Forecasting disruptions in global food value chains to tackle food insecurity: The role of AI and big data analytics – A bibliometric and scientometric analysis

Abstract

Globalization and interconnected supply chains have led to complex disruptions in global value chains, caused by various factors such as natural disasters, climate events, geopolitical conflicts, and economic crises. Recent breakthroughs in AI, machine learning, blockchain, and big data analytics offer new possibilities for forecasting and managing these disruptions effectively. This study examines the role of AI in forecasting and managing disruptions within global value chain to tackle food insecurity. We conducted a bibliometric and scientometric analysis using comprehensive data from Scopus and Web of Science to explore emerging research trends, influential publications, leading institutions, collaborations, themes, policy implications, and future research avenues. The research revealed an average yearly growth rate of 13.78 % in publications from 1973 to 2022. China, the United Kingdom, and the United States lead in AI applications to address supply chain disruptions, particularly concerning food insecurity. Frequently used keywords include ''food security,'' ''supply chain management,'' ''agriculture,'' ''modelling,'' ''climate change,'' and ''COVID-19.'' Themes identified focus on the impact of COVID-19 on food supply chains, achieving food security amidst climate change, leveraging predictive models in agriculture, and assessing the impact of disruptions on food price volatility and global supply chain risk assessment approaches. The insights gained from this research offer valuable guidance for policymakers and researchers to enhance food security. The identified themes provide direction for future research efforts in advancing food security amidst uncertainties and disruptions in global value chains.

Keywords

• Supply chain disruptions
• Supply chain resilience
• Food insecurity
• Forecasting
• AI
• Machine learning