Operations | Monitoring | ITSM | DevOps | Cloud

The scarcity of labor resources and high costs of robotics ownership are pushing fulfillment companies to rethink their automation strategies. Zebra addressed these challenges head on in a recent Making the Case report published in collaboration with Modern Materials Handling. This report delves into the top challenges fulfillment operations are dealing with on the robotics front today.

As we mark the last day of National Nurses Week in the U.S., Zebra’s Chief Nursing Information Officer and Healthcare Industry Principal, Kassaundra McKnight-Young offers an inspiring glimpse into the evolving role of technology in healthcare. Transitioning from over 31 years on the frontline, she remains dedicated to enhancing patient care by integrating innovative solutions that empower clinicians.

Although there’s a growing understanding of how autonomous mobile robots (AMRs) can reduce warehouse travel, we need to be asking a much more important question. How can a real-world operation transform the efficiency of its fulfillment workflows by optimizing both robots and people? If you’ve ever talked to anyone on our team in person, you know this is a challenge we’re passionate about solving.

The good news: this is no longer the case with our new “How to Buy” experience!. The case is clear: B2B buyers want the same seamless online shopping, research and purchasing experience they enjoy as consumers. They expect that process to be easy, smart and empowering. Business buyers are also banking on features and functionality that are tailored for them, especially in the small-and-medium business (SMB) segment.

In the world of business, AI is like a superhero - but even superheroes need the right tools to do their job. To make AI truly effective, it’s important to pair it with automated data capture across your operations. Just like us, AI needs a complete picture to make smart decisions and avoid mistakes. Without all the details, it can’t spot patterns, catch defects, or find hidden inefficiencies.

I was recently asked this question on an episode of the Industrial Automation Insider podcast. I immediately called out the need to look closer at cloud-based training of the AI models that serve as the decision engines within these automation systems. Why haven’t more manufacturers, warehouse operators, or healthcare providers jumped on this opportunity? They’re all using machine vision systems in various ways, often across multiple sites that are geographically distant from one another.