With Covid-19 continuing to impact many businesses, lead time as well as sourcing new suppliers is increasingly difficult. If you currently outsource manufacturing overseas, it is likely you have encountered some turbulence to your supply chain.

Rapid prototypes and large-quantity production of special precision parts and components are key to many business’s operations. Along with this, an agile business strategy that enables the sourcing of verified suppliers, as well as maintaining production-line efficiency, are critical. With travel to Asia currently stalled, and many factories presently closed or operating at low capacity, this strategy is not easily executed for many companies worldwide. Although engineers have a wide array of companies to choose from to get their machining parts manufactured, the turnaround time can be weeks from order to delivery.

‘That escalated quickly!” is a common trope used in popular culture to describe when a situation gets out of hand before you’ve even had a chance to think about it. We don’t often use this trope in medicine, but I can think of nothing better to describe what has been going on in the United States with the coronavirus outbreak.

I am a physician scientist who practices infectious disease medicine and runs a research laboratory that specializes in viruses. I spend much of my time directing a clinical microbiology laboratory for a large academic medical center. If you’ve ever had a doctor tell you that they are going to test you for a virus, it’s teams like mine that develop and run that test.

When I first heard about the coronavirus outbreak in China, I had no idea I would soon be on the front lines of dealing with this outbreak.

For most of us, the word “robot” conjures something like C-3PO—a humanoid creature programmed to interact with flesh-and-blood people in a more or less human way. But the roster of real-world robots is considerably more varied. The list includes Boston Dynamics’ dog-inspired robots, Dalek-like security bots, industrial arms on an assembly line, and any number of flying insect-inspired robots. If a machine is designed to do a complicated task in an automated fashion, it’s a robot.

A robot, it turns out, doesn’t even need to have a fixed shape. That’s the vision of researchers who work in modular reconfigurable robotics (MRR) and are pursuing bots that can assemble themselves, by rearranging similar or identical parts into whatever shape suits the task at hand. These robots can take the form of snakes, lattices, trusses, and more, and can be set to any challenge—providing construction support, doing repair work, or scouring for survivors after a natural disaster.

Companies and societies are at the precipice of rebuilding their foundations to compete in an age of advanced analytics, artificial intelligence (AI), and machine learning (ML). Yet, in the real economy—or in the world outside the tech companies—I see more struggle than success in making advanced analytics and AI a management discipline.

Most leaders in these companies recognize that the perfect storm of big data, computing capacity, and algorithmic advances has arrived. They hear about spectacular use cases such as AI outperforming trained radiologists in detecting retinopathy in preemies. Research also shows that text analytics of earnings calls reveal that executives’ use of euphemisms (think “headwinds”) obscures the details of bad news and delays negative investor reaction. Yet, many leaders feel unsure about this new environment and are struggling to extract value from these cutting-edge technologies.

Each new year brings about a period of reflection, where one can think back on the path that the previous year took us on. 2020 represents an even larger opportunity for reflection as the world enters a new decade. Reflection provides an opportunity to learn and improve, and extends beyond just an individual to include industries and businesses. As a U.S. manufacturing enthusiast, I’m looking back over the past 10 years at how manufacturing has changed, evolved, and innovated so that I can continue to support that evolution.

The U.S. manufacturing industry is entering this new decade in a much different state than when it entered the last. The industry is no longer shaking off the aftereffects of the Great Recession, but it is still grappling with the economic uncertainty that comes with new trade deals, tariffs, and other global uncertainties. There is also the need to keep pace with the ever-increasing speed of technological change. Industry 4.0 and its adoption by U.S. manufacturers has begun to pick up steam, and manufacturing’s digitization is only going to increase. Things like 3D printing, advanced robotics, artificial intelligence, and smart factories are becoming more commonplace in the U.S. manufacturing industry, emphasizing a deepening need for stronger cybersecurity.

Imagine you’re a fossil hunter. You spend months in the heat of Arizona digging up bones only to find that what you’ve uncovered is from a previously discovered dinosaur.

