Quantitative Methods for Agri-Food Supply Chain Resilience: A Systematic Literature Review Using Text Mining
Year: 2025
Type: review
Study design: Systematic Review
Abstract: 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.
Source: IFAC-PapersOnLine
Authors: Sedef Çalı, Banu Yetkin Ekren, Ayhan Özgür Toy
Institutions: Yaşar University, Cranfield University
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4414567889