MES-Enabled Cyber-Physical Production Systems: Accelerating Automotive Line Efficiency via Real-Time Decision Frameworks
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I2P109Keywords:
Cyber-Physical Production Systems (CPPS), Manufacturing Execution System (MES), Industry 4.0, Real-Time Decision-Making, Automotive ManufacturingAbstract
The high pace of digitalization of the manufacturing process in the automotive sector creates the need to integrate modern technologies that foster efficient operations, minimize waste, and raise the level of market responsiveness. In combination with the Cyber-Physical Production Systems (CPPS), the Manufacturing Execution Systems (MES) provide a revolutionary path to an optimal production process flow in real-time. In this paper, the author will discuss the implementation and revealing analysis of MES-enabled CPPS through their application in enhancing efficiency in an automotive production line. The suggested real-time decision-making models have used machine data, context-based data, and predictive analysis to make dynamic planning of production processes. In contrast to traditional manufacturing systems, where latency and fragmentation in the decision cycle are in place, MES-integrated CPPS creates a closed-loop ecosystem, which offers smooth vertical and horizontal integration of shop floor and enterprise-level IT infrastructure. The paper provides a modular decision-making process concept in real time, proves the modelled concept with a case study in a medium-sized car assembly plant, and provides benchmarks of efficiency improvement. The reason the real-time decision logic can function is that it is sustained by edge-computing devices, integrated digital twins, and machine learning models that forecast bottlenecks and proactively adjust the workflow. We demonstrate that we can improve Overall Equipment Effectiveness (OEE) by up to 23.5 per cent, reduce the Mean Time To Repair (MTTR) by 19.3 per cent, and make the production schedule more accurate by 28.7 per cent. Moreover, the study has identified interoperability standards, system architecture, and communication protocols that are critical to scalable deployment. These results are supported by a combination of simulation-empirical methodology, which utilises the Arena simulation program, and the integration of sensor-based data in real-life settings. The study stresses the potential of MES-enabled CPPS as a staple of Smart Manufacturing through Industry 4.0 and opens the path towards autonomous production through the automotive industry
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