Blockchain & ML-Based Logistics System

A decentralized logistics platform that combines Ethereum smart contracts for shipment transparency with Machine Learning for delivery time & demand prediction — ensuring trust, efficiency, and real-time visibility across the supply chain.

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Overview

Traditional logistics systems suffer from opacity, data tampering, and poor demand forecasting. This project tackles all three by fusing two cutting-edge technologies:

• Blockchain (Ethereum + Solidity) — Every shipment event is recorded on-chain, creating an immutable, tamper-proof audit trail that all stakeholders can trust.
• Machine Learning — Predictive models forecast delivery times and demand patterns, enabling smarter planning and reduced delays.
• Animated Frontend (GSAP) — A smooth, interactive UI built with vanilla JS, CSS, and HTML, powered by GSAP animations for a polished user experience.

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Features

• On-chain Shipment Tracking — Log and verify shipment status events on the Ethereum blockchain
• End-to-End Transparency — Stakeholders (sender, carrier, receiver) can independently verify shipment history
• ML Demand & Delivery Prediction — Predict estimated delivery times and demand using trained ML models
• Animated UI — GSAP-powered frontend with smooth transitions and interactive components
• Tamper-Proof Records — Immutable on-chain logs prevent data manipulation

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Tech Stack

Layer	Technology
Blockchain	Ethereum, Solidity
Smart Contract Dev	Hardhat / Truffle (local dev)
Frontend	HTML, CSS, JavaScript
Animations	GSAP (GreenSock Animation Platform)
ML Models	Python (scikit-learn / pandas)
Web3 Integration	Web3.js / Ethers.js

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Project Structure

block-chain-and-ML-based-logistics-/
│
├── frontEnd/               # Frontend UI (HTML, CSS, JS + GSAP)
│   ├── index.html
│   ├── styles/
│   └── scripts/
│
├── contracts/              # Solidity smart contracts
│   └── Logistics.sol
│
├── ml-model/               # ML prediction scripts
│   └── predict.py
│
├── package.json            # Node dependencies (GSAP)
└── README.md

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Getting Started

Prerequisites

• Node.js v16+
• MetaMask browser extension
• Python 3.8+
• Hardhat or Ganache for local blockchain

1. Clone the Repository

git clone https://github.com/vaibhavdhyani09/block-chain-and-ML-based-logistics-.git
cd block-chain-and-ML-based-logistics-

2. Install Frontend Dependencies

npm install

3. Set Up Local Blockchain

# Using Hardhat
npx hardhat node

# Deploy the smart contract
npx hardhat run scripts/deploy.js --network localhost

4. Install ML Dependencies

pip install -r requirements.txt

5. Run the Frontend

Open frontEnd/index.html in your browser or serve it locally:

npx serve frontEnd

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🔗 Smart Contract

The core Logistics.sol contract handles:

• Creating shipments — Assign a unique ID, origin, destination, and sender
• Updating shipment status — In Transit, Out for Delivery, Delivered
• Querying history — Fetch the full status history of any shipment by ID

All state changes emit events that are permanently recorded on the Ethereum blockchain.

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ML Prediction Module

The ML module is trained on historical logistics data to predict:

• Estimated Delivery Time — Based on distance, carrier, weather conditions, and package weight
• Demand Forecasting — Predict shipment volume for better resource planning

# Example usage
from predict import predict_delivery_time

result = predict_delivery_time(origin="Mumbai", destination="Delhi", weight=5.2)
print(f"Estimated delivery: {result} days")

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Future Improvements

• IPFS integration for document storage (invoices, receipts)
• Multi-carrier support with role-based access control
• Real-time GPS tracking feed
• Mobile-responsive redesign
• Deploy to Ethereum Sepolia testnet

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Contributing

Contributions are welcome! Please open an issue first to discuss what you'd like to change.

1. Fork the repository
2. Create your branch: git checkout -b feature/your-feature
3. Commit your changes: git commit -m 'Add some feature'
4. Push to the branch: git push origin feature/your-feature
5. Open a Pull Request

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Author

Vaibhav Dhyani

• GitHub: @vaibhavdhyani09

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⭐ If you found this project helpful, please give it a star on GitHub!