Call For Papers

AISCLM-2026 welcomes original research papers, case studies, innovative applications, and industry-driven contributions from researchers, academicians, practitioners, policymakers, and technology leaders. The conference aims to provide a multidisciplinary platform for discussing recent advances, challenges, and future directions in Artificial Intelligence in Supply Chain and Logistics Management. It seeks to foster collaboration among experts working at the intersection of intelligent computing, supply chain management, computational intelligence, and logistics management.

AISCLM-2026 invites original work from the following list of topics (but is not limited to):

1. Foundations of AI for Supply Chain and Logistics

• Artificial Intelligence Theories and Models for Supply Chains
• Computational Intelligence and Soft Computing
• Intelligent Decision-Making in Supply Chain Management
• Knowledge-Based Supply Chain Systems
• Intelligent Agents and Autonomous Supply Chain Systems
• AI-Based Optimization and Operations Research
• Hybrid Intelligence for Supply Chain Management
• Explainable and Interpretable AI for Supply Chain Decisions
• AI-Driven Supply Chain Strategy and Planning
• Human–AI Collaboration in Supply Chain Management

2. Machine Learning and Predictive Supply Chain Analytics

• Machine Learning for Supply Chain Optimization
• Supervised, Unsupervised, and Semi-Supervised Learning
• Deep Learning for Supply Chain Applications
• Demand Forecasting and Demand Sensing
• Predictive Analytics for Supply Chain Management
• Reinforcement Learning for Dynamic Decision-Making
• Time-Series Forecasting for Demand and Supply
• Anomaly and Outlier Detection in Supply Chains
• Transfer Learning and Adaptive Supply Chain Models
• Generative AI and Foundation Models for Supply Chain Analytics

3. Intelligent Procurement, Inventory and Supplier Management

• AI-Driven Procurement and Strategic Sourcing
• Intelligent Supplier Selection and Evaluation
• Supplier Risk Assessment and Prediction
• AI-Based Inventory Optimization
• Multi-Echelon Inventory Management
• Safety Stock and Replenishment Optimization
• Automated Purchase and Reorder Decisions
• Supplier Relationship Management Using AI
• Procurement Fraud and Anomaly Detection
• Intelligent Contract and Spend Analytics

4. AI in Transportation, Fleet and Logistics Management

• Intelligent Transportation Management Systems
• AI-Based Route and Path Optimization
• Vehicle Routing and Scheduling
• Dynamic Routing and Real-Time Logistics Optimization
• Fleet Management and Predictive Analytics
• AI for Freight and Cargo Management
• Autonomous Vehicles and Intelligent Transportation
• Last-Mile Delivery Optimization
• Delivery Time and Cost Prediction
• Intelligent Logistics Network Design

5. Smart Warehousing, Robotics and Autonomous Logistics

• AI-Powered Warehouse Management Systems
• Intelligent Warehouse Automation
• Robotics and Autonomous Warehouse Systems
• Automated Storage and Retrieval Systems
• Computer Vision for Inventory and Warehouse Operations
• Intelligent Picking, Packing, and Sorting
• AI-Based Warehouse Layout Optimization
• Human–Robot Collaboration in Logistics
• Autonomous Mobile Robots and Drones
• Smart Sensors and IoT-Enabled Warehouses

6. Generative AI, NLP and Intelligent Supply Chain Systems

• Large Language Models for Supply Chain Management
• Generative AI for Supply Chain Planning and Decision Support
• AI-Powered Supply Chain Chatbots and Assistants
• Natural Language Processing for Procurement and Logistics
• Intelligent Document Processing and Automation
• Contract and Purchase Order Intelligence
• Conversational AI for Customer and Supplier Management
• Generative AI for Logistics Planning and Optimization
• AI-Based Knowledge Management in Supply Chains
• Evaluation, Governance, and Responsible Use of Generative AI

7. IoT, Digital Twins, Cloud and Edge AI for Supply Chains

• Internet of Things (IoT) in Smart Supply Chains
• AIoT and Intelligent Supply Chain Networks
• Digital Twins for Supply Chain Simulation
• Cloud-Based Supply Chain Intelligence
• Edge AI for Real-Time Logistics Operations
• Connected Vehicles and Intelligent Fleet Systems
• Real-Time Supply Chain Visibility
• Sensor-Based Asset and Shipment Tracking
• AI-Enabled Cold Chain Monitoring
• Digital Platforms for Intelligent Logistics Ecosystems

8. Supply Chain Risk, Resilience, Security and Trust

• AI for Supply Chain Risk Management
• Supply Chain Disruption Prediction
• Resilient and Adaptive Supply Chain Systems
• AI-Based Risk Identification and Early Warning Systems
• Cybersecurity in Digital Supply Chains
• Privacy-Preserving Supply Chain Analytics
• Blockchain and AI for Supply Chain Transparency
• Fraud Detection and Supply Chain Security
• Traceability and Product Authentication
• Explainable, Trustworthy, and Responsible AI in Supply Chains

9. Sustainable, Green and Circular Supply Chain Management

• AI for Sustainable Supply Chain Management
• Green Logistics and Intelligent Transportation
• AI for Carbon Emission Measurement and Reduction
• Sustainable Route and Network Optimization
• Energy-Efficient Warehousing and Logistics
• AI for Circular Economy and Reverse Logistics
• Waste Reduction and Resource Optimization
• Sustainable Procurement and Supplier Assessment
• AI for Climate-Resilient Supply Chains
• Intelligent ESG and Sustainability Analytics

10. Emerging Trends and Future of AI-Enabled Supply Chains

• Autonomous Supply Chains and Logistics Networks
• Agentic AI for Supply Chain Management
• Multi-Agent Systems for Supply Chain Coordination
• Quantum Computing for Supply Chain Optimization
• AI and Autonomous Delivery Systems
• AI-Driven Smart Ports and Maritime Logistics
• Intelligent Air Cargo and Airport Logistics
• AI for Humanitarian and Disaster Logistics
• AI for Global and Hyperconnected Supply Networks
• Future Directions in AI-Enabled Supply Chain and Logistics Management