Applications of Big Data Analytics in Engineering Design, Simulation, and Operations Management

Abstract

The rapid digitalization of engineering systems has led to unprecedented growth in data generated across the lifecycle of engineered products and infrastructures. Sensors, simulation platforms, enterprise systems, and cyber-physical technologies continuously produce high-volume, high-velocity, and high-variety data that traditional analytical methods are unable to process effectively. Big data analytics has therefore emerged as a critical enabler in modern engineering practice, transforming design methodologies, simulation frameworks, and operations management strategies. This review examines the role of big data analytics in engineering applications, with emphasis on data-driven design optimization, large-scale simulation enhancement, predictive operations management, and decision-support systems. The paper discusses analytical architectures, machine learning models, and data integration strategies that support complex engineering workflows. Challenges related to data quality, computational scalability, model interpretability, and organizational adoption are critically analyzed. Emerging trends such as digital twins, real-time analytics, and autonomous decision systems are explored as future directions shaping data-centric engineering ecosystems.