Supplier Ranking Service

A Django-based web application that uses Q-learning algorithms to rank suppliers based on various performance metrics. This intelligent system learns from supplier interactions and performance data to optimize supplier selection for businesses.

Features

• Intelligent Supplier Ranking: Utilizes reinforcement learning (Q-learning) to rank suppliers based on historical performance
• Performance Metrics Tracking: Monitor supplier performance across multiple dimensions
• Data-Driven Decisions: Make informed supplier selection decisions based on automated analysis
• REST API: Access ranking data and analytics through a comprehensive API
• Admin Dashboard: Manage suppliers and view performance analytics through a user-friendly interface

Technology Stack

• Backend: Django 5.2
• Database: PostgreSQL
• Machine Learning: Q-learning implemented with NumPy and Pandas
• Data Visualization: Matplotlib

Installation

Prerequisites

• Python 3.9 or higher
• PostgreSQL
• Virtual environment (recommended)

Setup Instructions

1. Clone the repository

   git clone https://github.com/IASSCMS/supplier-ranking-service.git
   cd supplier-ranking-service

2. Create and activate a virtual environment

   python -m venv venv
   
   # On Windows
   venv\Scripts\activate
   
   # On macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment variables

   cp .env.example .env

   Edit the .env file and replace placeholder values with your actual configuration:

   • Generate a new Django secret key:

     python -c "from django.core.management.utils import get_random_secret_key; print(get_random_secret_key())"

   • Configure database settings
   • Set DEBUG mode appropriately

5. Apply migrations

   python manage.py makemigrations
   python manage.py migrate

6. Create a superuser (admin)

   python manage.py createsuperuser

7. Run the development server

   python manage.py runserver

   Visit http://127.0.0.1:8000/ in your browser to access the application.

Usage

Admin Interface

Access the admin interface at http://127.0.0.1:8000/admin/ to:

• Manage suppliers
• View performance metrics
• Configure ranking parameters
• Monitor system learning

API Endpoints

• /api/suppliers/ - List all suppliers
• /api/suppliers/{id}/ - Get specific supplier details
• /api/rankings/ - Get current supplier rankings
• /api/metrics/ - View performance metrics

Q-Learning Algorithm

The supplier ranking service implements a reinforcement learning approach using Q-learning. The algorithm:

1. Evaluates supplier actions as states
2. Assigns rewards based on performance metrics
3. Updates Q-values to optimize future supplier selection
4. Ranks suppliers based on their Q-values

Key metrics used in the learning process include:

• Delivery time performance
• Quality consistency
• Price competitiveness
• Responsiveness
• Compliance with requirements

Development

Running Tests

pytest

Code Style

This project follows PEP 8 style guidelines. To check code style:

flake8

Deployment

For production deployment:

1. Set DEBUG=False in .env
2. Configure proper database settings
3. Set up a production-ready web server (e.g., Gunicorn)
4. Use a reverse proxy (e.g., Nginx)
5. Set up static files serving

Contributing

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

License

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

Acknowledgments

• Django community for the excellent web framework
• Reinforcement learning resources and research papers on supplier evaluation