Wawa is embedding machine learning into its fresh food supply chain, aiming to reduce spoilage and keep popular menu items consistently in stock. The Pennsylvania-based convenience store chain has partnered with Relex Solutions, an AI-powered retail planning platform, to automate manual forecasting and replenishment processes across its 1,100 locations. The move comes as Wawa accelerates nationwide expansion and targets 1,800 stores by 2030.
AI Steps Into the Fresh Food Challenge
Fresh food accounts for some of Wawa’s highest sales volumes but also its greatest waste risks. Hoagies, salads, wraps, and other short-shelf-life products can quickly erode margins when demand is overestimated or replenishment cycles run long. Relex’s system integrates store-level sales data, historical demand patterns, and external variables such as day-of-week trends to predict replenishment needs more precisely.
The platform’s algorithms are designed to address the volatility of fresh food demand, ensuring stock levels meet peak traffic without triggering excess inventory that leads to spoilage. Nelson Griffin, Wawa’s chief supply chain officer, said the technology will help sustain both freshness standards and availability as the company scales its store network.
Following a Broader Retail Trend
Wawa’s move follows similar AI adoption by Circle K, Kwik Trip, and Casey’s General Stores, which have also turned to Relex to streamline inventory decisions in high-turnover categories. Industry data shows food waste accounts for up to 10% of operating costs in some convenience formats, a figure that AI-based planning tools are increasingly targeting.
By using store-specific demand signals rather than static replenishment rules, retailers can not only cut waste but also boost sell-through on premium fresh products, which often drive higher margins. For Relex, the Wawa partnership expands its footprint in a competitive U.S. c-store segment that is increasingly defined by fresh food quality as much as fuel sales.
Looking Beyond Spoilage Reduction
While the headline benefit is cutting waste, the deeper opportunity lies in using forecasting data to shape menu design, supplier commitments, and even in-store labor scheduling. Retailers that turn replenishment insights into upstream planning decisions can create a tighter feedback loop between what sells, what’s made, and what’s ordered, a shift that turns AI from a cost-saving tool into a growth lever.