An Intelligent B2C eCommerce Framework for Direct Agricultural Product Distribution

Abstract

The rising demand for fresh, locally sourced agricultural products, along with the need for efficient distribution approaches, has prompted interest in direct-to-consumer (B2C) ecommerce models within the agriculture industry. Existing agricultural distribution systems frequently incorporate intermediaries, resulting in inefficiencies, inflated costs and delays in the delivery of perishable goods. The development of advanced technologies, including artificial intelligence (AI), has facilitated the development of more efficient, transparent, and consumer-oriented agricultural distribution systems. This paper outlines the design and architecture of an intelligent B2C ecommerce platform for the direct distribution of agricultural products, incorporating modern technology such as machine learning to improve supply chain efficiency, transparency and user experience. The system has proposed a framework to analyse key factors such as price, freshness, and shipping conditions to deliver customized product recommendations and improve shipment arrangements. Similarly, the real-time tracking backed by AI-based logistics management, ensures the freshness of perishable items while reducing delays and reducing transportation expenses. Also, the proposed framework facilitates interaction between consumers and producers, promoting a transparent, efficient, and economical distribution process. The intelligent model continuously adjusts to customer preferences and market dynamics, improving efficiency in operation and user satisfaction.