Tag: Faculty



Machine translations driven by artificial intelligence can ease the friction and increase international trade, according to a study from two Olin professors.

Machine learning and artificial intelligence have exploded onto the scene in recent years, offering the hope of greater business efficiency. At the same time, researchers have found virtually no empirical evidence supporting the promised strides in labor productivity and economic activity.

That is, until now.

Meng Liu

A forthcoming paper from WashU Olin researchers draws a direct connection between language translation driven by artificial intelligence and an increase in international trade. The paper, which analyzes data from online e-commerce site eBay, is among the earliest tangible signs that AI and machine learning are living up to their promise.

“There is plenty of anecdotal evidence that AI has exceeded humans in many areas, but there was not much causal empirical evidence,” said Meng Liu, visiting assistant professor of marketing. She notes that there is evidence that AI correlates with economic growth. “There seems to be a discrepancy between what our intuition says about AI versus what is actually observed.”

For example, aggregate productivity growth rates have been stagnating since the 2000s.

Xiang Hui

Liu and coauthor Xiang Hui, assistant professor of marketing at Olin, cited 2017 research from MIT and the University of Chicago highlighting the paradox between high expectations and modest productivity results for artificial intelligence. That paper, accounting for slower economic activity, cited stagnant or declining numbers for productivity and median income while the new technology burst on the scene.

“Pointing to aggregate growth statistics, AI pessimists say it’s not really helping our productivity,” Hui added. “The problem is that it normally takes time for organizations to ramp up complementary innovations, be it organizational or technological, to harvest AI’s benefits. This is where our paper comes in. Let’s look at the question in a friction-free platform where they use an AI-based translation system.”

Their paper, “Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform,” was accepted in April for publication in the journal Management Science. Using data from eBay, which managed more than $14 billion in trade across more than 200 countries in 2014, Hui and Liu demonstrated that a moderate improvement in the quality of language translation increased trade between countries on eBay about 10.9%.

The paper contrasted trading results between buyers and sellers in the United States against those in countries that do not primarily speak English, including countries in Latin America, Europe and Asia. They looked at trade before and after eBay implemented a new AI-driven machine translation service in 2014. By some measures, that service improved translation quality by about 10%.

The researchers also compared their results to a measure of trade cost Hui had explored in earlier research. As distances decrease between trading partners on eBay, the cost of trade drops. “What we show is that the introduction of machine translation is equivalent to reducing distances between countries by 26.1%.”

The research team took two approaches to analyzing the trade data. First, they contrasted US exports to countries where the advanced translation was available against those where it was not. The graph above shows results after the technology was introduced—and the visible increase in US exports to countries where it was available.

Next, the research team mitigated the effect of other factors that could have increased trade (more marketing, for example) by examining how machine translation affected longer versus shorter product titles. The theory was that translating longer titles required greater cost and effort, but would yield greater payoff—meaning the benefit of introducing machine translation should be higher for these items. Meanwhile, if eBay increased marketing, it would affect longer titles similarly to shorter ones. However, the graph below shows the greater effect on longer product titles.

“These comparisons suggest that the trade-hindering effect of language barriers is of first-order importance,” the researchers wrote. “Improved machine translation has made the eBay world significantly more connected.”

The authors noted that since their research was completed, Google has rolled out an even more powerful language translation tool “that has significantly improved translation quality” and, based on their research, “the effect (of Google’s software) on cross-border trade could be large.”

The authors argue that the introduction of machine translation on eBay provides a clean experiment where we can measure impacts. But ultimately, AI’s effect will like be seen in almost all economic sectors. As new systems come online, the authors wrote, “they will provide new opportunities to assess the economic impact of AI via natural experiments such as the one examined in this paper.”




Seth Carnahan

Seth Carnahan, Olin associate professor of strategy, has been recognized with the biennial “emerging scholar” award by the strategic management division of the Academy of Management, the professional association for management and organization scholars.

The STR Emerging Scholar Award “will be a promising scholar who has established a research record of exceptional quality. The recipient will have a solid publication record and his/her scholarly contributions will already demonstrate an impact on the field of strategic management.” Only researchers who have received their terminal degree within six years of the award date are eligible.

