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.

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.

‘I have been offered a significant increase in salary by another employer and am giving my two-week notice.”

My guess is that this is the most common reason given when employees quit their current job. But is salary the real reason most employees quit? I have always suspected and believed that, given a fair salary, people do not quit their jobs for money. So why do they leave?

I was recently drawn to explore this topic a little more deeply after reading an article about IBM Watson’s latest feat. A new, proprietary IBM AI algorithm can predict with 95-percent accuracy which workers are about to quit their jobs. The algorithm has been successfully deployed to predict IBM employees who are a flight risk and then to propose actions to managers to engage and retain those employees. Exploring a little further the topic of employees quitting, I discovered a recent Harvard Business Review blog by Jon Christiansen. Through looking at 15 years of data, Christiansen identified eight reasons he believes employees quit.

As usual with Quality Digest’s diverse audience, this year’s top stories covered a wide range of topics applicable to quality professionals. From hardware to software, from standards to risk management, from China trade to FDA regulations. It’s always fun to see what readers gravitate to, and this year was no different.

Below are five articles that garnered a lot of interest from our readers. As you can see, the topics are quite diverse.

Improve Risk Management and Quality Across the Value Chain by Increasing Visibility
by Kelly Kuchinski

Eric, a 40-something married father of three, runs a successful startup. Given his demanding career, he and his wife decided she would be a stay-at-home mum. Eric believed the attention he devoted to his family was adequate, and that he had fully harmonized his work as CEO and life as a family man.

On a recent family trip, Eric continued working as much as he could, as he always did. While taking a conference call, he dropped his phone and, without hesitation, leapt to catch it at the risk of hurting himself. Seeing this, his 13-year-old son blurted out, “I don’t know if you would have jumped after me like that.” Only then did Eric realize that his son didn't think he prioritized their family. Eric had been oblivious that his family felt neglected; he had been unaware or was in denial.

An underlying theme emerged from the Stanford Institute for Human-Centered Artificial Intelligence’s fall conference: Artificial intelligence (AI) must be truly beneficial for humanity and not undermine people in a cold calculus of efficiency.

Titled “AI Ethics, Policy, and Governance,” the event brought together more than 900 people from academia, industry, civil society, and government to discuss the future of AI (or automated computer systems able to perform tasks that normally require human intelligence).

Discussions at the conference highlighted how companies, governments, and people around the world are grappling with AI’s ethical, policy, and governance implications.

How will the United States’ withdrawal from the Paris Agreement affect greenhouse gas emissions? Quality Digest editor in chief Dirk Dusharme and Mike Richman, principal at Richman Business Media Consulting, point out that most manufacturers already recognize that waste, including waste of energy as represented by carbon emissions, costs the supply chain money.¹ This leads to my conclusion that withdrawal from the agreement will not have any significant effect on U.S. carbon emissions.

Involving relevant interested parties

It is a basic principle of ISO 9001:2015 that organizations must identify the needs and expectations of their relevant interested parties, but not all interested parties are relevant. The Paris Agreement offers little identifiable value to organizations, so it is not a relevant stakeholder. Neither are investment banks that had hoped to profit from cap-and-trade mandates.² The supply chain should contain nothing that does not deliver value to the other supply chain participants.

When my kids, ages 11 and 8, bang through the back door after school, often the first thing out of their mouths is: “Mom! Can we play Prodigy?”

After a quick mental calculation of how much screen time they've already had for the week and how much peace and quiet I need to finish my work, I acquiesce. After all, Prodigy is a role-playing video game that encourages kids to practice math facts. It’s educational.

Right?

Though video games are increasingly making their way into classrooms, scientists who study them say the data are lacking on whether they can actually improve learning—and most agree that teachers still outperform games in all but a few circumstances.

But there is growing evidence that some types of video games may improve brain performance on a narrow set of tasks. This is potentially good news for students, as well as for the millions of people who love to play, or at least can’t seem to stop playing (see infographic).

“There is a lot of evidence that people—and not just young people—spend a lot of time playing games on their screens,” says Richard Mayer, an education psychology researcher at the University of California, Santa Barbara. “If we could turn that into something more productive, that would be a worthwhile thing to do.”

In 1500, China’s economy was the strongest in the world. But by the 19th century, the United States, Western Europe, and Japan had leapfrogged over China by churning out goods and services in vast quantities while the former superpower stalled.

Why? Some economists argue that China’s lack of free markets and unencumbered innovation in the West led to the shift. But what is the relationship between innovation and markets, productivity, and inequality?

The answer to that puzzle and others were explored during a recent forum on the relationship of innovation to economic growth at the Hoover Institution. Three Stanford professors, all Hoover fellows—Stephen Haber, Edward Lazear, and Amit Seru—spoke on a panel moderated by Jonathan Levin, dean of Stanford Graduate School of Business.

How do we get smart? I was first asked this question while sitting in on a call with a small manufacturer who supplied parts for automotive manufacturers. With all the buzz around “smart manufacturing,” this manufacturer wanted to join the movement. The problem was that its leaders didn’t know where to start.

They bought sensors without any real understanding of what to do with them. When I asked why, they said they didn’t have any useful data and they needed data to be “smart.” I asked them, “How do you not have any useful data?” They said they only had documents capturing the history of each maintenance event in the facility. These “textual maintenance work orders” were hardly useful due to their unstructured, jargon-filled nature. At the time they were right; these data in its natural form seemed useless.