https://github.com/tanishpoddar/logitrack
LogiTrack is a Python & Streamlit-powered inventory management system for real-time warehouse optimization. It offers multi-warehouse planning, interactive maps, and supply chain analytics, supporting global coordinates, CSV/SQL data, and customizable parameters.
https://github.com/tanishpoddar/logitrack
data-visualization database inventory-management logistics optimization python streamlit supply-chain supply-chain-analytics warehouse-optimization
Last synced: 2 months ago
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LogiTrack is a Python & Streamlit-powered inventory management system for real-time warehouse optimization. It offers multi-warehouse planning, interactive maps, and supply chain analytics, supporting global coordinates, CSV/SQL data, and customizable parameters.
- Host: GitHub
- URL: https://github.com/tanishpoddar/logitrack
- Owner: tanishpoddar
- License: gpl-3.0
- Created: 2025-03-23T19:59:19.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-03-25T05:24:32.000Z (10 months ago)
- Last Synced: 2025-09-10T07:39:06.104Z (4 months ago)
- Topics: data-visualization, database, inventory-management, logistics, optimization, python, streamlit, supply-chain, supply-chain-analytics, warehouse-optimization
- Language: Python
- Homepage: https://logitrack-tanishpoddar.streamlit.app/
- Size: 251 KB
- Stars: 0
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# LogiTrack : Inventory Management System
LogiTrack is a modern, web-based inventory management system built with Python and Streamlit. It provides real-time optimization of warehouse inventory distribution, order management, and supply chain analytics.
## a) Features
### 1) Dashboard & Analytics
- Real-time inventory tracking
- Warehouse utilization metrics
- Order fulfillment statistics
- Supply chain performance indicators
- Interactive data visualizations
### 🗺2) Inventory Distribution
- Multi-warehouse optimization
- Geographic distribution mapping
- Cost-effective allocation algorithms
- Real-time route visualization
### 3) Order Management
- Order tracking and status updates
- Priority-based fulfillment
- Delivery deadline monitoring
- Automated allocation suggestions
### 4) Warehouse Management
- Capacity utilization tracking
- Stock level monitoring
- Storage cost optimization
- Location-based analytics
### 5) Supplier Management
- Supplier performance metrics
- Reliability scoring
- Lead time tracking
- Quality assessment
## b) Getting Started
### Prerequisites
- Python 3.9 or higher
- pip package manager
- Git (optional)
### Installation
1. Clone the repository:
```bash
git clone https://github.com/tanishpoddar/logitrack.git
cd logitrack
```
2. Create and activate virtual environment (optional but recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Run the application:
```bash
streamlit run src/app.py
```
## c) Project Structure
```
logitrack/
├── src/
│ ├── app.py # Main Streamlit application
│ ├── backend/
│ │ ├── __init__.py
│ │ ├── data_loader.py # Data handling and processing
│ │ └── optimizer.py # Optimization algorithms
│ └── utils/
│ └── helpers.py # Utility functions
├── data/
│ ├── sample_warehouses.csv
│ ├── sample_sales.csv
│ └── other sample data...
├── tests/
│ └── test files...
├── docs/
│ └── documentation files...
├── requirements.txt
├── README.md
└── LICENSE
```
## d) Usage
1. **Login:** Enter your credentials to access the system.
2. **Data Source:** Choose between:
- Sample data
- Upload your data (CSV)
- Database connection
3. **Navigation:** Use the sidebar to access different features:
- Overview
- Inventory Management
- Order Management
- Supplier Management
- Optimization
- User Guide
## e) Data Format
### Warehouse Data (CSV)
```
warehouse_id,name,capacity,current_stock,location,storage_cost,latitude,longitude
W001,Mumbai Central,10000,7500,Mumbai,1200,19.0760,72.8777
W002,Singapore Hub,15000,12000,Singapore,1500,1.3521,103.8198
```
### Order Data (CSV)
```
order_id,date,product_id,quantity,delivery_deadline,status,delivery_latitude,delivery_longitude
ORD001,2025-03-24,P001,500,2025-03-26,Pending,19.0760,72.8777
ORD002,2025-03-24,P002,750,2025-03-25,Urgent,1.3521,103.8198
```
## f) Configuration
The system supports various configuration options:
- Database connections (MySQL, PostgreSQL, SQLite)
- Optimization parameters
- Visualization preferences
- Time zone settings
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
1. Fork the repository
2. Create your feature branch:
```bash
git checkout -b feature/AmazingFeature
```
3. Commit your changes:
```bash
git commit -m 'Add some AmazingFeature'
```
4. Push to the branch:
```bash
git push origin feature/AmazingFeature
```
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.
**Made with ❤️ by Tanish Poddar**