A Data-Centric Framework for Implementing Artificial Intelligence in Smart Manufacturing

The manufacturing segment is undergoing a rapid transformation as manufacturers integrate artificial intelligence (AI) and machine learning (ML). These technologies increasingly rely on data-driven architectures, which enable manufacturers to manage large volumes of data from machines, sensors, and other sources. As a result, they optimize operations, increase productivity, and reduce costs. This paper examines the role of AI in manufacturing through the lens of data-driven architecture. It focuses on the key components, challenges, and opportunities involved in implementing these systems. The paper explores various data types and architecture models that support AI-driven manufacturing, with an emphasis on real-time analytics. It highlights key use cases in manufacturing, including predictive maintenance, quality control, and supply chain optimization, and identifies the essential components required to implement AI successfully in smart manufacturing. The paper emphasizes the critical importance of data governance, security, and scalability in developing resilient and future-proof AI systems. Finally, it reviews a data-centric framework with essential components for manufacturers aiming to leverage these technologies to drive sustained growth and innovation.