Walmart runs roughly 10,500 stores and dozens of distribution centers globally. Managing that at scale means that every day, thousands of decisions get made about where products sit, how they move, and what happens when something goes wrong. What has changed is that those decisions are increasingly being made, or at least prompted, by software rather than planners.
The centrepiece of this is a digital twin: a virtual replica of the physical logistics network that updates in real time. Think of it as a living map of the entire operation, one that can run simulations. If a warehouse closes unexpectedly, or a weather event disrupts a key transport route, the system models the knock-on effects and suggests what to do. Staff review those suggestions and act on them.
This is not a new concept for Walmart. The company has been using digital twins to plan distribution centres for years. What has expanded is the scope. Walmart CEO John Furner confirmed in January 2025 that digital twin technology is now in place across more than 1,700 locations. The practical results are measurable: the system can predict refrigeration equipment failures up to two weeks ahead, which allowed Walmart to cut emergency maintenance alerts by 30% and reduce refrigeration spend by 19% in a year.
On the delivery side, Walmart's AI-driven route planning has eliminated 30 million unnecessary driving miles and avoided 94 million pounds of carbon emissions. That same technology is now available to other businesses. Walmart has packaged it as a paid software service, called Route Optimization, sold through its commerce technology arm. Any company with a delivery operation can buy access to it.
This matters beyond Walmart's own operations because it signals where the floor is moving. When the world's largest retailer solves a hard logistics problem and then sells the solution to others, smaller operators no longer need to build from scratch. The capability gap that once separated large and small players narrows.
The broader supply chain industry is moving in the same direction, but more slowly. Active AI use among retail supply chain leaders has risen from 24% to 40% in two years. Yet only about one in five organisations globally has deployed AI at meaningful scale across multiple teams. The main barriers are not technical: over half of supply chain executives cite a lack of internal expertise and difficulty getting teams to trust and act on AI recommendations.
That trust problem is real. A demand forecast that planners ignore is worth nothing. Getting staff comfortable with acting on system recommendations, rather than relying on personal judgment built over years, is an organisational challenge that no software solves on its own.
For any business that moves physical goods, the Walmart story points to something specific. The companies pulling ahead are not just buying AI tools. They are building a data foundation: clean, connected records of what they have, where it is, and how it moves. Without that, no simulation runs accurately. The Walmart digital twin works because the underlying data is reliable and feeds in from sensors, point-of-sale systems, weather feeds, and transport networks simultaneously.
For operators who are not yet at that stage, the starting point is simpler than it sounds. Map your highest-cost disruptions from the past two years. If any of them were predictable in hindsight, that is where a focused AI or simulation tool pays back fastest. The full Walmart approach took years to build. The practical question for most operators is which single part of their network would benefit most from being able to run a "what if" before the crisis arrives.