IMPROVING AGRI-FOOD SUPPLY CHAINS THROUGH EFFICIENT INVENTORY MANAGEMENT

Abstract

This article examines the impact of efficient inventory management on improving agri-food supply chains. Food products are by nature perishable, so finding the right point of balance between supply and demand to minimize waste and holding costs is essential for accurate food demand forecasting. The study also implemented state-of-the-art forecasting models such as ARIMA (Auto-Regressive Integrated Moving Average) and exponential smoothing) which improved forecast accuracy by 15%. The use of PC software and later on the web worked on the interaction, as well as the advances made in AI and machine learning, which could read and pick up observations and connections between data on atmospheric conditions, consumer conduct, and acts of advancing. IoT sensors issued real-time information on inspection levels and product freshness, resulting in more accurate stocks. An 18% decrease in holding costs was achieved using EOQ and blockchain for quality control. To improve overall supply chain resilience and efficiency standardized protocols and collaborative storage solutions were proposed to address issues on data integration problems and high implementation costs among challenges of this technology.