“Seth’s publication record displays his open-mindedness, intellectual range in both micro and macro disciplinary streams, and mastery of empirical methodologies,” the organization said in its announcement. “His mainstream contributions anticipate the entrepreneurial challenge of growth through labor advantages and reflect the new economy issues ahead.”

Carnahan said the award—from the largest academic organization for strategy researchers—was particularly affirming to his work.

“My research area is human capital, so I tend to study individual employees more often than the typical strategy researcher does. Consequently, there has always been some risk that the field would not view my work as mainstream,” Carnahan said. “The folks on the awards committee are not necessarily working in my research area, so it is very encouraging to me that an audience like that would appreciate what I am working on.”




Olin

The Center for Analytics and Business Insights, along with the Bauer Leadership Center, hosted a seminar March 26-27 on values-based/data-driven decision-making. The seminar, which hosted nearly 50 executives from a variety of local data-centered companies—including Wells Fargo and Teradata—was a huge success.

Seethu Seetharaman led the morning session for participants in a March 26-27 workshop on values-based, data-driven decision-making.

Seethu Seetharaman, director of CABI, kicked off the event with a discussion on hypothesis testing.  Seetharaman, W. Patrick McGinnis Professor of Marketing, brought statistics to life with riddles and brain teasers. With a group filled with professionals working with statistics daily, Seetharaman’s brain teasers still expanded their knowledge and truly kept them on their toes. A quick example of a brain teaser to try at home:

“A light flashes red 75% of the time and green 25% of time. You have to predict what the next flash will be. Is it reasonable to use a biased coin weighted to flip toward red 75% of the time to predict the next flash? Or is it more reasonable to just predict red?”

Seethu Seetharaman and Stuart Bunderson teamed up on the March 26-27 workshop on values-based, data-driven decision-making.
Seethu Seetharaman and Stuart Bunderson teamed up on the March 26-27 workshop on values-based, data-driven decision-making.

The answer is actually no. The likelihood of having the coin and the light flash the same color is slim because there are two separate events. The two events will only align 62.5% of the time, so it’s safer to predict red from the get-go with its 75% probability. 

The first afternoon was followed up by Stuart Bunderson, director of the Bauer Leadership Center. Bunderson, George & Carol Bauer Professor of Organizational Ethics & Governance, discussed values-based decision-making, incorporating values and ethics in business, while backing them with statistical evidence.

Bunderson amusingly warned the data-focused audience, “We are now entering the domain of moral philosophy… we answer which outcomes we should be striving to achieve through discussion and mindful reflection, informed by theoretical frameworks.”

Bunderson continued, asking, “What are values?”

Values are beliefs about preferred “end states.” They can be explicit or implicit, but regardless, represent the fundamental building blocks of an individual’s character or organization’s culture.

Values are all about the why of a business: the how represents tactics and execution, the what are objectives and KPIs, while the core why are values and the mission. The why drives the what and the how.

At the end of the session, Seetharaman’s statistics-heavy lecture paired perfectly with Bunderson’s value-based message. The audience collectively resonated with the idea that a business is firmly, at its core, driven by values.

However values must guide decisions made and then must be secondly backed by statistics. You must be able to understand your KPIs while still keeping true to your business’s mission.




A global trade war initially launched with Trump Administration tariffs on Chinese steel in 2018 indeed boosted domestic steel production. But as analysts learned how higher costs would affect downstream manufacturers—and later affect demand for domestic steel—stock prices for US steelmakers tumbled by almost 50 percent year-over-year.

Panos Kouvelis

Olin researchers cited that anecdote—among many others—in new research exploring the complexity of tariffs as a trade tool in a global economy. The new paper also established a supply chain model to explain those effects and proposing that in some cases, the effects were foreseeable when accounting for strategic, multi-party interactions and competition.

