Todd Lewis

Zebra Robotics Automation is revitalizing AMR-assisted picking to optimize fulfillment efficiency and productivity with up to 30% fewer robots need in comparison to legacy systems. Their innovative approach can help significantly lower your cost per unit by combining workers and robots into a streamlined partnership that increases throughput without sacrificing performance rates, accuracy or reliability. Maximize AMR-assisted picking efficiency with Zebra’s three-fold strategy: Balanced Utilization: Use low-cost carts for buffering instead of extra robots or labor, reducing costs. Increased Capacity: Carts boost pick density and eliminate AMR wait times, handling 150-300% more items while cutting robot needs by up to 30%. Optimized Workflows: Seamless coordination ensures every picker and robot stays productive—no idle time, no wasted effort.   Download the “Unlocking Fulfillment Potential” eBook here to learn more  

Special Guest Blog Post written by Matt McKinney, Co-Founder and CEO of Loop   I spend most of my time with supply chain and innovation leaders at major enterprises who are sitting on significant AI budgets but struggling to show measurable business impact in an increasingly complex and volatile supply chain environment.   These conversations have evolved dramatically. A year ago, executives were asking basic questions about AI feasibility. Today, the questions have shifted to strategic implementation at enterprise scale.   Based on hundreds of these discussions, three questions consistently emerge that separate companies making transformational progress from those stuck in pilot purgatory.   How do we move from AI experiments to enterprise-scale impact?   Most organizations have yet to see organization-wide, bottom-line impact from AI use. This is the strategic challenge keeping C-suite leaders awake at night.   The problem isn’t the technology. It’s the application of the technology. Too many enterprises are trying to treat AI like a magic wand they can bolt onto existing systems. But garbage in, garbage out. If your underlying data is fragmented and inconsistent, AI won’t solve your problems; in fact they’ll get worse.   At its core, anything automated is powered by…