” Ivey Business Journal, May/June 2013. “Structuring the analytic BPO relationship: Sending your analytics offshore can be a winning strategy, but only if it’s carefully managed” (with D. Fogarty), MIT Sloan Management Review, 55, 2. Winter 2014, pp. 40-45. “Sustaining an analytics advantage” MIT Sloan Management Review, 56, 3. Spring 2015, pp. 21-24


Peter Bell earned degrees from Oxford University and the University of Chicago. He is an INFORMS Fellow, and a past winner of the INFORMS prize for teaching the practice of Management Science. He chaired the 2013 and 2014 Franz Edelman Award Competitions and is the immediate Past-President the INFORMS Section on Practice. He has authored or co-authored more than 100 articles in academic and business journals and 17 books including Analytics for Managers (with Greg Zaric), Routledge Publishing, New York (2012). He was founding Editor-in-Chief of the scholarly journal International Transactions in Operational Research 1993-2000 and is an Associate Editor of Operations Research, and Interfaces. He is a Past President of the Canadian Operational Research Society and was 1995-97 President of the International Federation of Operational Research Societies. He has served as a consultant to corporations, hospitals, small businesses, lawyers, charities, and government agencies, and serves on a number of Boards of Directors.

Michael Bloem

Senior Analyst, Advanced Analytics Team
Steelcase, Inc.

Proving and Improving Steelcase’s Workplace Insights with the Internet-of-things, Digital Exhaust, and Experimentation

Steelcase Inc. offers a comprehensive portfolio of architecture, furniture, and technology products and services designed to unlock human promise and support social, economic, and environmental sustainability. Steelcase products and services are inspired by more than 100 years of insight gained while serving the world’s leading organizations. These insights guide product and service development, which is how Steelcase differentiates its offerings in a competitive market. Historically, Steelcase’s insights have primarily been gleaned through qualitative user-centered design techniques, such as observations, interviews, workshops, and prototypes. Today, Steelcase is exploring approaches that also leverage the Internet-of-Things (IoT), digital exhaust, and analytics to prove and improve its insights, as well as to enable new products and services.
Workplace experiments are one such new approach that Steelcase is exploring for proving and improving its insights. Steelcase recently designed and conducted more than 20 experiments within its own workplaces in Grand Rapids, MI. These experiments tested hypotheses related to worker preferences for spaces with different amenities and how workspaces impact behaviors. For example, we tested whether employees in different demographics tended to select an enclave with a desk and task seat over an enclave with a lounge seat and whether employees stood more often while working in an enclave with standing-height seating.
We will review the design of these experiments, the IoT sensors and digital exhaust we used to collect data, and the statistical methods we used to execute the hypothesis tests. Details from one experiment will be provided. We will also describe how we combined experiment data and analysis with qualitative information to gain deeper insights and identify new opportunities for further investigation. Finally, we will discuss our next steps, which include communication of these early results, actions we are taking based on the results, and some institutional and infrastructure capabilities we are developing to support further experimentation.


Michael Bloem is a Senior Analyst on the Advanced Analytics team at Steelcase. He develops and architects analytics solutions that leverage data from workplace sensors and other sources to help leading organizations maximize human and spatial potential. Prior to working at Steelcase, Michael spent eight years researching applications and extensions of control theory and operations research techniques to air traffic management while working at NASA Ames Research Center and studying at Stanford University, where he earned a PhD in Operations Research in 2015.

John Celona

Decision Analysis Associates LLC

Winning at Litigation through Decision Analysis at Stanford University Medical Center

Ron Howard describes decision analysis as a conversation. Decision analysis also works as a conversation between two thinking modes: your Type 1 intuitive thinking supplies the frame (alternatives, information and preferences) and your Type 2 linear, logical thinking analyzes it. The results feed back to intuitive evaluation for synthesis, revision and insight. This conversation functions both internally in a person and between multiple people.
We show how this approach to decision analysis was applied at Stanford University Medical Center to analyze all cases and claims. This approach enabled analysts and people not trained in decision analysis to conduct analyses together effectively and in real time. The results are quantifiably better than prior results with actuarial methods.


