Role of artificial intelligence in transforming agricultural supply chain management in Bangladesh

Abstract

Abstract This study explores the role of Artificial Intelligence (AI) in transforming agricultural supply chain management in Bangladesh through a systematic comparative analysis of existing literature, institutional reports, and global case studies. AI technologies including predictive analytics, machine learning, blockchain, and precision agriculture are examined for their potential to address longstanding inefficiencies in Bangladesh’s agri-supply chain. The study finds that AI-driven demand forecasting models using LSTM and ARIMA achieved 89–92% crop yield prediction accuracy, representing a 37% improvement over traditional methods. Smart warehousing systems reduced operational costs by 25% and increased order processing speed by 40%, while blockchain integration cut payment cycles from 15 days to 2.3 days and increased smallholder farmer incomes by 22–25%. Precision agriculture technologies achieved 25% yield growth with 15–20% water savings and 30% fertilizer efficiency gains. Despite these promising outcomes, Bangladesh’s AI adoption rate remains at only 18%, significantly behind India (35%) and Vietnam (28%), primarily due to insufficient infrastructure, lack of digital literacy, and high implementation costs. The study proposes targeted policy interventions including IoT subsidies, farmer training programs, and public-private partnerships to enable inclusive and sustainable AI integration across Bangladesh’s agricultural sector.