“The logic that levying tariffs will help protect and strengthen the corresponding domestic industries is not that straightforward in today’s global economy,” wrote Lingxiu Dong and Panos Kouvelis in their paper, “The Impact of Tariffs on Global Supply Chain Configuration: Models, Predictions and Future Research,” accepted for publication in the journal Manufacturing & Service Operations Management.

Lingxiu Dong

When policy makers employ a tariff—a tax on imported or exported goods—as a tool to protect a domestic industry from foreign manufacturing, they may assume the industry operates in a vacuum. But as Kouvelis explained, the effect of imposing a tariff on, say, soybean exports, has ripple effects throughout the supply chain for both soybean farmers and their suppliers as well as for the downstream consumers of soybeans.

Risk of changing suppliers

In retaliation for earlier US tariffs, the Chinese government imposed a 25% tariff on 106 US goods—including soybeans—in April 2018. Chinese buyers of US soybeans—often used as feed by pork producers—have started finding suppliers in Brazil and Argentina, avoiding higher prices. Thus, the Chinese market begins to dry up for US soybean farmers, possibly forever. Agribusiness firms in South-America are expanding aggressively in the region to capture the Chinese market opportunity.

“Suddenly, they realize there’s another sourcing opportunity and they seize the opportunity,” said Kouvelis, Emerson Distinguished Professor of Operations and Manufacturing Management at Olin. “Tariffs have short-term benefits and long-term implications that are frequently quite unpleasant. In the long term, firms adjust to the new realities.”

Kouvelis and Dong began working on their paper about 10 months after the Trump Administration levied the first tariffs on steel and aluminum imports from all nations, including China, in March 2018.

“It’s a very timely topic. What we thought as we started reading the stories was that the impact is not that obvious,” Kouvelis said. Under the theory, tariffs would protect US manufacturers from cut-rate imports of foreign-made steel and aluminum. US firms could expand, hire and supply more US consumers of steel and aluminum. But that’s not how it works.

“Trade policies such as tariffs have significant implications not only for the industries the policies were intended to protect, but also for the complex supply chain that they are a part of,” said Dong, professor of operations and manufacturing management. “The net effect of those reactions on the industry and the supply chain is hard to predict.”

The researchers did not conclude that tariffs were a poor instrument for executing trade policy. Rather, policy makers must be aware of the likely effects if they’re used. For example, tariffs may indeed restrict trade within a region of the globe—but that doesn’t mean all the companies within that region will be US firms.

“Companies go where they see the opportunities and the growth,” Kouvelis said. “We are moving towards regional supply chains, and in many cases that might be a desirable supply chain outcome. Shorter and market-focused chains are often argued as agile and lean. But tariffs might not have been the best way to end up there, and they may have caused competitive headaches for some of the US companies.”

Building a forecasting model for supply chain restructuring

In their research, the pair developed a number of mathematical models accounting for different variables in the supply chain. They examined where the supplier of raw materials is located relative to the manufacturers of finished goods, for example. Or whether the suppliers or manufacturers have multiple production plants in international locations or localized facilities.

Other variables include the costs of shipping goods or finished products and the ability (or inability) of a company to pivot to new suppliers or production facilities as costs rise.

Woven throughout, the two researchers sprinkled anecdotes about how the tariffs have affected companies and industries. Motorcycle maker Harley-Davidson, for example, experienced higher production costs in the United States, thanks to steel and aluminum tariffs, and an increase of $2,200 per bike from shipment costs resulting from European retaliatory tariffs. The company ended up shifting some of its production to Europe to better deal with such cost increases.

Meanwhile, the researchers captured the complexity of the auto industry, where US-made cars may be using Chinese components that are potentially exposed to US tariffs, while the final products exported to China are also exposed to Chinese tariffs.

The model Kouvelis and Dong created would predict what is actually happening: US carmakers are shifting production to China, especially for the lower end car models—employing more Chinese workers and fewer US workers. US production facilities will experience further labor declines through flexible automation.

“We can tell you in stories after the fact some of the impact, but we need a model that predicts the direction of change and explains the stories,” Kouvelis said. “What are the factors you have to think about so you can predict the move before it happens—rather than being a Monday-morning quarterback?”




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.”