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🤖 Supply Chain AI Agents for Beginners

12 Lessons Python Jupyter MIT

12 Lessons — Build autonomous AI agents that plan, procure, ship, and optimize your supply chain.


🌟 The Agentic Supply Chain Revolution

The supply chain industry is undergoing its most significant transformation since the invention of ERP. AI agents — autonomous systems that can sense, reason, and act — are replacing manual planning processes that haven't fundamentally changed in 30 years.

This course teaches you to build these agents from scratch:

flowchart LR
    subgraph Traditional
        A[📊 Data] --> B[👤 Human Analyst]
        B --> C[📋 Decision]
        C --> D[⏰ Hours/Days Later]
    end
    
    subgraph Agentic
        E[📊 Data] --> F[🤖 AI Agent]
        F --> G[📋 Decision + Action]
        G --> H[⚡ Seconds/Minutes]
    end
    
    style Traditional fill:#ffcdd2
    style Agentic fill:#c8e6c9
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📚 Curriculum

# Lesson Notebook
01 🤖 What Are Supply Chain AI Agents? Open
02 🔧 Building Your First SC Agent Open
03 🛠️ Tool Use: Connecting Agents to SC Systems Open
04 🧠 Memory & State: Agents That Remember Open
05 📋 Planning Agents for Demand & Supply Open
06 🤝 Procurement Agents: Autonomous Sourcing Open
07 🚚 Logistics Agents: Routing & Scheduling Open
08 📦 Inventory Agents: RL-Based Replenishment Open
09 ⚠️ Risk Monitoring Agents Open
10 🎛️ Multi-Agent Orchestration Open
11 👤 Human-in-the-Loop Agent Design Open
12 🚀 Deploying SC Agents in Production Open

🏗️ Agent Architecture

flowchart TB
    subgraph Agent Core
        S[👁️ Sense\nData Ingestion] --> R[🧠 Reason\nLLM + Rules]
        R --> A[🎯 Act\nExecute Decision]
        A --> L[📝 Learn\nFeedback Loop]
        L --> S
    end
    
    subgraph Tools
        T1[📊 ERP/WMS API]
        T2[🌐 Web Search]
        T3[📧 Email/Slack]
        T4[🔢 Optimization Engine]
    end
    
    subgraph Memory
        M1[💾 Short-term\nConversation]
        M2[🗄️ Long-term\nKnowledge Base]
    end
    
    R --> T1 & T2 & T3 & T4
    R --> M1 & M2
    
    style S fill:#e3f2fd
    style R fill:#fff9c4
    style A fill:#c8e6c9
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🚀 Quick Start

git clone https://github.com/virbahu/sc-ai-agents-for-beginners.git
cd sc-ai-agents-for-beginners
pip install -r requirements.txt
jupyter notebook

👤 Author

Virbahu Jain — Founder & CEO, Quantisage

Building the AI Operating System for Scope 3 emissions management and supply chain decarbonization.

🎓 Education MBA, Kellogg School of Management, Northwestern University
🏭 Experience 20+ years across manufacturing, life sciences, energy & public sector
🌍 Scope Supply chain operations on five continents

⭐ Star History

If you find this useful, please ⭐ star this repo — it helps others discover it!

📄 License

MIT License — see LICENSE for details.

Part of the Quantisage Open Source Initiative | AI × Supply Chain × Climate

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