Walmart logistics manager Leo Garcia built an AI tool that helps truck drivers find the best available loads on their routes home, reducing delays and inefficient empty-mile driving.
Using Walmart's in-house coding agent, the tool analyzes hundreds of truckloads in the Chicago region and flags the top options based on location and timing, allowing drivers to get home faster while keeping trucks loaded with merchandise.
What happened
Leo Garcia, a regional load manager at Walmart in the Chicago area, created a tool using Walmart's in-house coding agent called Code Puppy that analyzes available truckloads in the region and flags the best options based on location, timing, and other factors. The tool has already helped drivers stay on schedule—for example, it once found an alternative load five miles away when a scheduled pickup would have delayed a driver by three hours.
Why it matters
Garcia learned AI fundamentals through a Google AI certification course available to Walmart employees, then applied that knowledge to solve a real operational problem he faced daily. By helping drivers reach home on time without leaving trucks empty, the company reduces inefficient empty miles while improving driver satisfaction—something Garcia understands personally from his own five years as a truck driver before moving into management.
What to watch
The tool is currently being tested for wider use across the organization, and like many employee-made projects at Walmart, it could be evaluated and distributed to other regions. Garcia said the tool has not yet been rolled out company-wide, but the system's ability to quantify routing options in seconds suggests potential for scaling.
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