Research Themes and Publications
Welcome to our curated collection of classic and contemporary publications in the field of Supply Chain Artificial Intelligence (SC-AI). This resource highlights influential research and cutting-edge developments that have shaped, and continue to shape, the intersection of Supply Chain Management and AI.
To help you navigate this evolving landscape, articles are organised into research themes, each with its own dedicated page offering context, summaries, and key insights. Whether you're a researcher, practitioner, or student, we invite you to explore these themes and deepen your understanding of how AI is transforming global supply chains.
Deep Research Agents for Supply Chain
Deep research agents leverage large language models and autonomous planning to conduct multi-step supply chain analysis, risk scanning, and supplier due diligence with minimal human intervention.
Federated Learning for Supply Chain
Federated learning enables multiple supply chain partners to collaboratively train AI models without sharing raw data, supporting privacy-preserving collaboration, demand forecasting, and risk analytics.
Multi-Agent Systems for Supply Chain
Multi-agent systems model supply chains as networks of autonomous decision-makers that coordinate, negotiate, and learn, enabling decentralised optimisation and resilient operations.
Synthetic Data Generation for Supply Chain
Synthetic data generation tackles data scarcity, confidentiality, and benchmarking challenges in supply chain research, using generative AI to produce realistic artificial datasets for training, evaluation, and collaboration.
Autonomous Supply Chains
Autonomous supply chains (ASCs) transform traditional operations by using AI, IoT, and digital twins for real-time, intelligent decision-making with minimal human input. Explore key research shaping this emerging field.
Delay Prediction
Predicting delivery delays using AI and machine learning is critical for supply chain resilience. Explore key publications advancing forecasting methods for logistics and procurement.
Knowledge Graph
Knowledge graphs enable structured representation of supply chain relationships, supporting transparency, risk assessment, and intelligent decision-making. Explore key research in this area.
Link Prediction
Link prediction methods uncover hidden relationships in supply chain networks, enabling better visibility and risk management. Explore the latest research on network-based supply chain analytics.
Complex Supply Networks
Complex network science reveals hidden structural patterns in supply chains, enabling better understanding of resilience, risk propagation, and efficiency. Explore foundational and recent research.
Machine Unlearning
Machine unlearning addresses the challenge of selectively removing learned data from AI models, with growing importance for privacy and compliance in supply chain AI systems.
Supply Chain Finance
Supply chain finance leverages financial instruments and AI-driven analytics to optimise working capital and manage risk across supply networks. Explore key research in this growing field.