https://github.com/wided-abdallah/logisense
An intelligent warehouse management system featuring automated data processing, demand forecasting, and real-time inventory tracking. Built with Python, Streamlit, and Prophet.
https://github.com/wided-abdallah/logisense
data-science forecasting postgresql prophe python streamli
Last synced: about 1 month ago
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An intelligent warehouse management system featuring automated data processing, demand forecasting, and real-time inventory tracking. Built with Python, Streamlit, and Prophet.
- Host: GitHub
- URL: https://github.com/wided-abdallah/logisense
- Owner: wided-abdallah
- Created: 2025-01-16T13:23:15.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-01-16T14:01:34.000Z (over 1 year ago)
- Last Synced: 2025-03-12T09:16:07.600Z (about 1 year ago)
- Topics: data-science, forecasting, postgresql, prophe, python, streamli
- Homepage:
- Size: 76.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Logisense
## Overview
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.
## Features
- 📊 Automated data collection and preprocessing pipeline
- 🤖 Machine learning-powered demand forecasting
- 📈 Real-time inventory tracking and management
- 👥 Role-based access control system
- 📱 Interactive web interface
- 📦 Containerized deployment
- 🔄 Automated stock level monitoring
- 📊 Custom dashboards for different user roles
## Tech Stack
- **Backend:** Python
- **Frontend:** Streamlit
- **Database:** PostgreSQL
- **ML Components:** Prophet (Time Series Forecasting)
- **Visualization:** Matplotlib
- **Deployment:** Docker
- **Version Control:** Git
## Architecture
The application follows a layered architecture:
- Presentation Layer (UI/UX)
- Business Logic Layer
- Data Access Layer
- Data Storage Layer
## Key Components
1. **Data Processing Engine**
- Automated data collection
- Data cleaning and preprocessing
- Historical data management
2. **Forecasting System**
- Time series analysis
- Demand prediction
- Inventory optimization
3. **User Interface**
- Role-specific dashboards
- Real-time data visualization
- Inventory management tools
4. **Security**
- Role-based access control
- Secure data handling
- Authentication system
## Checkout the Demo 🔗
https://youtu.be/asuVUGcNBTM
## Getting Started
```bash
# 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.