You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
ZeroWaste is an application to track supply chain and reverse logistics of products and calculate its negative environmental
and social impact across the value chain.
This application was developed by the team TechItUp, which won the First runner-up position at Smart India Hackathon 2022.
✨ Features
Manufacturer dashboard for predicting a product demand to reduce waste.
Tie up with waste recycling plants for easier recycling process.
Carbon footprint prediction for all products.
Recommendations and bough-return trends will be really helpful for a consumer/manufacturer to make business decision and plan recycling efficiently.
Connects needy NGOs to Manufacturers and Consumers.
Learning Modules to educate people on recycling.
Analytics Dashboard for stakeholders.
⭐ Top 5 Benefits
One man's trash is another man's treasure
The app provides 2 ways to put products back in the cycle. Donate to the NGO or Recycle at the Recycling Plant
Unsold products won’t be the cause of loss to anyone
Unsold products now can be sent back to the manufacturer and/or resold to the higher-demand areas.
Rewards for every act of kindness
Discounted scratchcards are provided to the customers on every donation.
Awareness in all dimensions
The app educates the customer by:
- Providing in-app D-I-Y videos to reuse/recycle at home.
- Predicting the required quantity of product based on historical data of sales.
- Showing the product's Environment Saving(ES) Values and how eco-friendly is the product that they are purchasing.
Advance Security
The application provides higher levels of security and verification for GST numbers to safeguard the customer from any fraudsters.
♻️ Use Cases
📱 Application
TechStack
Framework/language
Task
Flutter
Mobile Application development
Firebase
Real-time Cloud Database
TensorFlow
Quantity production recommendation model using deep neural networks