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For Nvidia, the future may be in leveraging digital twin technology.

The company announced Monday at the Consumer Electronics Show (CES) 2025 that it had created technology that would allow supply chain companies to create digital twins of their warehouses or industrial factories, in turn enabling them to simulate and test varying robotics capabilities virtually.

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The new technology is an Omniverse Blueprint, called Mega. Nvidia’s Omniverse allows users to put together world scenes digitally, simulating a physical environment. The blueprints are reference workflows for AI-powered robotics applications.

The company expects that Mega will help encourage more efficient outcomes in the physical warehouse. It could be particularly impactful in warehouses that employ a fleet of hundreds of robots with varying functions.

While many warehouses are only beginning to adopt robotics and automation, others with high levels of maturity leverage different models of robots to complete various jobs—from picking and packing, to taking stock of inventory, to helping with receiving incoming goods and more. In those more advanced cases, it can be difficult to understand how the robots and systems can best work in tandem for improved efficiencies and cost savings.

But Nvidia wants to change that. With Mega, companies can simulate course availability—otherwise thought of as the path non-stationary robots take to complete their tasks—and can include human workers in the digital landscape, ensuring that—particularly in the case of humanoid robots that work side by side with warehouse employees—the synergy between humans and technology works without snafus.

Madison Huang, director of product and technical marketing for Nvidia Omniverse, said the company has worked to meet the needs of logistics-focused customers powering the supply chain.

“In warehousing and distribution, operators face highly complex decision optimization problems—matrices of variables and interdependencies across human workers, robotic and agentic systems and equipment,” Huang wrote in a blog post.

Clients can simulate multiple scenarios at a time, and Mega tracks the movement and decisioning of each individual robot’s digital twin to share aggregated data about expected key performance indicators like task completion time, warehouse throughput, fleet efficiency, space utilization percentage and more.