A System-Dynamics-Based Model to Study the Implementation of Big Data with ETL Process in the Automobile Manufacturing Industry

Industry 4.0 marks a transformative paradigm, heralding a new era for the production and service sectors by integrating Big Data technologies. This progression unlocks latent knowledge within vast datasets, empowering decision-making through advanced analytics. However, the literature lacks a comprehensive understanding of the impact of such integration on organizational structures. Despite Big Data's maturation, its economic influence on industry operations is not fully understood and warrants further investigation. Understanding the implications of technology adoption is crucial to minimize the risks associated with capital investments. This study proposes a System-Dynamics-based model of Big Data architecture utilizing vehicular sensor data warehouses. The model delineates the application of the Extract, Transform, Load (ETL) process via Amazon Web Services (AWS), employing the Glue service for data integration and Quicksight to visualize insights. This approach aims to understand the advantages and disadvantages of Big Data in the automobile manufacturing industry. Also, it provides a strategic foresight into technology investment and enhances the analytical capabilities within the manufacturing domain.