Fresht

Fresht is a term used to describe systems and practices that monitor and optimize the freshness of perishable goods across the supply chain. The concept emphasizes real-time data, predictive analytics, and automated actions to minimize spoilage and ensure product quality. Although not a single standardized platform, fresht is used in literature and industry to refer to integrated approaches that combine sensors, data networks, and analytics to manage freshness.

Core technology in fresht approaches typically includes Internet of Things sensors that measure temperature, humidity, and door events, as well as specialized sensors or imaging techniques to assess product condition. Computer vision can evaluate appearance and color, while machine learning models are used to predict remaining shelf life and optimal timing for restocking or disposal. Data from these components is aggregated in a centralized platform, providing dashboards, alerts, and API connections to warehouse management systems, enterprise resource planning, and point-of-sale systems. Implementations also address data integrity, privacy, and security considerations.

Applications of fresht concepts span multiple sectors, with common use cases in cold-chain monitoring for dairy, meat, and seafood; fresh produce tracking; restaurant supply management; and e-commerce grocery operations. The goal is to reduce food waste, improve inventory turnover, ensure safety and compliance, and provide traceability for product quality from farm to consumer.

History and reception of fresht ideas trace to scholarly and industry discussions from the early 2010s, gaining practical traction through pilots and pilot-to-scale deployments in the late 2010s and early 2020s. Supporters highlight waste reduction, safety improvements, and enhanced consumer confidence, while critics point to the upfront costs of sensors and infrastructure and concerns about data quality and interoperability. Adoption varies by sector and organization size.