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 Computing. It seeks to foster collaboration among experts working at the intersection of intelligent computing, data-driven technologies, computational intelligence, and emerging digital ecosystems.

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