Knowledge flows in industry 4.0 research: a longitudinal and dynamic analysis

Abstract Industry 4.0 represents a significant shift in industrial practices, presenting unique opportunities to improve manufacturing via advanced digital technologies and sustainable processes. The rapid growth of Industry 4.0 research has uncovered a significant knowledge gap and emphasized the need for studies adopting dynamic and longitudinal perspectives to understand this field’s evolution comprehensively. This study meticulously analyzes 10,176 articles to investigate the thematic evolution and knowledge transfer mechanisms within Industry 4.0. The examination reveals four distinct sub-periods, each characterized by thematic transitions, starting with foundational themes such as simulation and cyber-physical systems, progressing to later focuses on cloud computing, convolutional neural networks, and digital twin technologies. As research progresses, themes like production facilities, monitoring, and security highlight the shift towards automation, real-time monitoring, and strong data security measures. Five primary thematic domains are identified: (1) core enablers of sustainable smart manufacturing, (2) innovation and strategic transformation, (3) smart and secure manufacturing systems, (4) advanced data-driven manufacturing technologies, and (5) AI-driven real-time monitoring and production. These domains illustrate a transition from fundamental enablers like the Internet of Things (IoT) to more intricate AI-based applications. The main path analysis indicates a shift in emphasis, moving from essential digital integration towards sustainability, digital transformation, and resource efficiency applications. The findings reveal significant implications and highlight Industry 4.0 as a driving force for sustainable and resilient industrial ecosystems.