🧴 AI & Blockchain-Enabled Cosmetics Supply Chain Transparency and Counterfeit Detection

Enhancing transparency, traceability, and authenticity in the cosmetics supply chain using Blockchain and Artificial Intelligence.

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📚 Table of Contents

1. Project Overview
2. Features
3. Key Benefits
4. Tech Stack
5. Workflow
6. Getting Started
7. Team Members
8. Next Steps
9. License

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🧾 Project Overview

This project aims to address real-world challenges in the cosmetics industry such as counterfeiting, opaque supply chains, and ethical sourcing verification. It combines Blockchain technology for immutable record-keeping with AI-driven analytics to detect anomalies and predict demand.

By leveraging smart contracts and predictive models, we are building a system that not only ensures product transparency but also empowers consumers to verify authenticity and supports manufacturers in improving operational efficiencies.

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⚙️ Features

✅ Blockchain Integration

• Immutable ledger for end-to-end supply chain tracking
• Smart contracts for automated quality checks, payments, and compliance

🤖 AI-Powered Predictive Analytics

• Anomaly detection using supervised learning
• NLP-based textual analysis for ingredient consistency checks

🔍 Consumer Empowerment

• Product history lookup via blockchain
• Real-time counterfeit alerts

🌐 Ethical Sourcing Verification

• Transparent tracking of raw material origins

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📈 Key Benefits

Benefit	Description
✅ Enhanced Transparency	Consumers can view full product lifecycle on an immutable ledger
🔍 Counterfeit Detection	AI models flag suspicious or counterfeit products in real time
⏱️ Operational Efficiency	Smart contracts automate workflows and reduce manual errors
💡 Trust Building	Proves ethical sourcing and responsible manufacturing practices

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🛠️ Tech Stack

Layer	Technology
Blockchain / Smart Contracts	Multichain, Python
Backend	Python FastAPI
Frontend	React.js
Database	MongoDB / PostgreSQL
AI/ML	Python (TensorFlow, PyTorch, Scikit-learn, NLTK)

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🔄 Workflow

1. Data Input: Suppliers, manufacturers, and logistics partners input product and shipment data.
2. Blockchain Recording: All events are stored immutably using smart contracts.
3. AI Analysis: Models analyze data for anomalies and predict trends.
4. Consumer Verification: End users query a product to retrieve its verified history.
5. Alert System: Any flagged anomalies trigger internal alerts for review.

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▶️ Getting Started

To run this project locally:

Prerequisites

• Python 3.x
• MongoDB / PostgreSQL

Installation

Run the App

Refer to the docs/ folder for detailed documentation.

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👥 Team Members

• Muhammad Hamza
• Emmanuel
• Muhammad Tahir
• Insha Javed

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© License

This project is licensed under the MIT License – see the LICENSE file for details.