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Integrated Quality Monitoring and Discrepancy Identification System

Project Overview

This project is a full-stack application designed to enhance quality control processes through an Integrated Quality Monitoring and Discrepancy Identification System. Utilizing advanced machine learning algorithms and data analysis techniques, this system is adept at detecting anomalies and providing real-time insights for quality improvements in various processes.

Core Objectives

  • To provide real-time monitoring of quality control processes.
  • To implement an automated system for anomaly detection and alert generation.
  • To offer a comprehensive data visualization and analytics dashboard.
  • To ensure seamless integration with existing quality management systems.

Technology Stack

  • Frontend: Next.js, HTML/CSS
  • Backend: Python, Flask
  • Machine Learning: TensorFlow
  • Data Processing: Pandas
  • Database: PostgreSQL

Features

  1. Real-Time Monitoring: Continuous tracking of quality control measures.
  2. Anomaly Detection: Automated detection of discrepancies using machine learning.
  3. Interactive Dashboard: User-friendly interface for data visualization and analytics.
  4. Integration Capability: Compatible with existing quality management frameworks.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

  • Git
  • Python
  • Node.js
  • A suitable IDE (e.g., VSCode)

Installation

  1. Clone the Repository

    git clone https://github.com/mohitvdx/Integrated-Quality-Monitoring-and-Discrepancy-Identification-System/tree/feature

  2. Install Dependencies

    • Navigate to the project directory and install Python dependencies: 
      pip install -r requirements.txt
    • Install Node.js dependencies for the Next.js frontend: 
      npm install

  3. Run the Application

    • In a new terminal, launch the Next.js frontend: 
      npm run dev

  4. Access the Application

    • The dashboard is accessible at http://localhost:3000 (default Next.js port).

Contributors

  • Mohit Verma
  • Nitin Yadav
  • Suryansh Yagnik
  • Satwik Singh
  • Nishita Singh

Acknowledgments

Special thanks to the team members and mentors at the Smart India Hackathon - SIH2023, who inspired and supported this project.

Project Insights

Developed during a 36-hour hackathon, this project involved collaboration with a diverse team of innovators, addressing the challenge of identifying discrepancies in Quality Monitoring Data on OMMAS. Key outcomes include:

  1. Machine Learning Algorithm: Developed to analyze data and identify discrepancies.
  2. Customizable Dashboard: For visualizing and comparing data from various sources.
  3. Automated Data Processing Pipeline: For efficient handling of large data volumes.
  4. NLP Techniques: Implemented for in-depth data analysis and discrepancy identification.

Future Scope

  • Enhancement of the machine learning model for broader anomaly detection.
  • Expansion of the data processing pipeline to include additional data sources.
  • Further development of the dashboard for more interactive features.
  • A new model revamp using BaaS (Backend as a Service) and FaaS (Function as a Service) technologies.

This README is designed to provide a comprehensive overview of the Integrated Quality Monitoring and Discrepancy Identification System, a full-stack project based on Next.js. It outlines the project's objectives, features, technology stack, installation guide, and acknowledgments, offering a clear guide for anyone interested in understanding or contributing to the project.