Reducing food waste through predictive analytics and cold chain monitoring: A U.S. Retail Review

Food waste represents a systemic inefficiency in the United States food supply chain, with approximately 30–40% of produced food failing to reach consumers (Buzby et al., 2014). This study examines how machine learning-based demand forecasting and Internet of Things (IoT) cold-chain monitoring have been deployed by major U.S. food retailers and distributors to reduce waste and improve supply chain performance. Drawing on publicly available corporate sustainability reports, industry case studies, and peer-reviewed literature, the analysis evaluates implementations at Walmart, Kroger, Sysco, and FreshDirect. Company-reported outcomes suggest that algorithmic forecasting systems improve prediction accuracy by 20–42% relative to conventional statistical baselines, while continuous IoT-based temperature monitoring reduces cold-chain spoilage rates by 15–30%. These figures, it should be noted, are drawn almost entirely from self-reported disclosures; the absence of independent verification limits the strength of causal claims. Nevertheless, the consistency and scale of reported improvements Kroger's documented 26% reduction in food waste (245,289 tons) since 2017, Walmart's 78% waste diversion rate, and FreshDirect's sub-2% waste generation suggest that technology-enabled distribution represents a viable pathway toward USDA 2030 waste reduction targets. The study also identifies implementation barriers including capital constraints, data interoperability gaps, and workforce skill deficits that may limit uptake among smaller operators. Future research should prioritize independent performance audits and cost-benefit analyses for small- and medium-scale distributors.