Step into the factory of the future. Alicia, an operations manager, sits at her workstation viewing a digitally enhanced video feed of the facility, using cameras installed in strategic locations. Wearing safety gear, a maintenance engineer named Bob checks his tablet for the next machine to fix. Equipped with a headset and controllers, Dave, a software engineer at HQ, serves as a virtual tour guide for Carrie, the company’s lead executive. Wearing an augmented reality (AR) headset, Carrie surveys her machines as she walks through the facility. With Dave’s guidance, she sees digital information, such as a machine’s status, appearing in her view.

Each able to experience a virtual overlay onto a physical environment that provides more context relevant for their jobs, these co-workers can realize their potential as a team through industrial extended reality (XR), an umbrella term that encompasses a spectrum of technologies, from partially immersive AR to completely immersive virtual reality.

This factory might be hard to imagine, but each technology already exists. What’s missing are standard formats, protocols, and guidelines for them to work seamlessly with one another. In other words, the communication channels among these technologies remain shut.

Job satisfaction is important to most people, and yet this can be a fairly nebulous concept that is tricky to achieve and also tough to measure in a meaningful way.

Luckily a number of software platforms designed to manage employee recognition have emerged in recent years, as outlined in this comparison list. The upshot is that it is not just easier for businesses to keep their workers content, but also to track satisfaction and thus extrapolate the morale trajectory for the entire organization.

Here is a look at how these platforms work, why they are useful for keeping team members happy, and how analyzing employee contentment benefits the business as a whole.

The basics

Although they vary in terms of features and functions, all employee-recognition software solutions share the same purpose, which is to codify and unify the way that appreciation for people in a workplace is demonstrated by management.

The old picking methods of paper, pick-to-light, and voice-picking are almost impossible when employees must practice social distancing, use PPE (personal protective equipment), and avoid contact that could potentially exacerbate the spread of Covid-19. One viable solution is pick-by-vision, which both reduces potential contamination and dramatically increases productivity.

Even during the pandemic, the issues of product-picking accuracy and productivity does not magically evaporate. A pick-by-vision system simplifies the workflow and makes it more efficient. Benefits include saving time, reducing the error rate, creating a low-fatigue operation, and strict process control to optimize the picking processes.

This story was originally published by Knowable Magazine.

Imagine a science textbook without images. No charts, no graphs, no illustrations or diagrams with arrows and labels. The science would be a lot harder to understand.

That's because humans are visual creatures by nature. People absorb information in graphic form that would elude them in words. Images are effective for all kinds of storytelling, especially when the story is complicated, as it so often is with science. Scientific visuals can be essential for analyzing data, communicating experimental results and even for making surprising discoveries.

Visualizations can reveal patterns, trends and connections in data that are difficult or impossible to find any other way, says Bang Wong, creative director of MIT's Broad Institute. "Plotting the data allows us to see the underlying structure of the data that you wouldn't otherwise see if you're looking at a table."

And yet few scientists take the same amount of care with visuals as they do with generating data or writing about it. The graphs and diagrams that accompany most scientific publications tend to be the last things researchers do, says data visualization scientist Seán O'Donoghue. "Visualization is seen as really just kind of an icing on the cake."

For manufacturers—as for all of us—the past few months have been a blur of fast adaptations and long periods of waiting. At the start of the pandemic, many manufacturers did what they have always done in the face of disruption: adapt and find the fastest workaround for the challenge at hand.

Manufacturers already know that rapid adaptation is an accepted cost of doing business. Uncertainty, risk, and volatility are not new for these seasoned organizations. However, the universal nature of this crisis, and the fact that it’s far from over, have highlighted areas in which complex quality management systems and procedures stand in the way of agile responses and effective operational optimization.

Many manufacturers have taken advantage of the opportunity to examine their operations with a fresh perspective, seeking proactive changes that will pave the way forward in a post-Covid-19 world.