Author(s): Richard Asamoah Oppong, Lambert Gameli Lotsu and Joseph Kwame Lewballah

Abstract: The increasing complexity of automotive assembly and metal-stamping production has exposed the limitations of conventional manufacturing systems that rely on isolated programmable logic controllers, supervisory control systems, periodic equipment inspection, and fragmented production databases. These disconnected environments hinder real-time operational visibility, delay fault detection, reduce Overall Equipment Effectiveness (OEE), and limit the ability of manufacturers to anticipate equipment degradation, tooling wear, process instability, and quality deviations. Emerging digital manufacturing technologies provide opportunities to transform these production environments into intelligent, interconnected, and self-optimizing systems. This review critically examines how Industrial Internet of Things (IIoT) devices, industrial automation, edge computing, cloud-based manufacturing platforms, and predictive analytics collectively enable continuous monitoring and autonomous decision support within automotive assembly and high-speed metal-stamping operations. The study analyses the integration of smart sensors, robotic assembly cells, servo presses, automated guided vehicles, machine vision systems, manufacturing execution systems, digital twins, and cloud analytics for synchronized production control and lifecycle asset management. An integrated smart-manufacturing architecture is proposed to connect cyber-physical production systems with real-time data acquisition, predictive maintenance, quality prediction, energy optimization, and production scheduling. The framework demonstrates how continuous data intelligence can minimize unplanned downtime, tooling failures, scrap generation, bottlenecks, and maintenance costs while improving throughput, process capability, manufacturing flexibility, traceability, and operational resilience. The review provides strategic guidance for accelerating Industry 4.0 implementation within modern automotive and precision metal-forming industries.

DOI: https://www.doi.org/10.22271/27078043.2026.v7.i2a.130

Pages: 01-17 | Views: 496 | Downloads: 252

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