ADVANCED AI-DRIVEN SYSTEM FOR PERSONALIZED TREATMENT RECOMMENDATIONS: A TECHNICAL OVERVIEW

Abstract

The convergence of the Industrial Internet of Things (IIoT) with artificial intelligence has created unprecedented opportunities for real-time process optimization, yet manufacturers face significant challenges in processing and analyzing high-dimensional sensor data for immediate decision-making. This article presents an innovative cloud-based Industrial Process Optimization Engine that combines sensor analytics, equipment monitoring, and machine learning to provide real-time, equipment-specific operational recommendations while ensuring scalability and manufacturing accuracy. Processing data from over 450,000 industrial sensors against 23,450 equipment parameters in under 185ms, maintaining 99.2% accuracy in fault prediction. Implementation across eight manufacturing networks demonstrates substantial improvements, including a 68.5% reduction in maintenance planning time and a 31.5% increase in first-pass yield rates. The architecture's scalability has been validated across 245 production facilities, supporting up to 50,000 concurrent sensor streams while maintaining sub-second response times. Article results show that the system significantly enhances manufacturing efficiency through real-time integration of sensor data with production systems, representing a major advancement in smart manufacturing implementation.