Tag: Olin Award



Dennis Zhang has won the 2020 Olin Award for research that creates a human-focused algorithm to improve warehouse workers’ packing time while also reducing material costs.

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.

Zhang, assistant professor of operations and manufacturing management, received notice that he had won this year’s award during a short meeting in Dean Mark Taylor’s office on Wednesday afternoon.

His winning research focused on bin packing at the Chinese online retail giant Alibaba to test a human-centric algorithm that, as it turns out, could save Alibaba more than $3 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.

“Well, that’s a nice surprise,” said Zhang, laughing.

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

Richard J. Mahoney, former CEO of Monsanto and a Distinguished Executive-in-Residence at Olin, initiated the award, now in its 13th year.

This is Zhang’s second Olin Award. Last year, he and Jake Feldman, assistant professor of operations and manufacturing management, received the award for “Taking Assortment Optimization from Theory to Practice: Evidence from Large Field Experiments on  Alibaba.” 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.

Human-centric algorithm

This year’s winning research focused on workers’ bin packing at Alibaba’s warehouse to test a human-centric algorithm. Conventional bin-packing algorithms prescribe which items to pack in which sequence in which box or bin. All the while, they focus on the best use of a bin’s volume. Here’s the rub: Those algorithms tend to overlook how humans might deviate from the plan.

“Today, the adoption of artificial intelligence (AI) and robotics is accelerating and revolutionizing business operations by augmenting human work,” Zhang and co-authors wrote in their award-winning paper.

“Indeed, rather than striving for autonomous automation, we believe that AI and robotics can improve human work by providing more decision support while always empowering human judgment, oversight and discretion.”

A panel of judges evaluated each of the 19 papers submitted this year for consideration for the Olin Award.

“The topic of the paper and applicability to business are very relevant as e-commerce continues to grow as a business channel in the United States and globally,” one judge wrote about Zhang’s paper. “Understanding how to save time on packing at warehousing is very relevant” and could deliver high savings for big operations like Alibaba and Amazon.

“This is a truly stellar paper,” another judge wrote.  “The issue addressed—how to anticipate human modification of computer algorithms in their work—is a large one across many sectors of the economy.  The randomized, controlled nature of the study makes the conclusions that much stronger. Well done.”

Zhang will be recognized at a luncheon on April 1, where he will have the opportunity to present his research.

Zhang’s co-authors 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.




FRIDAY, APRIL 15, 2016 - This is the Washington University Olin Business School's Distinguished Alumni Awards Dinner at the Ritz-Carlton Hotel.<br />

©Photo by Jerry Naunheim Jr.