Preventing Waste with AI Addressing the “lack of specificity” in c-store data, where many items are sold under a single price look up (PLU), Bonnie Zaring , executive director of food programs and offers at RaceTrac, posed a critical challenge: “We sell a lot of things as a one. And some- times being able to take that and make it actionable when you don’t actually know what was in the cup, or you don’t actually know when it was all rung up the same item.” Brask provided a solution, explaining how waste data can be used to “back into” the vari- eties for items like roller grill products or doughnuts sold under a single PLU, even though customers are choosing from multiple flavors or varieties. By lever - AI is a frequent topic within Vision Groups. Here are Vision Reports from 2025 that relate to AI, especially in foodservice. GCVG Vision Report Practical AI Applications in Convenience Retailing , August 2025 CxVG Owning the Numbers: Data-Driven Decisions from Strategy to Store , May 2025 GCVG Do You Want Chips With That? , April 2025 CLVG Lead the Future: Transformative AI Strategies , March 2025
aging waste data, Brask noted, AI can back into those varieties, predicting, for example, that while a store may sell 400 doughnuts in total, waste capture data can be used to break that down into forecasts for specific flavors. He added that this approach is especially valuable for c-store part- ners managing items like roller grills, doughnut cases, or steam tables, where products are often sold by weight under one code. Poye noted that this method could also provide insight into shrink. By comparing waste capture with sales data, retailers could identify where losses occur, whether on pizza, roller grill items, or other categories. In this way, AI not only supports more accurate forecasting but also highlights opportuni- ties to reduce shrink and improve efficiency.
Weber acknowledged that while there isn’t a direct answer yet, the future holds promise for AI to leverage imagery from security cameras to answer broader business questions beyond simple production forecasts. He envisioned AI looking at the “total store” to address these “much bigger questions.” The discussion also touched on AI’s potential for portion sizing, as Roy asked Weber and Brask if AI could help understand the gap between what consumers think they want and what they actually consume, thereby reducing post-production food waste. Brask shared an experiment conducted with a partner on pizza production, where whole pizzas were cooked and sold by the slice. The part- ner wanted to test whether shifting from batching
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