A System-dynamic-based Model to Study the Effect of Singular AWS Bucket Management Big Data Architecture into the Automotive Industry

Industry 4.0 is a paradigm that relies on the latest production and service evolution stage, with Big Data technology playing a pivotal role. This data volume enables interpreting significant insights through latent data mining to enhance decision-making capabilities. Although the use of Big Data technology is becoming common and adopted in manufacturing facilities, more research is needed on the impact of implementing this paradigm in organizations. Regardless of Big Data's maturation, its extensive economic influence on industrial performance is yet to be fully understood. This gap highlights the necessity of several analyses to mitigate the risks associated with technological investments. This paper proposes a System-Dynamic-based model to study the economic impact of Big Data architecture in the automotive industry. The proposed model considers sensor data from various vehicles into a singular AWS bucket. This architecture facilitates comprehensive data analysis and reporting via AWS Quicksight service using the structure of a Redshift data warehouse. The proposed model serves as a strategic tool for technological assessment and a method for effective Big Data utilization in the automotive sector.