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Optimising Washing Machine Manufacturing Supply Chain Using Data Lake

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Airbus Aerothon 5.0

Optimising Washing Machine Manufacturing Supply Chain Using Data Lake

The manufacturing company's supply chain process generates a significant amount of redundant intermediate data, leading to storage and sustainability issues. The data produced by various departments, including logistics, planning, and forecasting, needs to be consolidated efficiently and made accessible to relevant users. A solution is required to reduce redundancy, establish data authenticity, automate data stamping, and provide domain specific dashboards for data access and monitoring.


Implementation


Features

  • Multirole Authentication and Authorization
  • Data Redundancy Checks
    • Normalized Database
    • User defined constraints aligned to business requirements
    • Cron job scheduler to handle redundant data across departments after logical completion of cross functional business processes
  • Multiple Stakeholders
  • Interactive Dashboard
  • Responsive UI
  • Inventory Forecasting Model

Tech Stack

Backend

Frontend


Contributors

Sr. No. Name GitHub
1. Viraj Patidar @VirajPatidar
2. Somya Malgudi @Sage-2001
3. Om Khade @khadeom
4. Rishav Kumar @HappY-FaceS

Setup

# Clone the Repository
$ git clone https://github.com/VirajPatidar/Airbus-Aerothon-5.0.git 
$ cd Airbus-Aerothon-5.0

Backend

Setup: Linux, Unix

$ cd backend
$ pip install virtualenv
$ virtualenv env
$ source env/bin/activate
$ pip3 install -r requirements.txt

Setup: Windows

$ cd backend
$ pip install virtualenv
$ virtualenv env
$ .\env\Scripts\activate
$ pip install -r requirements.txt

Database

$ python manage.py makemigrations
$ python manage.py migrate
# Populate database with sample data via scripts
$ python manage.py scripts/populate_fabrication.py 
$ python manage.py scripts/machine.py 
$ python manage.py scripts/subassembly.py

Run the Backend Server

$ python manage.py runserver

Frontend

Install Node Modules

$ cd frontend
$ npm install
$ npm start