Tag: Olin Award

Anne Marie Knott, the Robert and Barbara Frick Professor of Business at Olin, has won the 2021 Olin Award for “RQ Innovative Efficiency and Firm Value,” forthcoming in the Journal of Financial and Quantitative Analysis.

The Olin Award, which includes business school recognition and a $10,000 prize, is intended to promote scholarly research that has timely, practical applications for complex, real-world management problems.

Anne Marie Knott

Dean Mark Taylor surprised Knott with the good news on Tuesday during a Zoom chat, which she initially believed was to discuss her annual performance review.

“I didn’t know it was a party!” Knott said when she joined the chat, which was populated by a small group including Richard J. Mahoney, former CEO of Monsanto and a Distinguished Executive-in-Residence at Olin.

Taylor told her the Olin Award judges had chosen her paper as the winner, and he led a round of applause.

“Thank you! I’m so excited,” Knott said.

“One of the things our judges liked was that it was written in our native tongue,” said Mahoney, who initiated the Olin Award 14 years ago. “Not that it was simple, but it was well written.”

Knott’s winning entry—one of 14 papers submitted—­­explores RQ’s ability to serve as a robust measure of a firm’s innovation for the finance audience. RQ is short for research quotient, which is measured as the output elasticity of Research & Development. First developed by Knott in 2008, RQ can be estimated for any firm reporting R&D.

Knott’s paper is “making an academic impact as well as a business impact,” Taylor noted.

Knott said people already had downloaded it about 5,000 times. “It’s my most-downloaded paper,” she said.

R&D is the primary source of growth for 40% of companies, Knott explained in her submission letter, “yet companies are flying blind with respect to their R&D, because they lack good metrics.”

The front-end implication of flying blind is that companies don’t know how much to invest in R&D, she said.

“Only 4% of companies invest within plus 10% of optimal levels. The remaining 96% of companies are leaving an average of $182 million of foregone profits on the table each year.”

The back-end implication of flying blind is that companies can’t tell if they’re effectively managing their R&D.

“As a consequence, companies’ R&D productivity has declined 65% over the past four decades,” she said. “This not only hurts companies and their stakeholders; it hurts the entire economy, because R&D is the primary driver of economic growth.”

Knott said she and her co-authors wondered if academics suffered similar problems with respect to innovation metrics. So they tested the ability of academic innovation metrics to predict firm value.

“While all metrics performed well in some tests, only RQ consistently predicted current and future firm value,” she said.

Knott will present her paper during a virtual Olin Award event in the spring. A date will be announced soon.

Dennis Zhang created a human-focused algorithm to improve warehouse workers’ packing time, winning him the Olin Award for the second consecutive year. 

“This was a stunning piece of research,” Dean Mark Taylor said. “A lot of the judges put ‘number one’ immediately.” 

Zhang’s research focused on packing efficiency at the Chinese online retail giant Alibaba. He and his coauthors created an algorithm that, as it turns out, could save Alibaba more than $2.6 million a year. The paper, “Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations,” is under revision in Management Science.

The Olin Award, which includes business school recognition and a $10,000 prize, is intended to promote scholarly research that has timely, practical applications for complex management problems.

Conventional bin-packing algorithms prescribe which items to pack in which sequence in which box. They focus on the best use of a box’s volume. But here’s the problem: Those algorithms tend to overlook how humans might deviate from instructions and create delays. Workers might not organize items as the algorithm prescribes if, for instance, packing a box is complex because it includes numerous items or items with unusual shapes. 

“It takes the algorithm and the executors of the algorithm—the people delivering the outcomes,” said Zhang, assistant professor of operations and manufacturing management. “I call this particular characteristic artificial intelligence and human collaboration. Such particular characteristics allow us to design better algorithms.” 

Here a some takeaways from the research:

  • The new algorithm predicts which orders will confuse workers and adjusts the box size to a larger one. 
  • The cost of box material may increase, but more savings come from fewer packing delays. 
  • AI and robotics can improve human work by providing more support for the decisions people make while working. 

Last year, Zhang and Jake Feldman, assistant professor of operations and manufacturing management, received the award. They used data from Alibaba to test the benefits of—and recommend a solution for—presenting buyers the optimum variety of products available for purchase with individual online retail stores. 

In this year’s winning research, the idea is not to strive for autonomous automation, the authors wrote. “We believe that AI and robotics can improve human work by providing more decision support while always empowering human judgment, oversight and discretion.” 

Zhang presented his research virtually to members of the Olin community and guests on June 25.

Coauthors of the paper are Jiankun Sun of Imperial College Business School, Haoyuan Hu of the Alibaba Group and Jan A. Van Mieghem of Northwestern University.

Dennis Zhang and Jake Feldman’s research started as a debate—a little like the Reese’s candy commercial pitting peanut butter lovers against chocolate lovers. In the end, they combined the ingredients to come up with something bigger.

In the case of their Olin Award-winning research, however, when Feldman got “customer choice modeling” in Dennis Zhang’s “machine learning” and Zhang got “machine learning” in Feldman’s “customer choice modeling,” the result was 28% higher revenue in a week’s time for Chinese online retail giant Alibaba.

Dennis Zhang

The result came about because the pair collaborated to create a new customer choice algorithm designed to better populate six available slots for products in online stores hosted by Alibaba. Those six products appear because the platform instantly crunches millions of variables to display options with the highest likelihood of driving a sale.

