Blockchain-Enabled Supply Chain Tracking System with Machine Learning-Based Optimization
Plag ProBlockchainProject Objectives:
The primary goal of the project is to design and develop a decentralized system that allows real-time tracking and verification of goods as they move through the supply chain. Traditional supply chains often suffer from inefficiencies, fraud, and a lack of transparency. This project addresses those challenges by leveraging blockchain technology to record immutable and verifiable transactions across all stages—from origin to destination. Each product’s movement is securely logged on a distributed ledger, ensuring end-to-end traceability. Additionally, machine learning models will be used to analyze supply chain data to forecast potential delays, optimize inventory levels, and recommend route efficiencies. The outcome is a prototype application that enables transparent logistics management, supports real-time decision-making, and strengthens supply chain trustworthiness.
Project Tasks:
The project is structured over twelve weeks, beginning with foundational knowledge and culminating in a fully functional prototype. In the initial weeks, students will explore key concepts in blockchain technology (Ethereum, Hyperledger Fabric) and familiarize themselves with smart contract development and distributed ledger architecture. They will also examine supply chain datasets and install relevant tools like MongoDB, PostgreSQL, TensorFlow, and Scikit-learn.
In the mid-project phase, students will build a blockchain-based system for transaction logging and traceability, followed by the integration of machine learning models that can predict delays or optimize delivery routes. The system will include a simple interface for tracking goods and visualizing real-time data. The later weeks are dedicated to performance testing, debugging, refining the interface, and compiling comprehensive project documentation. The project excludes large-scale real-world implementation due to infrastructure limitations, but offers a full demonstration of how such a system would work in a live environment.
