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Inventory App using Machine Learning

Managing inventory efficiently is a critical challenge for businesses, especially those that rely on accurate monitoring of stock levels and real-time insights. The Inventory App leverages machine learning (ML) and AI to automate inventory tracking using image recognition and volume measurement. It simplifies processes such as fluid-level detection and inventory management, providing a scalable solution for warehouses, retail stores, and industries handling liquid stock.

Challenges

  1. Manual Inventory Tracking:
    • Traditional inventory systems require manual counting, which is prone to errors and inefficiencies.
  2. Fluid-Level Measurement:
    • Accurately estimating the remaining fluid inside containers is difficult without sophisticated tools.
  3. Real-Time Monitoring:
    • Businesses often lack real-time insights into stock levels, leading to delays in replenishment.
  4. Integration with Mobile Devices:
    • Creating a lightweight and accurate mobile application for real-time inventory management is challenging.

Our Solutions

The Inventory App addresses these challenges by implementing machine learning algorithms and leveraging mobile technology for inventory management.

  1. Image Recognition:
    • Integrated TensorFlow Lite to process images of bottles and containers.
    • Recognizes the type of bottle or container and matches it with pre-stored inventory details.
  2. Fluid-Level Measurement:
    • The app uses a scale feature to calculate the quantity of liquid inside bottles.
    • Trained ML models estimate the volume based on the bottle’s dimensions and the visible fluid level.
  3. Inventory Management:
    • Automatically updates inventory levels after detecting changes in stock through image recognition and volume calculations.
    • Provides real-time stock updates and low-stock alerts.
  4. Mobile Application:
    • Built with Android Studio for mobile compatibility.
    • A lightweight and user-friendly interface allows users to scan and monitor inventory on the go.

Technology Slack

Android Studio

Js

Tensorflow Lite

Impacts

Scenario 1: Monitoring a Warehouse’s Liquid Stock

  • Objective:
    • Automate the tracking of liquid inventory in a warehouse.
  • Process:
    • Warehouse workers use the app to scan bottles with the camera.
    • The app recognizes the bottle type using TensorFlow Lite.
    • It measures the remaining fluid level using the scale feature.
    • Inventory levels are automatically updated in the backend.
  • Outcome:
    • Reduced manual effort by 75%.
    • Improved accuracy in inventory tracking by 90%.
    • Real-time low-stock alerts led to timely replenishments.

Scenario 2: Real-Time Tracking for Retail Stores

  • Objective:
    • Simplify inventory updates in retail stores handling liquid products.
  • Process:
    • Store employees use the mobile app to scan shelves at the end of each day.
    • The app calculates the remaining stock and updates the system automatically.
  • Outcome:
    • Reduced inventory check time from hours to minutes.
    • Enhanced efficiency in daily operations

Benefits

  1. Improved Efficiency:
    • Automated fluid-level detection and inventory updates save time and effort.
  2. Increased Accuracy:
    • Eliminates human errors in stock tracking.
  3. Real-Time Insights:
    • Provides instant updates on inventory levels, enabling faster decision-making.
  4. Mobile Accessibility:
    • Lightweight and intuitive mobile application makes inventory management convenient and portable.
  5. Scalability:
    • Adaptable to various industries, from retail to manufacturing and warehousing

Future Scope

  1. Advanced ML Models:
    • Integrate transformer-based models for even better fluid-level detection accuracy.
  2. Cloud Integration:
    • Add cloud storage for centralized inventory management and multi-device synchronization.
  3. IoT Connectivity:
    • Connect with IoT devices like smart shelves for automated stock updates without manual scans.
  4. Predictive Analytics:
    • Use AI to predict stock requirements based on historical data and consumption trends

Conclusion

The Inventory App revolutionizes inventory management by combining image recognition and machine learning into a mobile-first solution. With features like real-time stock updates, fluid-level measurement, and seamless integration with inventory systems, it empowers businesses to manage their inventory more effectively, saving both time and resources.