Module 1: Fundamental Concepts of Supply Chain Management
- 1.1 SCOR Model and Core Processes (Plan, Source, Make, Deliver, Return, Enable)
- 1.2 Key Functions: Procurement, Inventory Management, Logistics, Warehousing, Demand Forecasting, Risk, and Resilience
- 1.3 Global Challenges: Volatility, Sustainability, Nearshoring, and ESG
- 1.4 KPIs and Performance Measurement
- 1.5 Activity: Analyze and Map a Real-World Supply Chain
Module 2: AI Concepts, Techniques, and Tools for SCM
- 2.1 AI/ML Fundamentals – Supervised & Unsupervised Learning, Predictive & Prescriptive Analytics, Optimization, Reinforcement Learning
- 2.2 Key Techniques – Neural Networks, Computer Vision, NLP, Digital Twins, Edge AI
- 2.3 AI Tools for SCM
- 2.4 Data Foundations – IoT, Real-Time Data Pipelines, Data Quality & Governance
Module 3: LLM and Generative AI Applications in SCM
- 3.1 LLM/GenAI Fundamentals and Enterprise Integration
- 3.2 Use Cases – Demand Planning Assistance, Contract Analysis, Supplier Communication, Scenario Simulation, Report Generation, Synthetic Data
- 3.3 Chat-Based Copilots for Planners and Knowledge Management
- 3.4 Limitations and Best Practices (Hallucinations, Grounding, Integration)
- 3.5 Tools – Enterprise GPT-like Models, LangChain/LlamaIndex, Amazon Business Assistant, Custom GenAI Workflows
Module 4: Ethical Considerations and Responsible AI in SCM
- 4.1 Bias in Forecasting/Procurement, Transparency, and Explainability
- 4.2 Privacy, Security, Regulatory Compliance
- 4.3 Job Displacement, Upskilling, and Human-AI Collaboration
- 4.4 Sustainability & ESG – AI for Ethical Sourcing and Carbon Tracking
- 4.5 Governance Frameworks and Risk Management
Module 5: Supply Chain Digitization, Orchestration, and Intelligent Systems
- 5.1 Digitization – ERP + SCM Platforms, Cloud Integration, Blockchain for Traceability, APIs
- 5.2 Orchestration – Control Towers, Real-Time Visibility, Data Pipelines, Digital Twins
- 5.3 Intelligent & Smart SCM – Predictive/Prescriptive Analytics, Autonomous Exception Management, Robotics + Computer Vision, Edge AI
- 5.4 Human + AI Collaboration Models
Module 6: Industrial Applications, Case Studies, and Business Value
- 6.1 Applications Across Industries
- 6.2 Real-World ROI – Efficiency Gains, Cost Reduction, and Resilience Improvements
- 6.3 Implementation Best Practices
- 6.4 Case Studies from Blue Yonder, Kinaxis, Oracle, and Others
Module 7: Strategic SCM, Logistics Policies, and Sustainability
- 7.1 Logistics Policies, Trade Regulations, Tariffs, and Geopolitical Risks
- 7.2 Strategic Network Design: Optimization, Resilience, Nearshoring, and Friendshoring
- 7.3 Sustainable SCM: Circular Economy, Green Logistics, and AI-Driven ESG Reporting
- 7.4 Organizational Transformation and Leadership in AI-Enabled Supply Chains
- 7.5 Case Studies
Module 8: Agentic AI and the Future of Autonomous Supply Chains
- 8.1 Agentic AI Concepts: Autonomous Goal-Oriented Agents, Multi-Agent Systems, and Reasoning-Action Loops
- 8.2 Applications: Autonomous Replenishment, Risk Mitigation, Supplier Onboarding, Dynamic Rerouting, and End-to-End Orchestration
- 8.3 Tools & Platforms: Kinaxis Maestro Agents, Oracle AI Agents, Blue Yonder Cognitive Agents, Custom Builds, and Automation Anywhere
- 8.4 Architectures, Guardrails, and Human Oversight
- 8.5 Future Outlook for 2026+: From Copilots to Semi-Autonomous Operations
- 8.6 Capstone Project: Design and Prototype a Multi-Agent Workflow for a Supply Chain
- 8.7 Case Studies
Optional Module: AI Agents in Supply Chain
- 1. What Are AI Agents
- 2. What Are AI Agents in Logistics and Supply Chain
- 3. Applications & Trends of AI Agents in Supply Chain
- 4. How Does an AI Agent Work
- 5. Core Characteristics of AI Agents
- 6. Key Advantages of AI Agents in Logistics and Supply Chain
- 7. Types of AI Agent
- 8. Case Studies
- 9. Hands on experiment
Tools you will explore
- LeewayHertz (ZBrain)
- C3.ai
- Coupa (LLamasoft)
- Zebra (Workcloud Demand Intelligence Suite)