Multiple Authors
By: Jill Barshay, Sasha Aslanian

When Keenan Robinson started college in 2017, he knew the career he wanted. He’d gone to high school in a small town outside Atlanta. His parents had never finished college, and they always encouraged Robinson and his two older siblings to earn degrees. Robinson’s older brother was the first in the family to graduate. “My parents always stressed how powerful an education is and how it is the key to success,” Robinson says.

When Robinson arrived at Georgia State University in Atlanta, he wanted to major in nursing. “I always knew I had a passion for helping people,” he says. Biology had been his best subject in high school. “My dad, my mom would always kind of call me like the king of trivia because I’d always have just like random science facts.”

During his freshman year, Robinson earned a B average. But the university was closely tracking his academic performance and knew from 10 years of student records that Robinson wasn’t likely to make the cut for the nursing program.

Georgia State is one of a growing number of schools that have turned to big data to help them identify students who might be struggling—or soon be struggling—academically so the school can provide support before students drop out.

Ekim Saribardak’s picture

By: Ekim Saribardak

Transporting cargo over long distances has always been a logistical nightmare, but when the goods are of a delicate nature, the whole operation becomes significantly more challenging. Perishable foods, chemicals, pharmaceutical products, and other delicate goods all need special treatment during transportation to keep them in optimal condition; in many cases, constant monitoring of the cargo’s temperature is necessary to ensure its integrity until delivery.

Luckily, thanks to the technological advances of the last two decades, logistics companies no longer have to rely on rudimentary methods such as manually inspecting the cargo hold, which used to be a cause of excess downtime and loss of productivity, and wasn’t particularly reliable.

Andrew Edman’s picture

By: Andrew Edman

On factory floors all over the world, 3D printing has quietly moved from a prototyping novelty to an essential tool. Advances in printer technology and material science mean that today’s 3D printed parts are robust enough to hold up to real-world wear and tear, and precise enough for demanding production requirements. Today, when production engineers look to maintain quality, reduce cost, or boost efficiency, they are turning to 3D printing to get the job done on time and on budget.

Shortly after Ashley Furniture, the world’s largest furniture manufacturer, brought in the company’s first Formlabs stereolithography (SLA) 3D printer, one of their production engineers decided to try replacing machined alignment pins with 3D printed parts. If these held up to constant cycles and impact, the company could avoid the long lead times and minimum-order quantities of outsourcing the production of the alignment pins.

The engineer’s experiment was successful and led to more tests to find out where they could use 3D printing to improve fabrication and assembly processes. By examining how Ashley Furniture is driving best practices with 3D printing, we can better understand how to apply those insights to any manufacturing or assembly environment.

Peter Rose’s picture

By: Peter Rose

On May 26, 2020, the new European Union Medical Device Regulation (MDR) will finally take effect. By that date, all Class I manufacturers wishing to continue their trading activities within the EU market must have effectively completed the transition from the previous medical device directive and be fully compliant under EU MDR.

This statement alone may be surprising to certain Class I manufacturers, who assume that their products’ classification as low-risk devices under the previous directive will exempt them from all this EU MDR commotion. These presumptions are misguided because classification requirements listed in the EU MDR are relevant to all manufacturers, irrespective of past classification.

With this deadline in sight, it is crucial that all manufacturers familiarize themselves with these regulatory changes and promptly make a start on implementing necessary measures. Those that fail to achieve compliance on time will be left behind, and their products removed from the market. In light of this industry bustle, this article aims to advise Class I manufacturers about the primary alterations that the EU MDR will enforce, as well as offer practical steps that manufacturers can begin to follow.

Simon Côté’s picture

By: Simon Côté

The aerospace industry is known for manufacturing parts with critical dimensions and tight tolerances, all of which must undergo demanding inspections. Given the scale of the controls to be carried out on these parts, it is hardly surprising that quality people in the industry prefer to turn to coordinate measuring machines (CMMs). However, directing all inspections to the CMM may cause other problems: CMMs are hyper-loaded and can generate bottlenecks during inspections, slow down manufacturing processes, and cause production and delivery delays.

Is it possible to unload CMMs so that they are fully available for the final quality controls? How can we improve manufacturing processes to produce more parts faster, and above all, of better quality? In the event of a quality issue occurring during production, is it possible to identify the root cause more quickly to minimize the delays that could impact schedules and production deliveries?

Knowledge at Wharton’s picture

By: Knowledge at Wharton

From a lone statistician toiling over narrowly defined problems for the marketing department, to a C-level executive overseeing a mission-critical area impacting every function of the company, the meaning of “data and analytics professional” has changed a lot in recent years. A. Charles Thomas’s career has reflected those developments.

