AI+ Supply Chain Practitioner™

Course structure

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