After nearly 130 years in business and a series of breakthrough innovations that shaped the way we light up our homes, General Electric has sold its lighting division to the U.S.-based market leader in smart homes, Savant, for a reported $250 million (£198 million). Although a licensing agreement means that consumers will continue to see GE-branded light bulbs in stores, the sale marks the end of an era for this quintessential giant of the illumination industry.

GE traces its roots to Thomas Edison’s invention of the electric light bulb in 1879. Since then, GE Lighting and its direct legal predecessors have shaped illumination technology like no other company: building on Edison’s legacy, the company went on to patent the tungsten filament in 1912 and the first practical fluorescent tubes in 1927.

Throughout human history we’ve constantly sought out tools and capital to make us more productive. From the formation of basic tools to assist in farming to real cultivation and shaping of the land for greater yields, humankind learned to grow food. Further research into genetics, fertilizers, and pesticides enabled us to rapidly scale food production. From early sweatshops to almost fully automated factories, we’ve learned how to scale manufacturing and get far more productivity from fewer workers and more machinery and automation.

In this manner, we’ve learned to improve the deployment of human labor, land, tools, machinery, and other capital to improve our quality of life. Now, we must fully engage the asset that we have the most of that is producing the least for us: data. It’s time to put our data to work.

‘What’s the MTBF of a human?” A bit of a strange question I ask in my Reliability 101 course. Why ask such a weird question? I’ll tell you why. Because MTBF is the worst, most confusing, crappy metric used in the reliability discipline.

OK, maybe that statement is a smidge harsh, but it does have good intentions because the amount of damage done by misunderstanding MTBF is horrendous.

MTBF stands for “mean time between failure.” It is the inverse of failure rate. An MTBF of 100,000 hours/failure is a failure rate of 1/100,000 fails/hour = .00001 fails/hour. Those are numbers; what does that look like in operation?

Does it mean:
The product lasts 100,000 hours before failing?
Half the population fails by 100,000 hours?

Wait a minute! Our product is only supposed to last three years with a 50-percent duty cycle. That’s 13,140 hours of use. Why would we have an MTBF goal of 100,000 hours? It can’t even run that long if everything goes perfectly.

Robert Siegel has peered into the post-Covid-19 future and concluded that anyone hoping for a quick recovery is likely to be disappointed. Which means a great many businesses will fail.

“We can say that with 1,000-percent certainty, and there are many reasons why,” says Siegel, a lecturer in management at Stanford Graduate School of Business.
First, he says, a vaccine almost certainly won’t be widely available for at least a year. In the interim, restaurants, airlines, and hotels are going to be running well below capacity.

“There’ll be fewer jobs, and fewer jobs means less money flowing into the economy,” he says. “It’s impossible for things to bounce right back.”

As a general partner at XSeed Capital and a venture partner at Piva, Siegel researches strategy and innovation in companies of all sizes, with an emphasis on technology. Stanford Business asked a few questions about what good leaders should do if the current pandemic proves to be an extinction event for their firms.

This story was originally published by Knowable Magazine.

From mask wearing to physical distancing, individuals wield a lot of power in how the coronavirus outbreak plays out. Behavioral experts reveal what might be prompting people to act—or not.

As a fluid dynamicist and mechanical engineer at the National Institute of Standards and Technology (NIST), I’ve devoted much of my career to helping others see things that are often difficult to detect. I’ve shown the complex flow of air that occurs when a dog sniffs. I’ve helped develop ways to detect drugs and explosives by heating them into a vapor. I’ve explored how drug residue can contaminate crime labs. I’ve even shown how to screen shoes for explosives.

Most of these examples fit into a common theme: detecting drugs and explosives through the flow of fluids that are usually invisible. When I’m in the laboratory, I use a number of advanced fluid flow-visualization tools to help better understand and improve our ability to detect illicit drugs and explosives on surfaces, on people, and in the environment.

The daily Covid-19 pandemic values tell us how things have changed from yesterday, and give us the current totals, but they are difficult to understand simply because they are only a small piece of the puzzle. This article will present a global perspective on the pandemic and show where the United States stands in relation to the rest of the world at the end of the third week in June.

Here we will consider 27 countries that are home to 5 billion people (67% of the world's population). According to the European CDC database, which is the source for all of the data reported here, these 27 countries had more than 75 percent of the world’s confirmed Covid-19 cases and 86 percent of the Covid deaths as of June 20, 2020. So they should provide a reasonable perspective on the worldwide pandemic. Figure 1 lists these countries by region and gives the relevant Covid-19 counts and rates as of June 20, 2020.

Figure 1: Countries used for global summary

As the coronavirus wreaks economic turmoil around the world, our modern supply chains are facing unprecedented stress. For months prior to the Covid-19 crisis, trade tensions had been mounting due to the escalating tariff war between Washington and Beijing. A rise in protectionism, coupled with concrete costs and new financial barriers, has fueled broader challenges and concerns for worldwide logistics networks. Against this backdrop, our modern supply chain infrastructure is well overdue for a rethink.

Today’s globalized supply chain networks have been optimized to identify minimum lead times at the lowest possible costs. However, rapid political developments, extreme climate events, and now a global pandemic have all revealed the hidden costs of single-source dependencies and poor flexibility in adapting to real-time shocks, with fast changes to supply and demand. During the next several years, as we undertake a broader overhaul of our logistics infrastructure, I believe that a new order will emerge based on three key dimensions.

The U.S. House of Representatives has passed the HEROES Act (Health and Economic Recovery Omnibus Emergency Solutions Act)¹ which will, if approved by the Senate and president, require OSHA to develop a standard for workplace protection against Covid-19.

Under section 120302 the legislation says specifically (emphasis is mine):

“(a) EMERGENCY TEMPORARY STANDARD

(1) In general—in consideration of the grave danger presented by COVID-19 and the need to strengthen protections for employees, notwithstanding the provisions of law and the Executive orders listed in paragraph (7), not later than 7 days after the date of enactment of this Act, the Secretary of Labor shall promulgate an emergency temporary standard to protect from occupational exposure to SARS-CoV-2—

(A) employees of health care sector employers;
(B) employees of employers in the paramedic and emergency medical services, including such services provided by firefighters and other emergency responders; and
(C) other employees at occupational risk of such exposure. ...

A new report from Trend Micro Research illustrates how advanced hackers are using unconventional attack vectors against smart manufacturing environments. Smart manufacturing technology generally operates through proprietary systems, which use their own proprietary language. However, these systems also still run on the computing power of traditional IT systems. While typically designed to function independently from other systems, it’s this particular loophole that leaves these systems vulnerable to IT threats.

“Past manufacturing cyberattacks have used traditional malware that can be stopped by regular network and endpoint protection,” says Bill Malik of Trend Micro. “However, advanced attackers are likely to develop operational technology-specific attacks designed to fly under the radar. As our research shows, there are multiple vectors now exposed to such threats, which could result in major financial and reputational damage for Industry 4.0 businesses. The answer is IIoT-specific security designed to root out sophisticated, targeted threats.”