👋 Hello Devs,
I recently completed a passion project that blends AI, forecasting, and simulation — all focused on one of the most mission-critical systems in the world: supply chains.
🔗 GitHub: https://github.com/AquarlisPrime/AI-Driven-Forecast-Resilience-Simulator-for-Supply-Chain
💡 Why I Built This
Supply chain disruptions — whether from pandemics, port congestion, or raw material shortages — have massive ripple effects. I wanted to build a hands-on tool that:
Simulates disruptions in real-time
Predicts demand using machine learning
Helps visualize impact across a supply chain network
Calculates cost, emissions, and risk dynamically
Something intuitive enough to demo and powerful enough to experiment with.
🧠 Key Features
✅ AI-based Forecasting
Uses models like Prophet, LightGBM, and even supports plugging in custom models like LSTM/XGBoost.
✅ Digital Twin Supply Chain Network
Visualizes your supply chain as a graph of nodes and edges using networkx and PyVis.
✅ Disruption Scenario Engine
Simulate transit delays, demand spikes, and supply outages. Adjust nodes live and see results in real time.
✅ Cost, Emissions, and Risk Calculators
The app tracks and recalculates operational metrics dynamically — great for "what-if" analysis.
✅ Streamlit-Powered Interface
No complex setup. Just pip install and launch via browser.
🛠️ Tech Stack
📊 Forecasting: Prophet, LightGBM, NeuralProphet
🔗 Visualization: networkx, pyvis, plotly, streamlit
📦 Core Libraries: pandas, numpy, scikit-learn
🧪 Future Additions: SHAP, SQLite/Firebase, optimization engine
💬 What I'm Looking For
🚀 Feedback from ML or supply chain folks
🔧 Ideas for additional models or optimization logic
💡 Feature suggestions or UX feedback
👥 Collaborators who’d like to help scale this
📬 Try It Out! And feel free to fork, star, or open issues — all contributions are welcome!
❤️ Thank You!
If you're into ML, supply chains, data apps, or even just cool Python projects — I'd love your feedback! Drop a comment, GitHub issue, or DM anytime.
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