Zhang and Feldman, both assistant professors of operations and manufacturing management at Olin, thought the platform could do better—but not before they realized they could combine their disparate approaches into a new mathematical model for presenting product choices to customers. The pair partnered with Alibaba researchers on their working paper, “Taking Assortment Optimization from Theory to Practice: Evidence from Large Field Experiments on Alibaba,” under consideration by a top journal.

Jake Feldman

“I worked at Google as a machine learning scientist,” Zhang said. “I was a true believer of machine learning, which can basically crack most problems. But Jake is a hardcore believer of (customer choice) optimization. In order to convince each other, we started working on this project by combining machine learning with optimization.”

For Alibaba and other online retailers, the problem goes something like this: Among thousands of available products, which ones should the platform recommend to maximize revenue and give customers the most useful choices? Alibaba had relied on machine learning to quickly gauge a visitor’s past purchasing history, age, location, the history of similar customers and a million other variables to come up with a selection of choices to display.

But while machine learning can instantly weigh those variables and display customized product choices, it’s a poor tool for providing wider variety, often displaying products that overlap with or cannibalize other products.

As Feldman describes it, the problem is two-tiered: The first tier is about estimating what customers might like. Machine learning is good at that. The second is optimizing the choices based on what other choices are available and who the customer is—a weak spot for machine learning.

The machine learning-based system might present shoppers with two very similar shirts, or shirts with very different price points—products that don’t offer the right variety or might undermine the retailer’s ability to maximize revenue. It’s particularly troubling when one product is not quite right, but the shopper isn’t given a suitable substitution option.

So, Feldman turned to mathematical models for optimizing customer choices from half a century ago. “They’re not as robust as these machine learning models,” he said. “But what that buys us is we can then solve a more sophisticated optimization problem.”

The researchers tested their new algorithm against the traditional machine learning model Alibaba had used. In a weeklong experiment in March 2018, watching 14 million customers on Alibaba-owned shopping sites, the new combination model showed 28% higher revenues—or nearly $22 million. The results were so conclusive, Alibaba adopted the new product selection model.

The pair agrees that the next step in their research—already underway with Alibaba—is tweaking the model to include data about products the shopper has shown interest in by clicking on them without a purchase. The new model also breaks when more than one product choice is appealing to the customer—another area ripe for further study.

But for now, the pair is very happy with the results of this first collaboration. The results, Zhang said, “seem very inspiring to me because it shows my field and Jake’s field are actually important in the real world of business.”

Dick Mahoney, Jake Feldman, Dennis Zhang and Dean Mark Taylor at the announcement of the two professors

Dear Olin community,

It is my pleasure to announce that the recipient of the 2019 Olin Award is Taking Assortment Optimization from Theory to Practice: Evidence from Large Field Experiments on Alibaba by Jake Feldman, assistant professor of operations and manufacturing management, and Dennis Zhang, assistant professor of operations and manufacturing management.

In its 12th year, the Olin Award was established to recognize scholarly research that has timely, practical applications. This year’s winning entry uses data from the Chinese online retail giant Alibaba to test the benefits—and recommend a solution—for presenting buyers the optimum variety of products available for purchase with individual online retail stores. Of the 16 papers submitted this year, six went on to the second round, rated by our corporate judges as research with potential impact to business.

Some of those papers will be presented in the coming months in the Olin Business Research Series. The winning paper will be presented at a luncheon on May 22, 2019, from noon till 1:30 p.m. We will send out a formal invite in the near future.

Special thanks to Olin Distinguished Executive in Residence Richard Mahoney and all of the judges for their ongoing support. We look forward to all of next year’s faculty submissions. Please join me in congratulating Jake Feldman and Dennis Zhang.

Pictured above: Richard Mahoney, Jake Feldman, Dennis Zhang and Dean Mark Taylor.

Ling Dong and Durai Sundaramoorthi smile as Dick Mahoney announces that they won the 2018 Olin Award with Dean Mark Taylor.

Ling Dong and Durai Sundaramoorthi have won the 2018 Olin Award for research that creates a framework that can help farmers select the proper seed varieties to maximize their crop yields from one season to the next.

The Olin Award, which includes business school recognition and a $10,000 prize, is intended to promote scholarly research that has timely practical applications for complex management problems.

“Soybean farmers are subjected to dozens and dozens of seed varieties,” said Richard J. Mahoney, former CEO of Monsanto and a Distinguished Executive-in-Residence at Olin, who initiated the $10,000 prize. “If you knew you’d have perfect weather, certain varieties would work better than others. A bad guess can be quite punishing.”

Dong, professor of operations and manufacturing management, and Sundaramoorthi, senior lecturer in management, received notice that they had received this year’s award, competing against a score of other papers and finalists, during a brief ceremony in Dean Mark Taylor’s office on Monday.

“Improving crop yield is a critical and necessary component of achieving food security and protecting natural resources and environmental quality for future generations,” Dong and Sundaramoorthi wrote in their award-winning paper, entitled, “Machine Learning Based Simulation and Optimization of Soybean Variety Selection.”

“We formulate a simulation-based optimization problem to determine the optimal soybean-mix to minimize the risk associated with the yield…to make soybean-mix recommendations to the farmers.”

A panel of judges evaluates each paper submitted for consideration for the Olin Award. After reviewing the entries, one judge wrote, “It is directly applicable to business results and is something that every farmer that raises soybeans can benefit from regardless of their size—scale independent.”

Wrote another: “Within the narrow application this would seem to have great potential to produce meaningful benefits.”

The research pair will be formally recognized at a luncheon yet to be scheduled, where they will have the opportunity to present their research.