Kelley Jacobsen’s picture

By: Kelley Jacobsen

In the wake of the Covid-19 pandemic, medical device supply chains are one of the top priorities for health system leaders. Amid supply chain disruptions during the pandemic, hospitals scrambled to find enough devices to keep up with unprecedented demand. The global crisis revealed gaps in standard operating procedures for how the healthcare industry manages supply chains to keep medical devices functioning. Now, while many supply chain issues linger, health systems also face surging costs at a time when operating margins are tighter than ever.

To successfully navigate existing supply chain challenges and inflation, health systems should have a process with rigorous quality standards and an in-depth understanding of their inventory and ecosystem of suppliers. This will give health systems more predictability, the ability to better control costs, and quality assurance to reduce risk.

Emily Newton’s picture

By: Emily Newton

Pumping systems are critical to many manufacturing operations. Consequently, any errors with this equipment can be costly. So, it’s important to catch oddities as soon as they occur. The internet of things (IoT) and artificial intelligence (AI) enable that kind of pump anomaly detection.

Industrial pumps are relatively straightforward machines when you break them down. But detecting errors in them is often less simple. That can scare manufacturers away from advanced monitoring systems. But the benefits are too significant to ignore.

Why manufacturers need pump anomaly detection

Regardless of how reliable a pumping system is, it will eventually fail. Without a way to detect errors early, that failure might not become evident until it’s grown into a sizable, costly issue. The resulting downtime costs oil and gas companies an average of $500,000 an hour, more than double what it was two years ago.

Mike Figliuolo’s picture

By: Mike Figliuolo

Stupid stuff is going to happen. Expect it. It’s part of our complex and highly interdependent world. People will make mistakes. A lot of them. Layer on top of that some incredibly politically charged cultures (for more on that subject see “Hot Heads and Karma”), and there’s an abundance of potentially bad outcomes.

There will be times where stupid stuff will happen to you (and yes, there are times you will do stupid stuff—accept it as axiomatic). When really stupid stuff happens, it can feel like others are conspiring against you. It can seem like a plot of Oliver Stone proportions. During those situations, the outcomes are mostly binary: You defuse the situation or it blows up, sometimes causing irreparable harm to your career or reputation. You may even have an urge to attack the responsible individual. Doing so can create a situation like backing a rabid ferret into a corner. When you create that dynamic, they’re most likely to attack back—and rabid ferrets can be nasty!

There are ways to defuse these events simply by adopting the right mindset.

Jones Loflin’s picture

By: Jones Loflin

I’ve been dabbling in AI (or ChatGPT as some of you may call it) for about eight months. I’ve used it to strengthen blog titles, make content “punchier,” and help me get some creative juices flowing.

However, by far my favorite thing to do with it is to ask it to explain a complex topic to a 5-year-old. From bias to creative avoidance, this tactic has given me a stronger foundation from which to share ideas with others.

There has been one concept I’ve hesitated to ask it to explain, partly because I thought (in full transparency) it might do a better job than me, and my pride would be hurt.

My curiosity got the best of me yesterday, and I finally worked up the courage to ask ChatGPT to “Explain work-life balance to a 5-year-old.”

The exact response it gave me

Hey there, kiddo! Today, let’s talk about something super important called “work-life balance.” Now, you might be wondering, what on earth is that? Well, it’s all about finding a good balance between our work or jobs and the other fun things we do in our lives.

Donald J. Wheeler’s picture

By: Donald J. Wheeler

Many articles and some textbooks describe process behavior charts as a manual technique for keeping a process on target. For example, in Norway the words used for SPC (statistical process control) translate as “statistical process steering.” Here, we’ll look at using a process behavior chart to steer a process and compare this use of the charts with other process adjustment techniques.