Thomas, who is General Motors’ first-ever chief data and analytics officer, shared where corporate data analytics has been, where it’s going, and the evolution of chief data officer roles, in a keynote at the recent Wharton Customer Analytics conference “Successful Applications of Analytics.” He spoke from his experience not only at GM, but also other major companies, including Hewlett Packard, the United Services Automobile Association (USAA), and Wells Fargo.

During the late 1990s, he said, data analysts were typically individual contributors working with transactional data involving marketing, credit, and retail. “The [data analyst’s] reputation was ‘a smart guy,’” said Thomas. “You want an answer, you come to Charles.”

Zach Winn’s picture

By: Zach Winn

Manufacturers are constantly tweaking their processes to get rid of waste and improve productivity. As such, the software they use should be as nimble and responsive as the operations on their factory floors.

Instead, much of the software in today’s factories is static. In many cases, it’s developed by an outside company to work in a broad range of factories, and implemented from the top down by executives who know software can help but don’t know how best to adopt it.

That’s where MIT spinout Tulip comes in. The company has developed a customizable manufacturing app platform that connects people, machines, and sensors to help optimize processes on a shop floor. Tulip’s apps provide workers with interactive instructions, quality checks, and a way to easily communicate with managers if something is wrong.

Managers, in turn, can make changes or additions to the apps in real-time and use Tulip’s analytics dashboard to pinpoint problems with machines and assembly processes.

Multiple Authors
By: Natasha Gilbert, Knowable Magazine

Alfalfa, oats, and red clover are soaking up the sunlight in long narrow plots, breaking up the sea of maize and soybeans that dominates this landscape in the heart of the U.S. farm belt. The 18 by 85 meter sections are part of an experimental farm in Boone County, Iowa, where agronomists are testing an alternative approach to agriculture that just may be part of a greener, more bountiful farming revolution.

Organic agriculture is often thought of as green and good for nature. Conventional agriculture, in contrast, is cast as big and bad. And, yes, conventional agriculture may appear more environmentally harmful at first glance, with its appetite for synthetic pesticides and fertilizers, its systems devoted to one or two massive crops and not a tree or hedge in sight to nurture wildlife.

As typically defined, organic agriculture is free of synthetic inputs, using only organic material such as manure to feed the soil. The organic creed calls for caring for that soil and protecting the organisms within it through methods like planting cover crops such as red clover that add nitrogen and fight erosion.

Aliyah Kovner’s picture

By: Aliyah Kovner

It’s 1 p.m. on a sunny afternoon in July—smack dab in the middle of summer break—and a perfect 75° outside, but Jonathan Park is laser-focused. Though he could be strolling down a beach, or at home browsing social media, this 16-year-old is bent over a lab bench, intently pipetting reagents to run an Amplex Red assay.

Park, a soon-to-be junior at Dublin High School, is part of the 2019 cohort of the Introductory College Level Experience in Microbiology (iCLEM) summer intensive, hosted and run by the Joint BioEnergy Institute (JBEI) in Emeryville, California. First launched in 2008, iCLEM immerses local Bay Area students in the biological sciences—and gives them a taste of day-to-day life as a scientist—through an eight week-long blended curriculum of instruction, hands-on basic laboratory skill training, and in-depth tours of working labs within JBEI, Lawrence Berkeley National Laboratory (which manages JBEI), and local biotech companies. The students, who receive a stipend so that they may attend the program in place of a summer job, utilize their newfound knowledge by conducting independent research projects and presenting their findings at the end of the program.

Eric Weisbrod’s picture

By: Eric Weisbrod

In manufacturing, standardization in production and process control leads to increased profitability and cuts down on many siloed problems that can plague even the most quality-focused organization. But when you have multiple, disparate plants around the country or the globe, standardization can seem unattainable, as each site operates more like an island with its own way of doing things.

I previously worked with one company that had numerous plants throughout the United States and Europe. Over time, each site had developed unique practices. One specific issue that came up was whether to use the metric system for quality data collection. Unfortunately, the company did not standardize. So, when it was time to run cross-plant reports, the results were in different units, presenting a challenge to comparative analysis.

In contrast, I worked with a separate organization that was very adamant about using a standardized approach and implementing the same quality management system at every site. Standardization made it easy to deploy the system to approximately 80 plants in merely 18 months. In addition to streamlining deployment, standardization enabled the company to perform enterprisewide reporting for better decision-making.

Syndicate content