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Django based inventory management system for franchisee stores

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Inventory Management for Franchisee Stores

This project is an inventory management system for franchisee stores built using Django and Tailwind CSS.

Features

  • User Authentication: The application leverages Django's built-in authentication system to provide secure user registration and login functionality.
  • Dashboard: Upon logging in, users are presented with a dashboard that provides an overview of key inventory metrics, sales data, and notifications.
  • Inventory Management: Users can add, update, and delete products in the inventory. Each product entry includes details such as name, description, price, and quantity.
  • Stock Management: The system allows users to track stock levels of each product and receive notifications for low stock items, enabling timely restocking.
  • Purchase Orders: Users can generate purchase orders based on low stock items or predicted demand. The application provides features for managing and tracking purchase orders.
  • Sales Tracking: The system tracks sales made by the franchisee store, providing insights into product performance and sales trends.
  • Reporting: Customized reports can be generated to analyze inventory levels, sales performance, and other relevant metrics, aiding in decision-making processes.
  • Notifications: Users receive notifications for important inventory updates, low stock items, and pending purchase orders to stay informed about the system's status.

Workflow

  1. User Registration and Login: Users can create an account and log in using their credentials.
  2. Dashboard Overview: Upon logging in, users are presented with an interactive dashboard displaying key inventory metrics, sales data, and notifications.
  3. Inventory Management: Users can add new products, update existing product details, and delete products from the inventory using intuitive forms.
  4. Stock Monitoring: The system provides a clear overview of stock levels for each product, highlighting low stock items for attention.
  5. Purchase Order Generation: Users can generate purchase orders based on low stock items or predicted demand, specifying the required quantity and supplier information.
  6. Purchase Order Management: Users can track and manage purchase orders, including updating delivery information and marking orders as completed upon receipt of stock.
  7. Sales Tracking: The system records sales made by the franchisee store, allowing users to view sales history, including details such as the product sold, quantity, and date of sale.
  8. Reporting and Analysis: Customized reports can be generated to analyze inventory levels, sales performance, and other relevant metrics, assisting in data-driven decision-making.
  9. Notifications: Users receive notifications for important inventory updates, low stock items, and pending purchase orders, ensuring timely actions to maintain sufficient stock levels.

Installation and Usage

  1. Clone the repository.
  2. Install the required dependencies using pip install -r requirements.txt.
  3. Set up the database and run migrations using python manage.py migrate.
  4. Start the development server with python manage.py runserver.
  5. Access the application through your browser at http://localhost:8000.

Please note that this is a basic setup guide. You may need to adjust the instructions according to your specific environment and deployment requirements.

Contributing

Contributions to this project are welcome. If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request.

Refer to issues and kindly contribute:

  1. Better and optimized frontend
  2. Visually appealing dashboard
  3. Currently purchase model is only for superuser, once she generates a purchase, inventory of respective stores get updated, Add an option to let vendors request inventory
  4. Any more features
  5. Contribution for better documentation

License

This project is licensed under the MIT License.

Feel free to customize and modify the project according to your needs.

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  • JavaScript 44.6%
  • CSS 34.6%
  • Python 11.7%
  • HTML 9.1%