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.”

Catherine Barzler’s picture

By: Catherine Barzler

Falls are a serious public health issue that result in tens of thousands of deaths annually while racking up billions of dollars in healthcare costs. Although there has been extensive research into the biomechanics of falls, most current approaches study how the legs, joints, and muscles act separately to respond, rather than as a system. The ability to measure how these different levels relate to each other could paint a much clearer picture of why someone falls and precisely how their body compensates. Until recently, however, an integrated measuring approach has been elusive.

Multiple Authors
By: Ella Miron-Spektor, Kyle Emich, Linda Argote, Wendy Smith

‘The experience was magical. I had enjoyed collaborative work before, but this was something different,” says Daniel Kahneman of the beginnings of the years-long partnership with fellow psychologist Amos Tversky that culminated in a Nobel Prize in economic sciences three decades later.

What Kahneman didn’t dwell on in his account was how different the two men were. One was confident, optimistic, and a night owl; the other was a morning lark, reflective, and constantly looking for flaws. Yet their partnership flourished.

“Our principle was to discuss every disagreement until it had been resolved to mutual satisfaction,” recalls Kahneman, author of the best-selling book, Thinking, Fast and Slow (Farrar, Straus and Giroux, 2013). “Amos and I shared the wonder of together owning a goose that could lay golden eggs—a joint mind that was better than our separate minds.”

Tim Mouw’s picture

By: Tim Mouw

According to autolist.com, more than 80 percent of cars produced today are white, black, or some shade of gray. It’s not necessarily because bright and bold colors are more difficult to produce and match than their grayscale counterparts. They just take longer to get through the inspiration and design process.

Believe it or not, producing a new auto color can take up to five years before it makes it to the showroom floor. It’s a long, tedious process for designers, paint companies, and auto manufacturers, but innovative color measurement technology is changing the game and reducing time to market.

Moving from inspiration to production of new car colors

Inspiration for future auto colors can come from just about anywhere—architecture, nature, Pantone Color of the Year, even the Paris Fashion Show. However, it’s not as simple as choosing a bright red poppy flower from the park or a muted yellow scarf from the runway and putting the color on a car.

Martine Haas’s picture

By: Martine Haas

One thing is clear about the future of work: Hybrid work arrangements are becoming the norm for many organizations. And no matter the industry, the concerns involve the same five “C” challenges: communication, coordination, connection, creativity, and culture. If you’re struggling to manage a hybrid team or workforce, or your own hybrid work, start by understanding the five challenges, then use the action steps below to assess where you’re at and where to go from there.

Communication: This challenge includes technology snags; meetings in which some people are remote; conversations monopolized by one or a few team members; and barriers due to power, status, and language differences.

Coordination: Greater effort is required to level the playing field between onsite and remote workers. Remote teammates can get left out of small exchanges and minor decisions, which can grow into bigger conversations and more important decisions.

NIST’s picture

By: NIST

A novel, quantum-based vacuum gauge system invented by researchers at the National Institute of Standards and Technology (NIST) has passed its first test to be a true primary standard—that is, intrinsically accurate without the need for calibration.

Precision pressure measurement is of urgent interest to semiconductor fabricators that make their chips layer by layer in vacuum chambers operating at or below one hundred-billionth the pressure of air at sea level. They must rigorously control that environment to ensure product quality.


NIST scientist Stephen Eckel behind a pCAVS unit (silver-colored cube left of center) that is connected to a vacuum chamber (cylinder at right). Credit: C. Suplee/NIST

“The next generations of semiconductor manufacturing, quantum technologies, and particle acceleration-type experiments will all require exquisite vacuum and the ability to measure it accurately,” says NIST senior project scientist Stephen Eckel.

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