📈 Demand Forecasting Masterclass

The complete guide to demand forecasting — from moving averages to transformers, from theory to production.

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🌟 Why Demand Forecasting?

Every supply chain decision starts with a forecast. Inventory levels, production schedules, procurement quantities, logistics capacity — they all depend on knowing what customers will want, when, and how much.

A 1% improvement in forecast accuracy typically yields:

• 📦 10-15% reduction in safety stock
• 💰 5-8% reduction in total supply chain cost
• 📊 2-4 point improvement in service level
• ♻️ Reduction in waste from overproduction

This masterclass takes you from beginner to expert across 12 modules, covering every technique used in practice.

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🏗️ Course Architecture

flowchart TB
    subgraph Foundations
        M1[📊 Fundamentals] --> M2[📈 Statistical Methods]
    end
    
    subgraph ML Track
        M2 --> M3[🧠 Machine Learning]
        M3 --> M4[🔮 Deep Learning]
        M4 --> M5[⚙️ Intermittent Demand]
    end
    
    subgraph Advanced
        M5 --> M6[📐 Hierarchical]
        M6 --> M7[🔧 Feature Engineering]
        M7 --> M8[⚖️ Ensembles]
    end
    
    subgraph Production
        M8 --> M9[📡 Demand Sensing]
        M9 --> M10[🎯 Evaluation]
        M10 --> M11[🚀 Deployment]
        M11 --> M12[✨ GenAI]
    end
    
    style Foundations fill:#e3f2fd
    style Advanced fill:#fff9c4
    style Production fill:#c8e6c9

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📚 Modules

Module	Title	Notebook
01	📊 Foundations of Demand Forecasting	Open
02	📈 Statistical Methods: MA, ES, ARIMA	Open
03	🧠 Machine Learning for Forecasting	Open
04	🔮 Deep Learning: LSTM, Transformer, N-BEATS	Open
05	⚙️ Intermittent & Lumpy Demand	Open
06	📐 Hierarchical Forecasting & Reconciliation	Open
07	🔧 Feature Engineering for SC Forecasting	Open
08	⚖️ Ensemble Methods & Model Selection	Open
09	📡 Demand Sensing with External Signals	Open
10	🎯 Forecast Evaluation & Bias Correction	Open
11	🚀 Production Deployment & MLOps	Open
12	✨ GenAI-Enhanced Forecasting	Open

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🚀 Quick Start

git clone https://github.com/virbahu/demand-forecasting-masterclass.git
cd demand-forecasting-masterclass
pip install -r requirements.txt
jupyter notebook

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👤 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

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⭐ Star History

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📄 License

MIT License — see LICENSE for details.

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