John Celona has over three decades of experience as a management consultant in decision analysis and strategy development in industries ranging from A (automobiles) to Z (zinc mines). He is the President and Founder of Decision Analysis Associates, LLC. John teaches Enterprise Risk Management and Healthcare Risk Management in the Stanford Center for Professional Development and is a guest lecturer for the decision analysis courses in the School of Engineering at Stanford University. In addition, he is on faculty for the Academy of the American Society for Healthcare Risk Management (ASHRM) and American Course in Drug Development and Regulatory Sciences. John is co-author of Decision Analysis for the Professional, a textbook first published in 1986, now in its fourth edition and in use at Stanford University and around the world. His newest book, Winning at Decision Litigation through Decision Analysis was published in 2016 as part of the Springer Series in Operations Research and Financial Engineering.

Isaac Wagner

Director of Strategy Analytics
Memorial Sloan Kettering Cancer Center

Building Analytics Teams at Memorial Sloan Kettering

The Strategy Analytics team (within the Department of Strategy and Innovation) leverages the power of data and analytics to shape strategic decisions at Memorial Sloan Kettering Cancer Center, a world renowned organization dedicated to the progressive control and cure of cancer through programs of patient care, research, and education.
Over the past 12 years, we have applied our expertise in quantitative methodologies to investigate and improve operations, create long-term strategies for the hospital’s growth, and redefine models for care delivery. We were awarded the 2012 INFORMS Prize for “repeatedly applying the principles of advanced analytics and operations research / management science in pioneering, varied, novel, and lasting ways.”
We focus on solving problems that will measurably impact the lives of our patients and staff. Our team has close relationships with senior executives, clinicians, and front-line staff, with whom we work closely to make the results of our analyses practical and usable.
Through our experience, we have experimented with many types of team structures. This talk focuses on different types of analytical team structures within a large healthcare organization and the pros and cons of their implementation depending on the context.


Isaac serves as Director of Strategy Analytics at Memorial Sloan Kettering Cancer Center, in New York City. He leads a team of 12 analysts charged with advancing the hospital’s long-term mission through analytics, data science, operations research, and software engineering. Strategy Analytics won the 2012 INFORMS Prize for “repeatedly applying” these methods “in pioneering, varied, novel, and lasting ways.” Isaac has a MS in mathematics from NYU, has published on comparative effectiveness and cost research, and is passionate about using data to re-imagine the delivery of cancer care.

Denise White

Assistant Professor-Educator & Affiliated Assistant Professor
University of Cincinnati & Cincinnati Children’s Hospital Medical Center

Imagining Predictive Analytics In Health Care

The influx of electronic medical records in healthcare has opened up new opportunities for analytics, especially in the area of prediction. However, many health care organizations do not recognize the potential influence that this data can have on operations and outcomes. Nationally, we see an increase in the focus on performance reporting with the government even imposing penalties on those who do not perform well. This step forward provides an increase in knowledge of where opportunities exist for improvement. However, most health care organizations do not quickly recognize that using predictive analytics as part of the solution can improve their outcomes, performance, and results. It is critical that we understand the actions needed to gain buy-in to better utilize the potential provided with prediction in health care. We will explore the use of predictive analytics in the hospital environment including the tools and processes used for modeling. We will look at examples where Predictive Analytics Methods like Simulation Modeling, Forecasting, and Decision Trees have been applied. We will also discuss the challenges associated with expanding analytic capabilities from historic look-backs to forward-looking recommendations including the integration of business intelligence into the solutions. In addition, challenges and recommendations for gaining adoption throughout the organization will also be addressed.


Denise L. White, PhD, MBA is an Assistant Professor – Educator at the University of Cincinnati in the Carl H. Lindner College of Business and an Affiliated Assistant Professor at Cincinnati Children’s Hospital Medical Center. She is a member of the Operations, Business Analytics and Information Systems (OBAIS) Department where her educational focus is in Operations Management and Business Analytics. Prior to joining the OBAIS Department, Dr. White was the Director of Quality & Transformation Analytics in the James M. Anderson Center for Health Systems Excellence at Cincinnati Children’s Hospital Medical Center. In this position, she managed the analytic team supporting quality and transformation analytics across the hospital. Dr. White integrates the knowledge and experience she gained in this role into the classroom and her research interests. Her primary research interests lie in the area of capacity management, hospital flow, scheduling, and advanced analytics. Dr. White is a graduate from the University of Cincinnati’s College of Business where she received her PhD in Operations Management with a focus on Healthcare Operations. She holds a B.S degree in Mathematics and Computer Science along with an MBA.