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.

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.

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.

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.

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.

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.

Adding legs to robots that have minimal awareness of the environment around them can help them operate more effectively in difficult terrain, my colleagues and I found.

We were inspired by mathematician and engineer Claude Shannon’s communication theory about how to transmit signals over distance. Instead of spending a huge amount of money to build the perfect wire, Shannon illustrated that it is good enough to use redundancy to reliably convey information over noisy communication channels. We wondered if we could do the same thing for transporting cargo via robots. That is, if we want to transport cargo over “noisy” terrain, say fallen trees and large rocks, in a reasonable amount of time, could we do it by just adding legs to the robot carrying the cargo—and do so without sensors and cameras on the robot?

Meetings give me a rash. A really bad one. One that not even calamine lotion can soothe. The only things worse than meetings are reports. Standard daily reports, weekly reports, hourly reports. Reports on the status of reports. If I wasn’t already insane, these things would drive me insane.

Take a look at your Outlook for this week (or if you’re a rebel, look at your Lotus Notes). How much time is spoken for already? How little time is left for you to actually get stuff done? Now take a look at your inbox. How many Excel files are in there with their thousands of rows of data that no one will look at until some SVP gets bored, opens one up, and asks a random question about a data point no one has looked at in months? Even more alarming is if you have folders in your Outlook dedicated to storing each of these reports so you can quickly answer said random questions because you have the reports conveniently stored on your laptop. By the way, they’re probably also stored on a shared drive somewhere, so you’ve now doubled the amount of data storage space required for this crap repository.

Fear not, my friends. There is a cure.

You often hear about self-driving cars and their levels of autonomy. When can drivers completely remove their hands from the steering wheel? This also applies to robots. How can robots become fully autonomous?

What are autonomous robots?

Let’s look at the levels of freedom for self-driving cars and draw parallels with the levels of automation in robotic welding.

Level 0: No automation
Just like with self-driving cars at Level 0, everything needs to be done manually. You have a part that must be perfectly pre-assembled without any deviations. Otherwise, robots won’t be able to complete the task. You need to place this part in a precisely defined position, and deviations here are also unacceptable. A highly skilled programmer uses a teach pendant to program every trajectory of the robot’s path.

Level 1: Assistance
At Level 1 of self-driving cars, features like cruise control emerge. Similarly, for robots, individual functions also arise that enable them to adapt to changes in the physical world; for instance, there’s arc sense.

If you could experience the perfect workday, what would you be doing? Have you ever taken the time to think about it? 

Whether you’re an entrepreneur climbing the corporate ladder or you’re selling donuts out of the back of your car, it’s essential to pause and reflect periodically on the work you’re doing.

We can get so caught up in the hustle and bustle of the day-to-day, checking off items on our to-do list, that one day we look up and wonder where we are.

Why are we doing what we’re doing? How’d we get here?

Our work evolves over the weeks, months, and years as we grow our businesses, get promoted, or change roles. If we forget to notice how we feel about our work as we evolve and grow as professionals, we might end up feeling empty or unfulfilled.

The more your workdays reflect your current needs and desires, the more satisfied you’ll be. And the only way we can make sure we do this at every stage of our journey is to pause, reflect, and recalibrate.

How? With the Perfect Workday Exercise.

Step 1: Build your list

Grab a blank sheet of paper. Draw a line down the middle, creating two columns. At the top, label the left side “My Typical Workday,” and the right side, “My Perfect Workday.”