Real-Time Inventory Management System

Introduction

This project is a Real-Time Inventory Management System designed to track and update inventory levels for businesses in real time. It includes role-based access for Admins and Employees, providing features like inventory tracking, stock movement analysis, low-stock alerts, AI-powered inventory recommendations, customer analytics, supplier management, and advanced reporting capabilities. The system is built using the MERN stack (MongoDB, Express.js, React.js, and Node.js) with Python-based machine learning models.

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Features

Admin Features:

• Add, edit, and delete products
• View and manage inventory details
• Low-stock alerts for products
• Generate stock movement and sales reports
• Role management for users (add/edit/delete employees)
• AI-powered inventory recommendations and product bundling suggestions
• Customer analysis and segmentation
• Supplier performance analytics
• Advanced monthly and yearly reports
• Multiple bill generation capability
• Smart discount suggestions based on product expiry dates
• Fraud bill detection system

Employee Features:

• View inventory list with stock levels
• Low-stock alerts
• Real-time stock updates
• Access to inventory recommendations
• Generate multiple bills
• View customer analytics
• Access expiry-based discount suggestions

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Project Structure

Frontend:

• React.js: For building the user interface
• Material-UI: For UI components and styling

Backend:

• Node.js: Server-side logic
• Express.js: REST API framework
• Python: Machine learning models for inventory recommendations, customer analysis, and fraud detection

Database:

• MongoDB: For storing product details, user data, inventory records, customer analytics, and supplier information

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Installation and Setup

Prerequisites:

1. Node.js installed on your system
2. MongoDB running locally or on a cloud service (e.g., MongoDB Atlas)
3. Python 3.x with required ML libraries (scikit-learn, pandas, numpy, tensorflow)
4. Basic knowledge of JavaScript, Python, and the MERN stack

Getting Started

Environment Setup

1. Clone the repository:

git clone https://github.com/Abhinay2206/ims.git
cd ims

2. Set up environment variables:
   • Create a .env file in the root directory
   • Add the following variables:

MONGODB_URI=your_mongodb_connection_string
JWT_SECRET=your_jwt_secret
PORT=5000
NODE_ENV=development
PYTHON_API_PORT=5001

3. Install dependencies:

# Install Node.js dependencies
npm install

# Install Python dependencies
cd python-model
pip install -r requirements.txt

Running the Application

1. Start the MongoDB service:

# If using local MongoDB
mongod

2. Start the backend server:

# From the root directory
npm run server

3. Start the Python ML service:

# From the ml-service directory
python app.py

4. Start the frontend development server:

# From the root directory
npm run client

5. Access the application:
   • Frontend: http://localhost:5173
   • Backend API: http://localhost:5017
   • ML Service: http://localhost:5001

## Contributing

We welcome contributions to improve the Inventory Management System! Here's how you can contribute:

### Development Process

1. Fork the repository
2. Create a feature branch:
```bash
git checkout -b feature/your-feature-name

3. Follow the coding standards:

   • Use ESLint for JavaScript code
   • Follow PEP 8 for Python code
   • Write meaningful commit messages
   • Add comments for complex logic
   • Include unit tests for new features

4. Testing requirements:

   • Write unit tests for new features
   • Ensure all existing tests pass
   • Test across different browsers
   • Verify mobile responsiveness

Pull Request Process

1. Update documentation:

   • Add relevant comments to your code
   • Update README.md if needed
   • Document any new environment variables
   • Add API documentation for new endpoints

2. Submit your PR:

   • Provide a clear PR description
   • Link related issues
   • Include screenshots for UI changes
   • List any breaking changes

3. Code Review:

   • Address reviewer comments
   • Maintain a professional discussion
   • Make requested changes promptly

Reporting Issues

When reporting issues, include:

• Clear issue description
• Steps to reproduce
• Expected vs actual behavior
• Screenshots if applicable
• Environment details
• Related logs or error messages

Development Guidelines

Frontend

• Use functional components with hooks
• Follow Material-UI design patterns
• Implement responsive designs
• Use TypeScript for new components
• Follow the established state management pattern

Backend

• Follow RESTful API principles
• Implement proper error handling
• Add input validation
• Maintain API documentation
• Use async/await for asynchronous operations

Machine Learning Service

• Document model training procedures
• Include model performance metrics
• Save model artifacts properly
• Implement proper error handling
• Add logging for monitoring

Security Guidelines

• Never commit sensitive data
• Implement proper input validation
• Use parameterized queries
• Follow security best practices
• Regular dependency updates
• Implement rate limiting
• Use proper authentication

Release Process

1. Version Control:

   • Follow semantic versioning
   • Update CHANGELOG.md
   • Tag releases appropriately

2. Deployment:

   • Test in staging environment
   • Perform security checks
   • Update documentation
   • Create release notes

3. Post-Release:

   • Monitor for issues
   • Address critical bugs
   • Update status reports