Process behavior charts allow us to detect process upsets. Clearly, when we have an upset it’s important to get things operating normally again, so it’s natural to think of using process behavior charts to make adjustments to keep the process at a desirable level. When thinking in this manner, it’s natural to unconsciously insert a hyphen between the last two words to obtain “statistical process-control.” And the hyphen changes the meaning. Instead of a nominative phrase referring to a holistic approach to analyzing observational data, the hyphen changes SPC into a process-control algorithm that uses statistics.

Multiple Authors
By: Elizabeth Z. Johnson, Michael Platt, Vartika Parasramka, Victoria Villacorta, Emily Foy, Natalie Richardson

Countless management and HR blogs, articles, and books are packed with advice about best practices for improving workplace culture, making teamwork more effective, ways to stay on task, and methods to get the most out of meetings. In parallel, organizations often query employees with self- and peer assessments to better understand employee engagement. So why don’t those approaches always work?

Most organizations don’t take a neuroscience perspective into account. What people can and are willing to self-report doesn’t always predict their behaviors, decisions, and outcomes. Moving the needle requires getting neuroscience out of the lab and measuring neural activity in the real world and in real contexts. That is, we need to measure our brains while we do work at work, quite literally.

NIST’s picture

By: NIST

Manufacturing Day, or MFG Day, has grown to mean many things since it was officially proclaimed in 2012. Some celebrate on the first Friday in October with an event at a manufacturing facility or a school. Others participate in a regional celebration at an events center. Some areas have a Manufacturing Week, with multiple touch points, while others celebrate Manufacturing Month.

No matter how it’s being celebrated, we can all agree that MFG Day is a great rallying point to change people’s perceptions about manufacturing and promote careers that depend on creativity, problem solving, teamwork, and technology.

Oak Ridge National Laboratory’s picture

By: Oak Ridge National Laboratory

A team of scientists with the U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL) has investigated the behavior of hafnium oxide, or hafnia, and its potential for use in novel semiconductor applications.

Materials such as hafnia exhibit ferroelectricity, which means that they are capable of extended data storage even when power is disconnected, and might be used in developing new, so-called nonvolatile memory technologies. Innovative nonvolatile memory applications will pave the way for creating bigger and faster computer systems by alleviating the heat generated from the continual transfer of data to short-term memory.

The scientists explored whether the atmosphere plays a role in hafnia’s ability to change its internal electric charge arrangement when an external electric field is applied. The goal was to explain the range of unusual phenomena that have been obtained in hafnia research. The team’s findings were recently published in Nature Materials.

William A. Levinson’s picture

By: William A. Levinson

The difference between common (or random) cause and special (or assignable) cause variation is the foundation of statistical process control (SPC). An SPC chart prevents tampering or overadjustment by assuming that the process is in control, i.e., special or assignable causes are absent unless a point goes outside the control limits. An out-of-control signal is strong evidence that there has been a change in the process mean or variation. An out-of-control signal on an attribute control chart is similarly evidence of an increase in the defect or nonconformance rate.

The question arises, however, whether events like workplace injuries, medical mistakes, hospital-acquired infections, and so on are in fact due to random or common cause variation, even if their rates follow binomial or Poisson distributions. Addison’s disease and syphilis have both been called “the Great Pretender” because their symptoms resemble those of other diseases. Special or assignable cause problems can similarly masquerade as random or common cause if their metrics fit the usual np (number of nonconformances) or c (defect count) control charts.

Multiple Authors
By: Matthew T. Hughes, Srinivas Garimella

Not only people need to stay cool, especially in a summer of record-breaking heat waves. Many machines, including cellphones, data centers, cars, and airplanes, become less efficient and degrade more quickly in extreme heat. Machines generate their own heat, too, which can make hot temperatures around them even hotter.

We’re engineering researchers who study how machines manage heat and ways to effectively recover and reuse heat that is otherwise wasted. There are several ways extreme heat affects machines.

No machine is perfectly efficient; all machines face some internal friction during operation. This friction causes machines to dissipate some heat, so the hotter it is outside, the hotter the machine will be.

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