Smart Manufacturing Process Optimization Using Real-Time Data Analytics and Edge Computing

Abstract

The rapid advancement of Industry 4.0 technologies has transformed manufacturing environments by enabling intelligent automation, real-time monitoring, and data-driven decision-making for improved industrial productivity and operational efficiency. This study presents a smart manufacturing process optimization framework using real-time data analytics and edge computing to enhance production performance, resource utilization, and system responsiveness in modern manufacturing plants. The proposed framework integrates Industrial Internet of Things (IIoT) sensors, edge computing devices, cloud connectivity, and advanced data analytics techniques to process and analyze manufacturing data in real time. A data-driven methodology was implemented in which machine performance, production rates, energy consumption, process parameters, and equipment conditions were continuously monitored and analyzed using machine learning and predictive analytics models. Edge computing architecture was employed to reduce communication latency, enable decentralized processing, and support rapid decision-making at the operational level. Experimental and simulation results demonstrated significant improvements in production efficiency, process accuracy, energy optimization, and fault detection capability compared with conventional centralized manufacturing systems. The proposed framework also reduced network congestion, response time, and operational downtime through localized intelligent processing and adaptive control mechanisms. Furthermore, the integration of real-time analytics enhanced predictive maintenance performance, production scheduling, and quality control in dynamic manufacturing environments. The study concludes that combining edge computing with real-time data analytics provides an effective and scalable solution for smart manufacturing process optimization. The findings contribute to the development of intelligent industrial systems capable of achieving enhanced productivity, operational flexibility, sustainability, and reliable automation in next-generation manufacturing infrastructures.