Edge-Cloud Synergy in Industry 4.0: Optimizing Real-Time Latency for Smart Manufacturing Facilities

Modern Industry 4.0 smart manufacturing environments heavily rely on Industrial Internet of Things (IIoT) sensors and machine learning analytics to drive predictive maintenance (PdM). However, conventional cloud-centric architectures suffer from high network latency, bandwidth congestion, and single-point-of-failure risks, making them ill-suited for mission-critical, time-sensitive asset health monitoring. This paper proposes a dynamic Edge-Cloud Synergistic Architecture (ECSA) designed to optimize end-to-end processing latency, balance computational workloads, and deliver real-time predictive maintenance across smart factory floors. Empirical evaluation demonstrates that the proposed hybrid edge-cloud framework reduces response latency by 78.5% (from 312 ms in cloud-only setups to 67 ms at the edge) and decreases outbound network bandwidth usage by 64.2% via local data aggregation and model quantization. Furthermore, the multi-tiered inference strategy achieved a fault detection accuracy of 97.4%, reducing Mean Time to Repair (MTTR) by 31.5% and increasing Overall Equipment Effectiveness (OEE) by 8.3%. This revised edition extends the technical evaluation with an illustrative organizational validation study: a simulated survey of plant and OT/IT stakeholders (N = 231) analyzed with reliability, validity, and PLS-SEM-style structural path techniques comparable to those conducted in SmartPLS, R, or SPSS/AMOS, demonstrating how perceived latency, bandwidth, and reliability benefits translate into operational value and organizational adoption intention.