The Smart Inventory of Raw Materials is an AI + IoT project that combines Production dashboard and Machine sensing, built with Raspberry Pi. The project follows a clean, modular pipeline where sensor data acquisition, AI processing, and presentation stay separated, making it easy to test, extend, and present. It showcases practical AI and IoT techniques while producing a working, demo-ready system.
Manual monitoring and decision making for this task is slow, inefficient, and cannot scale across many devices. Without an AI + IoT approach built on Machine sensing and Raspberry Pi, users cannot collect data from the physical world and act on it intelligently in real time.
This project applies AI + IoT techniques through Production dashboard, orchestrated with Raspberry Pi and Machine sensing. The system is designed for reliability and intelligence, with sensing, connectivity, AI analysis, and clear evaluation. It produces consistent, reusable results and can be adapted to related smart applications with minimal changes.
Raspberry Pi
Factory dashboard
ESP32 / Arduino / Raspberry Pi
MQTT / HTTP
Cloud dashboard
Machine learning models
ESP32 / PLC
Vibration sensor
Temperature sensor
Modular IoT pipeline around Production dashboard and Machine sensing
Configurable sensor and AI settings
Real-time data collection and monitoring
Clear alerts, logging, and error handling
Reusable components for related smart systems
AI based decision and prediction
The project is layered: the sensing layer acquires data through Production dashboard; the connectivity and AI layer processes it with Raspberry Pi and Machine sensing; and the output layer monitors and presents results via AI fault prediction. Shared connectivity, alerting, and dashboard modules support all layers, keeping the system robust and easy to extend.
Set up the hardware, sensors, and development environment.
Build the sensor data acquisition layer with Production dashboard.
Implement connectivity and AI processing using Raspberry Pi and Machine sensing.
Add the dashboard and alert layer via AI fault prediction.
Wire up end-to-end flows and add error handling and retries.
Test with real data, tune AI models, and refine the system.
Package the project, document it, and prepare the demo and viva report.
Build production-style AI + IoT applications
Apply Fault detection and Monitoring machine health
Connect and control IoT hardware
Analyze sensor data with machine learning
Present and defend a complete AI + IoT project in viva
Add edge AI for on-device decisions
Add more sensors and cloud services
Add mobile app control
Deploy on scalable cloud infrastructure
The Smart Inventory of Raw Materials delivers a complete AI + IoT workflow — from sensor data acquisition and connectivity to AI analysis and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand AI and IoT skills.
