Enhancing the manufacturing processes and productivity in Industry 5.0 with the integration of machine learning and cyber-physical systems

Purpose This paper aims to examine the synergistic integration of Cyber-Physical Systems (CPS) and Machine Learning (ML) as a foundational enabler for Industry 5.0, focusing on creating human-centric, sustainable and resilient manufacturing ecosystems. Design/methodology/approach The study utilizes a synthesis and review approach, analyzing recent advances in ML-driven CPS applications such as workflow optimization and predictive maintenance, alongside enabling technologies like 5G and digital twins. Findings The findings show that the integration of ML in multi-tier Edge-Fog-Cloud CPS brings significant operational advantages for the shop floor, such as sub-millisecond real-time control, more efficient human–robot interaction and a 10–20% reduction in energy used by the shop floor. But, the benefits of such physical manufacturing solutions rely on the need to work through a number of operational challenges. These include formally verifying non-deterministic ML policies, reducing IT/OT cybersecurity threats, including data poisoning and signal spoofing, reducing data heterogeneity in Federated Learning (FL) and lowering high deployment costs for small and medium-sized enterprises (SMEs). Research limitations/implications Future implementation requires addressing the need for Explainable AI (XAI) for transparency, FL for privacy and reinforcement learning for human-in-the-loop control. Practical implications The proposed paradigm is applicable in smart factories, autonomous production lines and supply chain optimization, helping manufacturers maximize asset output and reduce environmental impact. Originality/value This paper highlights the essential shift from “technology-driven” (Industry 4.0) to “value-driven” (Industry 5.0) manufacturing, identifying the CPS-ML convergence as the critical engine for this transition.