Dustin Poppendieck’s picture

By: Dustin Poppendieck

On August 29, 2005, I was starting my first semester teaching freshman environmental engineering majors at Humboldt State University in Arcata, California. At the exact same time, Hurricane Katrina hit Louisiana and Mississippi with 190 kph (120 mph) winds and a storm surge in excess of 6 meters (20 feet). Levees failed, flooding more than 80 percent of New Orleans and many surrounding areas. This tragedy left more than 1,800 people dead, many of whom had been trapped in their own homes. It took nearly six weeks for the water to recede, exposing more than 130,000 destroyed housing units.

I spent the rest of the semester (and subsequent ones) discussing with my students the lessons that environmental engineers should learn from Katrina and its aftermath (levees, water treatment, mold, air testing, planning for disasters, and more). Little did I know I would still be dealing with some of the issues revealed by Hurricane Katrina nearly 15 years later as a scientist at the National Institute of Standards and Technology (NIST).

Heather Thompson’s picture

By: Heather Thompson

Software as a medical device (SaMD) is a growing sector in medical device technology. Through the use of artificial intelligence and machine learning, SaMD has the power to influence health on a global scale as well as allow for personalization in medicine and life-saving therapies.

Medical device companies developing these products can take advantage of the FDA’s new programs designed to advance trusted companies so they can get products to market efficiently and effectively.

Equally important, if you want to be part of the SaMD trend and its accompanying regulatory pathway, the FDA is clear: Make sure your quality management system (QMS) is exemplary.

Matt Kunkel’s picture

By: Matt Kunkel

Third-party vendors are increasingly working with their own third parties (fourth and fifth parties), spreading your data across many different vendors. This can make your company an easy target for cybersecurity threats, especially if your organization is a hospital or part of the healthcare system. You have to be aware of the risks associated with third parties. If you aren’t, you’re playing with fire.

Failing to address third-party risk can be costly and often just as damaging as risks that stem from within the organization. Thus, it’s critical that you don’t compromise private data in exchange for the convenience of services provided by third parties. These data are especially important in the healthcare industry because confidential records, clinical information, or medical data could end up in the wrong hands.

Jennifer Chu’s picture

By: Jennifer Chu

In today’s factories and warehouses, it’s not uncommon to see robots whizzing about, shuttling items or tools from one station to another. For the most part, robots navigate pretty easily across open layouts. But they have a much harder time winding through narrow spaces to carry out tasks such as reaching for a product at the back of a cluttered shelf, or snaking around a car’s engine parts to unscrew an oil cap.

Now MIT engineers have developed a robot designed to extend a chain-like appendage flexible enough to twist and turn in any necessary configuration, yet rigid enough to support heavy loads or apply torque to assemble parts in tight spaces. When the task is complete, the robot can retract the appendage and extend it again, at a different length and shape, to suit the next task.

The appendage design is inspired by the way plants grow, which involves the transport of nutrients, in a fluidized form, up to the plant’s tip. There, they are converted into solid material to produce, bit by bit, a supportive stem.

Likewise, the robot consists of a “growing point,” or gearbox, that pulls a loose chain of interlocking blocks into the box. Gears in the box then lock the chain units together and feed the chain out, unit by unit, as a rigid appendage.

ISO’s picture

By: ISO

As artificial intelligence (AI) becomes increasingly ubiquitous in various industry sectors, establishing a common terminology for AI and examining its various applications is more important than ever. In the international standardization arena, much work is being undertaken by ISO/IEC’s joint technical committee JTC 1—Information technology—Subcommittee SC 42—Artificial intelligence, to establish a precise and workable definition of AI. Through its working group WG 4, SC 42 is looking at various use cases and applications. The convener of SC 42/WG 4 is Fumihiro Maruyama, senior expert on AI at Fujitsu Laboratories.

Currently, there are a total of 70 use cases that the working group is examining. Health, for example, is a fascinating area to explore. Maruyama himself describes one use case in which a program undertakes a “knowledge graph” of 10 billion pieces of information from existing research papers and databases in the medical field. The application then attempts to form a path representing the likely development from a given gene mutation to the disease that deep learning has predicted from the mutation.