That’s how the search for antibiotics has panned out recently. The relatively few antibiotic hunters out there keep finding the same types of antibiotics.

With the rapid rise in drug resistance in many pathogens, new antibiotics are desperately needed. It may be only a matter of time before a wound or scratch becomes life-threatening. Yet few new antibiotics have entered the market of late, and even these are just minor variants of old antibiotics.

Although the prospects look bleak, the recent revolution in artificial intelligence (AI) offers new hope. In a study published in February 2020 in the journal Cell, scientists from MIT and Harvard used a type of AI called deep learning to discover new antibiotics.

When the 2019 Nobel Prize in Economics was announced, it was an exceptional moment because in addition to celebrating contributions to economic science, a methodology was also recognized. The laureates Abhijit Banerjee, Esther Duflo, and Michael Kremer are known for their work in randomized controlled trials (RCTs), which develop evidence-based recommendations for public policy and organizations in the fight against global poverty.

Work with RCTs has led to the identification and implementation of impactful poverty-reduction policies in areas such as skills training and agriculture. But as a methodology, RCTs are not only for the developing world. They have been used for decades in pharmaceutical companies’ clinical trials, and in this age of data, they hold enormous promise to identify business solutions and support evidence-based management.

Walk into any K-5 classroom in Illinois’ Rockford Public Schools, and there’s one thing you’re guaranteed to see: kids playing with Legos. Although it may look like unstructured free time, kids in Rockford are actually hard at work when the Legos are out—building historical homes, constructing ramps, and designing amusement park rides.

Lego play is a critical part of the district’s efforts to introduce science, technology, engineering, and math (STEM) concepts early, and in an engaging way. In 2018, the district began training educators on how to use special kits from Lego Education to teach STEM skills—and, in the process, concepts like cause and effect and problem solving. This school year, teachers are using Legos to help kids learn concepts from all subject areas, including literacy, history, and science. “Just to talk about [STEM concepts] abstractly is difficult at that level,” said Susan Uram, educational technology coordinator for Rockford Public Schools. “But if they can build something... they’re understanding in a concrete way.”

Almost half of Americans work in low-wage jobs despite the nation’s low unemployment rate. Aimee Picchi, writing for CBS News, cites a Brookings study that says “44 percent of U.S. workers are employed in low-wage jobs that pay median annual wages of $18,000.”¹ A Bloomberg story adds, “An estimated 53 million Americans are earning low wages, according to the study. Their median wage is $10.22 an hour and their annual pay is $17,950.”²

These wage levels are not consistent with the United States’ industrial and technological development or its standard of living, but this is far from the only issue. Executives with profit-and-loss responsibility should realize that low wages are also often symptomatic of low profits. Purchasing managers should recognize that a supplier’s low wages are often symptomatic of excessively high prices, even though this seems counterintuitive. The reason is that low wages, low profits, and high prices all have the same root causes: waste (muda) and opportunity costs. Recognizing this simple fact, for which there are proven, off-the-shelf, and simple remedies, opens the door to almost limitless wealth for all stakeholders.

Perhaps for as many as 40,000 years, people have been protecting their feet with some type of covering, initially using animal hides and fur. Today, footwear has become high-tech, sophisticated, and in some cases smart, incorporating sensors that communicate with apps on your phone. Much of the advancement in footwear is possible because of standards that address the basic performance and functionality, allowing manufacturers to go beyond the basics.

There are hundreds of standards for all types of shoes, from industrial work boots to high-heeled dress shoes and everything in between, and for the shoe materials and components. Most of these are published by private-sector standards-developing organizations, such as SATRA, ISO, and ASTM International. But what do I care if my shoes meet any standards? I just want them to look good, feel good, and be fit for my activity—running shoes for jogging, boots for hiking, high heels for dancing, safety shoes for work—that’s all there is to it, right? Not quite. In terms of construction, fit, comfort, functionality, and protection, footwear is probably the most complex of all the clothing that we wear.