Multiple Authors
By: Phanish Puranam, Ruchika Mehra

How should humans collaborate with artificial intelligence? This is a question of increasing urgency as AI becomes pervasive in the workplace. From screening job applications and chatting with customers to assessing investment portfolios, algorithms are working alongside us in myriad roles and organizational setups. But whether this collaboration is designed in ways that lead to trust and satisfaction—for us humans at least—is another story.

Respecting, rather than ignoring, human concerns about working with AI is not only consistent with humanistic values, as we noted in an earlier article, but also good for business. That’s why we ran the “Bionic Readiness Survey” to investigate what configurations of collaboration with AI algorithms humans are more or less likely to trust.

Katie Rapp’s picture

By: Katie Rapp

The Covid-19 pandemic brought to light a stark reality about current supply chains. As Nissan Motor’s chief operating officer Ashwani Gupta points out, “The just-in-time model is designed for supply-chain efficiencies and economies of scale. The repercussions of an unprecedented crisis like Covid highlight the fragility of our supply chain model.” The U.S. supply chain has so far struggled to adapt and restock pandemic-depleted inventories. There are industrywide shortages and a lag in how many manufacturers are responding.

Alexander Gelfand’s picture

By: Alexander Gelfand

For years, researchers have known that our physical and mental well-being improves when we freely give our time to help others. And when we do so through company-sponsored programs, performance-related outcomes like job satisfaction and commitment to work also get a boost.

But there has been little agreement among experts on why this should be the case.

Recently, however, professors Jeffrey Pfeffer and Sara Singer of the Stanford Graduate School of Business analyzed survey data from hundreds of businesses in the United Kingdom to tease out the mechanisms through which volunteering improves both employee health and organizational outcomes. (The data were collected through Britain’s Healthiest Workplace and includes more than 53,000 employee responses.)

David L. Chandler’s picture

By: David L. Chandler

Virtually all wind turbines, which produce more than 5 percent of the world’s electricity, are controlled as if they were individual, freestanding units. In fact, the vast majority are part of larger wind farm installations involving dozens or even hundreds of turbines whose wakes can affect each other.

Now, engineers at MIT and elsewhere have found that, with no need for any new investment in equipment, the energy output of such wind farm installations can be increased by modeling the wind flow of the entire collection of turbines and optimizing the control of individual units accordingly.


Illustration shows the concept of collective wind-farm flow control. Existing utility-scale wind turbines are operated to maximize only their own individual power production, generating turbulent wakes (shown in purple), which reduce the power production of downwind turbines. The new, collective wind-farm control system deflects wind turbine wakes to reduce this effect (shown in orange). This system increases power production in a three-turbine array in India by 32 percent. (Image: Victor Leshyk)

Lauren Dunford’s picture

By: Lauren Dunford

Industry 4.0 has been a hot topic for years now, for good reason: 86 percent of manufacturing C-suites say digital transformation is a priority, and about 91 percent of industrial companies are investing in digital factories. Yet Industry 4.0 has also become a buzzword in many ways, as so many companies’ attempts to execute have fallen short.

Scott Dietz’s picture

By: Scott Dietz

The manufacturing community has long struggled with finding skilled workers, citing, among other things, the misconceptions that manufacturing jobs underpay, are monotonous, and involve working in dirty factories. With the adoption of Industry 4.0—automation and robotics—the issue is as much about raising awareness and creating interest for high-tech careers in advanced manufacturing as it is about changing perceptions.

That’s why manufacturers should become more involved with their local schools. According to Bill Padnos, workforce development manager with the National Tool and Machining Association, 64 percent of high school students choose their careers based on their interests and experiences. Engaging with students via factory tours, educational programming and interactive contests raises awareness in ways that will help to fill the future talent stream. Plus, the more your region knows about manufacturing, the easier it is to get people interested in manufacturing careers.

Mark Hembree’s picture

By: Mark Hembree

‘Anyone can hit a home run if they try,” said the great Ty Cobb at the end of the deadball era as Babe Ruth rose to fame in the 1920s. Cobb was unimpressed by Ruth, the Sultan of Swat. “It’s a brute way to approach the game.”

In 2019, Major League Baseball (MLB) seemed to prove Cobb’s point as big leaguers whacked a record 6,776 home runs—671 more than any year in major-league history.

In previous years, there had been much scuttlebutt about the ball seeming livelier. But 2019 took the cake. That year, the MLB-standard ball was introduced in AAA baseball—and at the minor-league level, home runs soared at a major rate. Not since 2011 had there been more than 4,000 home runs in AAA. But in 2019 there were 5,749, up from 3,652 in 2018.

Consequently, minor changes were made in the manufacturing of the ball—giving rise to a new set of suspicions and theories.

Something must be done

Astute students of the game notwithstanding, everyone loves a home run. But MLB decided this was too much of a good thing, and Rawlings, MLB’s ball manufacturer, made changes to bring the ball more tightly within spec and the home-run count closer to the mean.

Bruce Hamilton’s picture

By: Bruce Hamilton

With GBMP’s 18th annual Northeast Lean Conference on the horizon, I’m reflecting on our theme, “Amplifying Lean—The Collaboration Effect.” The term collaboration typically connotes an organized attempt by unrelated, even competitive, parties to work together on a common problem; for example, the New United Motor Manufacturing, Inc. (NUMMI) collaboration between GM and Toyota, or the international space station. In a sense, these types of organized collaboration are analogs to kaizen events and significant organizational breakthrough improvement.

Being a longtime proponent of “everybody, every day”-type kaizen, however, I think the greater amplification of our continuous improvement efforts lies in our ability to work together in the moment to solve many small problems. But, just as intermittent stoppages on a machine may be hidden from consideration, so too these on-the-fly opportunities for collaboration may pass without notice.

An example from my own career as a manufacturing manager sticks with me as I consider the importance of everyday collaboration.

Adam Zewe’s picture

By: Adam Zewe

Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing. But figuring out how to print with these materials can be a complex, costly conundrum.

Often, an expert operator must use trial and error—possibly making thousands of prints—to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits.

MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real time.

They used simulations to teach a neural network how to adjust printing parameters to minimize error, and then applied that controller to a real 3D printer. Their system printed objects more accurately than all the other 3D printing controllers compared.

Ken Moon’s picture

By: Ken Moon

Henry Ford was onto something.

In 1914, the automaker began paying his factory workers $5 per day for eight hours of work on the assembly line. Although Ford had refined mass production to make it more efficient, he still needed employees to show up and stick around. The generous wage, equivalent to about $148 today, was meant to keep workers coming back.

A recent Wharton study measuring the effect of worker turnover on the quality of smartphones made in China proves what Ford probably realized more than 100 years earlier at his car plant in Michigan: A stable workforce is valuable, even in a factory setting where so much of the labor is unskilled.

“Ford created an automated system of work, but he recognized that to perform at a high standard, the system involved having workers whose work is interconnected,” says Ken Moon, a Wharton professor of operations, information and decisions. “From his actions, I kind of suspect that he knew what we found in this study.”

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