Chad Kymal’s picture

By: Chad Kymal

With the advent of the internet, cloud, and electronic workflows, what is the future of documented management systems? Do we continue with a structure of quality manual, processes, work instructions, and forms and checklists? How do we imagine the future of documented management systems?

For enterprise and site documentation, there’s a need for all entities, from site to department to individuals, to have their own documented management system structure. The documented management system should be a repository of organizational knowledge, in the form of documentation, records, projects, audits, dashboards, customer and/or interested party needs and expectations, calibration data, and much more. How is this possible?

Furthermore, documented flows should give way to virtual electronic workflows that help implement and sustain an integrated management system.

Jim Benson’s picture

By: Jim Benson

Editor’s note: This is episode two in the Respect for People series. Click here for episode one.

When we build any working system, we need to understand and appreciate how people naturally exchange information. They withhold some things, say some other things. Some of this is fear, some is etiquette, some is politeness, some is avoidance. Can we build systems to actually provide people with the information they need and the right triggers for action?

We were working with a startup with about 160 people. They were listing out the areas of their tech platform they wanted to reengineer. We laid out all the work that could be done and evaluated it for impact, effort, cost, and expertise.

We came up with a lot of results.

We came up with a direction.

But something didn’t feel right. So I drew a box and told them team (about 30 people) to put the stickies with the part of the system that scared them the most, made them lay awake at night, or that they knew would explode and bring the whole thing down one day.

The box soon had three stickies.

There was a pause.

People took two stickies out.

Steve Hawk’s default image

By: Steve Hawk

The company Grace Science was born through an inversion of the normal business sequence. Typically, if an entrepreneur launches a startup and it succeeds, the founders will create a nonprofit, declaring, “We want to give back.” In this case, the nonprofit spawned the startup.

The company’s inception accelerated when Matt Wilsey first met with Carolyn Bertozzi in 2015. Bertozzi is the Anne T. and Robert M. Bass Professor of Chemistry and professor of chemical and systems biology and of radiology (by courtesy) at Stanford University.

Wilsey’s daughter, Grace, has an ultra-rare disorder caused by a mutation in a gene known as NGLY1. Only 54 people in the world have been diagnosed with the disease. In 2014, Wilsey and his wife, Kristen, created a nonprofit, the Grace Science Foundation, in their quest to find a cure. The foundation has raised about $9 million to date.

Larry Emond’s picture

By: Larry Emond

No matter where you’re located, you might think that Schneider Electric is a native company. It’s an easy assumption to make. The €25.7-billion energy, automation, and software solutions company is officially headquartered in France, but its strategy is to localize to the markets it’s in—and it’s in most of them.

Schneider’s localization strategy requires distributed leadership, so the company spreads its top 1,000 leadership roles around the world. Leaders stay in situ for years, which keeps culture universal and decision-making decentralized. This allows for precision responses in differentiated business ecosystems and attracts talent where it lives.

That helps Schneider Electric weather economic storms that drive competitors out of the market, but being the most local of global companies requires leaders who prize diversity and want to become local experts of the whole world—and Schneider’s chief human resources officer (CHRO) Olivier Blum says it’s a strategy that others may have to adopt. As he explains to Gallup’s managing partner Larry Emond in the following conversation, “People don’t want to work in the old model where all decisions have to go back to the global corporate. So localization? Companies really have no other choice.”

Brian Charles’s picture

By: Brian Charles

It’s often difficult to pinpoint the exact moment when things change, but it usually happens faster than one imagines. Old technology gets replaced by new innovations; first by early adopters, and then, suddenly, by everyone. A century ago silent movies reigned, then talkies, and now 3D and virtual reality. What was once a disruptive new technology quickly becomes the norm.

For forward-thinking manufacturers, digital transformation initiatives, and their growing focus on machine health, are fast becoming the norm. I’ll say it out loud: The machine health tipping point is here. It’s no longer possible to avoid putting machine health at the center of your company's digital transformation strategy if you want to succeed through the Foutrth Industrial Revolution.

In the United States, manufacturing is on the rise, with a growth of 3.9 percent in 2019. At the same time, a generational shift is playing in the industrial workforce, as older workers retire and a new generation of manufacturing talent enters the workforce. This generational shift comes with a new mindset, attitude, and eagerness to adopt new technologies that will increase productivity on the shop floor.

Syndicate content