Quantitative methods for agri-food supply chain resilience: a systematic literature review using text mining - Çalı, Sedef; Ekren, Banu Y.; Toy, Ayhan Özgür (Elsevier, 2025)
Agri-food Supply Chains (AFSCs) face increasing disruptions from natural disasters, pandemics, and economic crises, necessitating robust quantitative analysis for resilience. This study conducts a Systematic Literature Review (SLR) using text mining and Latent Dirichlet Allocation (LDA) to identify six key research themes, including risk management, pandemic effects, simulation-based resilience, climate change, market price volatility, and optimization models. Findings reveal that multi-criteria decision-making, simulation, optimization, and machine learning are widely used, yet gaps remain in Artificial Intelligence (AI)-driven risk prediction, real-time data integration, and adaptive decision-making. This review offers insights for researchers and practitioners, emphasizing the need for AI, digital twins, and blockchain to enhance AFSC resilience.
11th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2025: 30 June – 3 July 2025, Trondheim, Norway
Keywords: 40 Engineering; 4007 Control Engineering, Mechatronics and Robotics; 4008 Electrical Engineering; Machine Learning and Artificial Intelligence; Networking and Information Technology R&D (NITRD); Cancer; 2 Zero Hunger; 13 Climate Action; agri-food supply chain; quantitative methods; text mining; latent Dirichlet allocation; literature review; resilient supply chains