News Release

Predictive analytics pays off with complementary investments

Study shows nearly $1 million productivity bump for some manufacturers

Peer-Reviewed Publication

University of Toronto, Rotman School of Management

Prof. Kristina McElheran

image: Kristina McElheran is an assistant Professor of Strategic Management at the University of Toronto, Scarborough and Rotman School of Management. Her research centres on the use of information technology and data by firms, with an emphasis on strategy, organizational design, and process innovation. Her current focus is on data-driven decision making and how firms and individuals can use data to improve their performance. She is also actively investigating the economic and strategic impacts of Cloud Computing. Her experience includes six years on faculty at the Harvard Business School. She is a Faculty Affiliate at UofT’s Schwartz Reisman Institute for Technology and Society; Digital Fellow at the Digital Economy Lab, Stanford Institute for Human-Centered AI; Visiting Researcher at Harvard Law School on AI, Robotics, and the Future of Work; Fellow at Boston University’s Technology and Policy Research Initiative; and Digital Fellow at MIT’s Initiative on the Digital Economy. Prior to her academic career, she worked for two early-stage technology ventures in Silicon Valley. She currently serves as a Lab Economist at the Creative Destruction Lab, one of Toronto’s premier seed-stage programs for technology startups. view more 

Credit: Rotman School of Management

December 2, 2021

Predictive analytics pays off with complementary investments

Study shows nearly $1 million productivity bump for some manufacturers 

Toronto - The predictive analytics industry is slated to earn more than $273 billion in 2022. Yet, despite the hype over big data and the forecasting power of tools such as statistical modelling and machine learning, not all firms that sink money into them reap benefits, prompting a research team to probe what makes the difference. 

They found that significant and complementary investments in IT capital, an educated workforce, and high efficiency manufacturing processes were “indispensable” to getting the most out of predictive tools that help firms optimize their performance. Among the 30,000 manufacturers surveyed in 2015 study, companies with predictive analytics averaged about a $500,000 to $1 million revenue increase. Firms that did not make at least one of these, mutually-reinforcing investments, however, saw little to no benefit. 

“These complements provide the organizational infrastructure to collect, analyze, and respond to predictions based on objective data,” explains Kristina McElheran, an assistant professor of strategic management at the University of Toronto Scarborough and UofT’s Rotman School of Management.  

“IT capital captures investments in data collection and computer hardware that can transmit, store, and analyze data, for example. Educated workers are known to be an essential ingredient for that system. And certain production environments provide richer data due to the processes they use.” 

Prof. McElheran and her co-authors worked with the U.S. Census Bureau to create a survey that was returned by a highly-representative sample of U.S. manufacturing plants for the two survey years, 2010 and 2015. The survey asked about manufacturers’ use of predictive analytics, management practices, availability and use of data in decision-making, and design of their production processes. Results were cross-linked with related data such as company production inputs and outputs. Manufacturers were targeted because they tend to be early innovation adopters. More than three-quarters of responding plants had adopted some form of predictive analytics by 2010, researchers found, although most firms used the tools only annually or monthly. Higher intensity of use was associated with greater productivity gains. 

Government requirements for collecting environmental and safety data also helped to “nudge” some firms into adopting predictive analytics by pushing them to implement necessary infrastructure and train workers to use it. Companies nudged in this way ultimately displayed stronger performance in the researchers’ findings. 

It’s no secret in the management world that IT investments realize better returns when supported by educated workers, and vice versa. What the research shows is that some firms have not yet made that connection in the context of predictive analytics, says Prof. McElheran. 

“We found it puzzling,” she says. “More research is needed to understand the organizational or market frictions that are causing this apparent misalignment, one that is proving to be quite costly in the firms we observe.” 

This is the first study to examine the impact of predictive technologies on productivity in a large sample. The paper was co-written with Erik Brynjolfsson, of Stanford University and Wang Jin at the MIT Initiative on the Digital Economy. 

The study appears in Business Economics.  

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For more information:

Ken McGuffin

Manager, Media Relations

Rotman School of Management

University of Toronto


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