A Review on Artificial Intelligence Integration in Food Processing Supply Chains: Framework for Sustainable Development

Abstract

Food processing supply chains face increasing challenges related to food safety, product perishability,
demand uncertainty, and sustainability requirements. Recent advancements in artificial intelligence (AI) have created
opportunities to improve decision-making, automation, and operational efficiency across supply chain activities.
Consequently, AI is emerging as a key technology for developing smarter and more resilient food systems.
Objective: This review aims to examine the current applications of AI in food processing supply chains and evaluate
their impact on efficiency, food safety, traceability, and sustainability. It also identifies key challenges and future
research directions for AI adoption in the sector.
Methods: A systematic review of 25 peer-reviewed studies published between 2015 and 2025 was conducted. Relevant
literature was collected from scientific databases focusing on AI applications in food processing and supply chain
management. The selected studies were screened based on relevance, quality, and contribution to the field. AI
technologies were classified according to their application areas, including procurement, processing, quality control,
packaging, logistics, and traceability. The studies were further analyzed to assess technological maturity, operational
benefits, sustainability impacts, and implementation challenges.
Results: The review revealed that machine learning, deep learning, computer vision, predictive analytics, federated
learning, and blockchain-integrated AI systems are increasingly used throughout food processing supply chains. AI
applications significantly improve demand forecasting accuracy, product quality assessment, inventory management,
predictive maintenance, and supply chain visibility. Computer vision technologies enhance automated inspection
and quality assurance processes, while intelligent traceability systems strengthen food safety and transparency.
Several studies reported reductions in food waste, operational costs, and resource consumption through AI-driven
optimization. AI-based decision-support systems also contribute to faster and more accurate responses to supply
chain disruptions. However, widespread implementation remains constrained by data quality issues, high investment
requirements, limited technical expertise, infrastructure limitations, and regulatory compliance challenges. The
level of adoption varies across supply chain stages, with quality control and traceability demonstrating the highest
technological maturity.
Conclusion: Artificial intelligence offers substantial potential to enhance the efficiency, sustainability, resilience,
and safety of food processing supply chains. Addressing technological and organizational barriers will be critical for
achieving wider adoption and maximizing the benefits of AI-driven food supply chain management.