Logisense is a comprehensive warehouse management solution that leverages data-driven approaches and machine learning to optimize inventory operations. The system automates data collection, provides intelligent demand forecasting, and offers real-time insights through an intuitive interface.
- π Automated data collection and preprocessing pipeline
- π€ Machine learning-powered demand forecasting
- π Real-time inventory tracking and management
- π₯ Role-based access control system
- π± Interactive web interface
- π¦ Containerized deployment
- π Automated stock level monitoring
- π Custom dashboards for different user roles
- Backend: Python
- Frontend: Streamlit
- Database: PostgreSQL
- ML Components: Prophet (Time Series Forecasting)
- Visualization: Matplotlib
- Deployment: Docker
- Version Control: Git
The application follows a layered architecture:
- Presentation Layer (UI/UX)
- Business Logic Layer
- Data Access Layer
- Data Storage Layer
-
Data Processing Engine
- Automated data collection
- Data cleaning and preprocessing
- Historical data management
-
Forecasting System
- Time series analysis
- Demand prediction
- Inventory optimization
-
User Interface
- Role-specific dashboards
- Real-time data visualization
- Inventory management tools
-
Security
- Role-based access control
- Secure data handling
- Authentication system
# Clone the repository
git clone https://github.com/wided-abdallah/LogiSense
# Navigate to project directory
cd logisense
# Install dependencies
pip install -r requirements.txt
# Run the application
streamlit run LogiSense/Template/Home.py
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.