TY - GEN
T1 - AI in Manufacturing
T2 - A comprehensive Keywords and Topics Analysis
AU - Straßer, Sonja
AU - Tripathi, Shailesh
AU - Brunner, Manuel
N1 - Publisher Copyright:
© 2024 The Authors. Published by Elsevier B.V.
PY - 2025
Y1 - 2025
N2 - Artificial Intelligence (AI) is among the advanced technologies driving significant transformations in the manufacturing sector. As a consequence, there has been a substantial increase in scholarly publications focusing on AI applications in manufacturing in recent years. This paper investigates the diverse applications of AI in manufacturing through a comprehensive analysis of scientific literature. The study employs metadata analysis, including titles, keywords, authors, and affiliations, to investigate regional variations and thematic trends in the field. Keywords are first analyzed for significance and categorized into 11 distinct categories. Significant keywords specific to each country are then identified to highlight individual research areas. Topic modeling is utilized to uncover nine key topics and their respective proportions across countries. Finally, clustering based on topic distributions identifies countries with similar research interests within AI applications in manufacturing. The theoretical implications emphasize the need to develop interdisciplinary approaches that reflect AI's transformative role in reshaping manufacturing. As AI drives shifts in technology, economics, and sustainability, it calls for an integration of new concepts like AI-led smart manufacturing and global innovation ecosystems, which are essential for understanding the broader societal and economic impacts. These shifts also underscore the importance of examining global technological convergence and international research collaborations.
AB - Artificial Intelligence (AI) is among the advanced technologies driving significant transformations in the manufacturing sector. As a consequence, there has been a substantial increase in scholarly publications focusing on AI applications in manufacturing in recent years. This paper investigates the diverse applications of AI in manufacturing through a comprehensive analysis of scientific literature. The study employs metadata analysis, including titles, keywords, authors, and affiliations, to investigate regional variations and thematic trends in the field. Keywords are first analyzed for significance and categorized into 11 distinct categories. Significant keywords specific to each country are then identified to highlight individual research areas. Topic modeling is utilized to uncover nine key topics and their respective proportions across countries. Finally, clustering based on topic distributions identifies countries with similar research interests within AI applications in manufacturing. The theoretical implications emphasize the need to develop interdisciplinary approaches that reflect AI's transformative role in reshaping manufacturing. As AI drives shifts in technology, economics, and sustainability, it calls for an integration of new concepts like AI-led smart manufacturing and global innovation ecosystems, which are essential for understanding the broader societal and economic impacts. These shifts also underscore the importance of examining global technological convergence and international research collaborations.
KW - Artificial Intelligence
KW - Data Analytics
KW - Industry 4.0
KW - Keyword Analysis
KW - Manufacturing
KW - Topic Modeling
UR - http://www.scopus.com/inward/record.url?scp=105000527082&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2025.01.312
DO - 10.1016/j.procs.2025.01.312
M3 - Conference contribution
VL - 253
T3 - Procedia Computer Science
SP - 2522
EP - 2536
BT - Procedia Computer Science
ER -