International Journal of Scientific Research and Engineering Development

Post-harvest loss remains a critical challenge in the U.S. agricultural supply chain, undermining both food security and sustainability objectives. Despite technological advancements in farming practices, inefficiencies in storage, transportation, and distribution account for significant product deterioration and waste. This paper investigates how engineering management approaches, particularly process optimization, lean methodologies, supply chain modeling, and predictive analytics, can be applied to minimize post-harvest losses within U.S. agriculture. The proposed framework integrates industrial engineering tools with emerging digital technologies, including the Internet of Things (IoT), blockchain, big data analytics, and cyber-physical systems, to enable real-time monitoring, traceability, and decision-making across supply chain nodes. By leveraging value stream mapping, simulation modeling, and operations research methods, the research identifies bottlenecks in harvesting, storage, and logistics, and demonstrates how targeted interventions can reduce inefficiencies. Case-based analysis and simulation experiments suggest that integrating engineering management with smart supply chain practices can reduce post-harvest loss by 15–20%, improve resource utilization by up to 25%, and enhance supply chain resilience against disruptions such as climate variability and labor shortages. Furthermore, the paper highlights the potential role of policy incentives, training programs, and collaborative platforms in scaling these practices across diverse farm sizes. The findings contribute to ongoing discourse on sustainable agriculture by providing a structured, engineering-driven roadmap for minimizing waste, improving food system efficiency, and strengthening national food security.