Wawa

Wawa operates 1,100 convenience stores across the Mid-Atlantic and Southeast, with fresh food — hoagies, hot beverages, and prepared meals — central to its brand identity. Managing perishable inventory at this scale exposes a structural weakness in traditional replenishment models: static par levels and manual ordering cannot adapt to the hyperlocal demand variability that characterizes convenience retail. With shelf lives measured in hours rather than days, overstocking translates directly to waste while understocking forfeits sales. U.S. retailers collectively lose an estimated $18 billion annually to food waste, and for high-turnover formats with constrained back-of-house storage, the margin pressure is disproportionately severe.

Wawa partnered with Relex Solutions to deploy machine learning-based demand forecasting and automated replenishment across all 1,100 locations simultaneously. The Relex platform ingests multiple data streams — historical sales, local demand patterns, seasonal fluctuations, and store-level operational constraints — to generate granular forecasts that replace manual ordering decisions. By automating the forecasting-to-replenishment workflow, the system eliminates the judgment gaps that typically cause over-ordering of perishables. Deployment at enterprise scale required systematic change management to transition store teams away from manual processes without disrupting daily operations. The solution is architected to optimize for two competing objectives in parallel: maximizing product availability to preserve the guest experience while minimizing spoilage to protect margin.

The initiative covers Wawa's full 1,100-store footprint at launch, establishing a forecasting infrastructure designed to scale to the company's target of 1,800 locations by 2030. Chief Supply Chain Officer Nelson Griffin cited maintaining "high standards of freshness and product availability" as the core operational mandate the system must uphold through this expansion phase. Specific quantitative spoilage-reduction figures have not yet been publicly disclosed, reflecting the early stage of the rollout. Qualitative outcomes include:

• Automated replenishment decisions replacing manual store-level ordering across all locations
• Standardized demand signal processing that accounts for local preference variation by site
• A scalable AI foundation positioned to absorb 700 additional stores over five years

• Specialized AI forecasting tools purpose-built for perishables outperform general-purpose replenishment modules where shelf life constraints require sub-daily optimization.
• Data quality is the critical precondition: clean, normalized historical sales data at the SKU-store level must exist before deploying ML forecasting or model accuracy suffers.
• Enterprise rollout across hundreds of stores demands structured change management — store teams require process retraining alongside system deployment to prevent workarounds.
• Aligning AI investments to expansion roadmaps ensures the platform scales with the business rather than requiring re-implementation at each growth threshold.