globusjournal

Post-harvest losses represent a major challenge in the agricultural sector, particularly in developing economies where inefficiencies in supply chain management, storage infrastructure, and demand forecasting contribute to significant food wastage. A considerable portion of agricultural produce deteriorates before reaching consumers due to inaccurate demand estimation, poor inventory planning, and inefficient distribution systems. In recent years, Artificial Intelligence (AI) has emerged as a powerful technological solution capable of improving demand forecasting and optimizing decision-making processes within agri-business supply chains. This study examines the role of AI-driven demand forecasting systems in reducing post-harvest losses in agri-business. AI technologies such as machine learning, predictive analytics, and big data processing enable the analysis of large datasets related to market demand, seasonal consumption patterns, weather conditions, and consumer behavior. These technologies provide more accurate demand predictions, allowing farmers, distributors, and retailers to align production, storage, and distribution strategies with actual market requirements. As a result, overproduction, excess inventory, and spoilage of perishable agricultural products can be significantly minimized. AI-based forecasting systems support better coordination among supply chain stakeholders and improve overall operational efficiency. The integration of AI into agri-business forecasting practices therefore offers a promising pathway toward reducing post-harvest losses, improving supply chain resilience, and promoting sustainable agricultural development.

Keywords: Artificial Intelligence, Demand Forecasting, Post-Harvest Losses, Agri-Business, Predictive Analytics, Supply Chain Optimization, Smart Agriculture, Food Waste Reduction.