🌆 City-Wide Dark Store Network Projection

📚 Table of Contents

1. Overview
2. Features
3. Technologies Used
4. Project Structure
5. Setup Instructions
6. Screenshots
7. Future Enhancements
8. Contributions
9. License

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🚀 Overview

This project models and optimizes a City-Wide Dark Store Network, focusing on:

• Efficient inventory distribution
• Demand forecasting
• Real-time delivery optimization

It aims to enhance operational efficiency, reduce costs, and improve customer satisfaction by leveraging cutting-edge technologies and data-driven insights.

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🛠️ Features

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🔧 Technologies Used

• Programming Language: Python
• Backend: Flask
• Database: SQLite/MySQL
• Data Analysis: Pandas, NumPy
• Visualization: Matplotlib, Seaborn
• Mapping API: Google Maps API

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🗂️ Project Structure

city-wide-dark-store-network-projection/
│
├── app.py                     # Main Flask application
├── templates/                 # HTML templates for the UI
├── static/                    # Static files (CSS, JS, images)
├── database/                  # Database and schema files
├── utils/                     # Utility scripts for forecasting, routing
├── tests/                     # Unit and integration tests
└── README.md                  # Project documentation

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🛠️ Setup Instructions

Prerequisites

• Python 3.9+
• Pip (Python Package Installer)
• API key for Google Maps (if using mapping features)

Steps

1. Clone the repository:

   git clone https://github.com/santheartist/city-wide-dark-store-network-projection.git
   cd city-wide-dark-store-network-projection
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the application:

   python app.py
   

4. Open your browser and navigate to http://localhost:5000.

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🖼️ Screenshots

Dashboard

Inventory Overview

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📈 Future Enhancements

• AI-Powered Demand Forecasting: Incorporate machine learning models for more accurate predictions.
• Third-Party Integration: Connect with external delivery platforms.
• Dynamic Routing: Integrate real-time traffic and weather data.

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🤝 Contributions

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a feature branch.
3. Submit a pull request with detailed explanations of your changes.

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📜 License

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

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🛠️ Maintainer

Sanchit Panda

• GitHub
• LinkedIn
• Email