Smart Computing for Industry 4.0: A Conceptual IoT–Edge Framework with a Simulated Smart-Manufacturing Case Study

Abstract Smart manufacturing under the Industry 4.0 paradigm increasingly depends on the convergence of the Internet of Things (IoT) and edge computing, which together promise sub-second decisions on the shop floor. Yet manufacturers still struggle to translate that promise into measurable gains in latency, energy use, and reliability. This paper proposes a conceptual four-layer IoT–edge framework, comprising perception, edge, fog, and cloud layers, in which time-critical analytics are executed close to assets while strategic workloads remain centralised. A simulated case study of a discrete-parts factory with 200 IoT sensors, four edge nodes, and one cloud server is built using synthetic process data generated over a 24-hour cycle. Three architectures, namely cloud-only, edge-only, and the proposed hybrid configuration, are compared in terms of average response time, energy consumption, and detection accuracy of anomalous events. Numerical experiments show that the hybrid framework reduces mean latency by approximately 71% and energy consumption by 38% relative to the cloud-only baseline, while maintaining detection accuracy above 96%. Managerial implications and limitations are discussed.