Manufacturing Execution Systems (MES) have evolved significantly over the past few decades, serving as a critical link between shop-floor operations and enterprise resource planning. Initially focused on reactive strategies—offering real-time visibility and control based on immediate conditions—MES have transitioned toward predictive capabilities driven by Industry 4.0 technologies. The integration of big data analytics, the Internet of Things (IoT), machine learning, and cloud computing has enabled autonomous factories to leverage MES for proactive and adaptive decision-making. This paper explores the transformation of MES from reactive to predictive systems, detailing the technological enablers, including IoT sensor networks, machine learning algorithms, digital twins, and cyber-physical systems. A methodology for designing and implementing a predictive MES architecture is presented, supported by empirical findings from a pilot implementation. Results demonstrate improvements in production efficiency, reduced downtime, and optimized resource use. Challenges such as data security, integration complexities, and workforce training are discussed, alongside future directions involving cognitive MES and AI-driven manufacturing. The paper also highlights environmental sustainability benefits, positioning predictive MES as a cornerstone of modern autonomous factories.

Manufacturing Execution Systems, Autonomous Factories, Industry 4.0, Predictive Analytics, Cyber-Physical Systems, IoT

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