Industry 4.0 Data Processing Requirements: Use Cases for Big Data Applications.

Abstract

By combining big data analytics, IoT, artificial intelligence (AI), and cyber-physical systems, Industry 4.0 has completely changed manufacturing and industrial processes. Efficient data processing has emerged as a crucial challenge as industrial systems produce enormous volumes of data in real-time. With an emphasis on real-time analytics, data storage architectures, scalability, security, and interoperability, this study examines the data processing needs crucial for Industry 4.0 applications. We highlight important use cases, such as supply chain optimization, quality control, predictive maintenance, and smart manufacturing, where big data applications spur innovation. Additionally, we examine how distributed processing frameworks, cloud computing, and edge computing are used to handle industrial data. In order to guarantee effective, scalable, and safe data management, the study also identifies potential solutions to problems like data integration, latency, and cybersecurity threats. This article offers useful insights for industries hoping to fully utilize big data in Industry 4.0 by addressing these aspects.