Process Optimization in Smart Manufacturing via Data-Driven Approaches and Digital Twin Simulations

Abstract

The advent of Industry 4.0 has necessitated a paradigm shift in manufacturing operations, moving from static, linear production lines to dynamic, interconnected ecosystems. This paper explores the integration of data-driven methodologies with Digital Twin (DT) technology to achieve process optimization in smart manufacturing environments. By leveraging real-time data acquisition, advanced analytics, and high-fidelity virtual replications, we propose a comprehensive framework for predictive maintenance, resource allocation, and anomaly detection. The research delineates a methodology for constructing a bidirectional data flow between the physical shop floor and its digital counterpart, enabling instantaneous feedback loops that enhance decision-making capabilities. Through a detailed implementation study involving a discrete manufacturing assembly line, we demonstrate that this synergistic approach significantly reduces operational downtime and energy consumption while improving throughput. The findings suggest that the convergence of machine learning algorithms and digital twin simulations provides a robust solution to the stochastic challenges inherent in modern production systems.