<![CDATA[The rapid evolution of smart manufacturing ecosystems has intensified the need for intelligent architectures capable of supporting real-time decision-making, operational resilience, and sustainable industrial performance. This study investigates the effectiveness of Edge Intelligence within smart manufacturing environments through an empirical system-design and experimental validation approach. A three-layer architecture consisting of the Industrial Internet of Things layer, the edge intelligence layer, and the cloud orchestration layer was developed and evaluated under predictive maintenance, production scheduling, and anomaly detection scenarios. Performance assessment employed metrics including inference latency, response time, bandwidth consumption, prediction accuracy, throughput, resource utilization, reliability, resilience, and energy efficiency. The experimental results demonstrate that edge-enabled intelligence significantly improves manufacturing performance by reducing latency and communication overhead while increasing operational responsiveness, decision consistency, throughput, and system reliability. The architecture also enhances adaptive decision-making capabilities, strengthens human-machine collaboration, improves cybersecurity resilience, and contributes to environmental sustainability through more efficient resource utilization and reduced carbon emissions. The findings establish Edge Intelligence as a strategic ecosystem capability that enables resilient, adaptive, human-centric, and sustainable manufacturing systems aligned with the emerging objectives of Industry 5.0. ]]>
Copyrights © 2026
