Optimizing Supply Chain Logistics with Big Data and AI: Applications for Reducing Food Waste

Food waste is a critical global issue with far-reaching economic, environmental, and social implications. Supply chain inefficiencies, such as improper demand forecasting, inadequate storage, and transportation delays, significantly contribute to food loss. This research explores how Big Data analytics and Artificial Intelligence (AI) can optimize supply chain logistics to mitigate food waste. By leveraging advanced predictive models, machine learning algorithms, and real-time data processing, this study aims to identify critical areas of waste, enhance decision-making, and improve overall supply chain efficiency.

The paper examines current applications of AI and Big Data in the food supply chain, focusing on predictive demand analytics, inventory management, and route optimization. Case studies from leading industries illustrate the transformative potential of these technologies, highlighting their role in reducing food spoilage and improving sustainability. Additionally, the study evaluates challenges such as data integration, scalability, and implementation costs, offering practical solutions to overcome these barriers.

Through a systematic analysis of field data and simulation models, this research demonstrates that adopting AI-driven approaches can reduce food waste by up to 30% in supply chains. Key findings include a significant reduction in lead times, improved freshness of perishable goods, and a measurable decrease in carbon footprint. The study concludes by emphasizing the need for collaborative efforts between stakeholders to harness the full potential of Big Data and AI for sustainable food supply chains.

This research contributes to the growing body of knowledge on digital transformation in logistics and provides actionable insights for businesses, policymakers, and researchers aiming to tackle food waste through innovative technological solutions.