Understanding digital transformation in advanced manufacturing and engineering: A bibliometric analysis, topic modeling and research trend discovery. (October 2021)

• Record Type: Journal Article
• Authors: Lee, Ching-Hung; Liu, Chien-Liang; Trappey, Amy J.C.; Mo, John P.T.; Desouza, Kevin C.
• Is Part Of: Advanced engineering informatics. Volume 50(2021)
• Publication Date: 2021-10
• Subjects: Digital transformation -- Advanced manufacturing and engineering -- Bibliometric analysis -- Topic modeling -- Systematic review; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101428
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Present a literature survey on digital transformation (DT) studies in advanced manufacturing and engineering. Address the challenge of DT by using bibliometric analysis and topic modeling to analyze the literature (i.e., 99 articles). Six dominant topics are obtained and detailed analysis is provided to draw meaningful insights. Give a research agenda that guides future disciplinary research on the DT of advanced manufacturing and engineering. Abstract: Digital transformation (DT) is the process of combining digital technologies with sound business models to generate great value for enterprises. DT intertwines with customer requirements, domain knowledge, and theoretical and empirical insights for value propagations. Studies of DT are growing rapidly and heterogeneously, covering the aspects of product design, engineering, production, and life-cycle management due to the fast and market-driven industrial development under Industry 4.0. Our work addresses the challenge of understanding DT trends by presenting a machine learning (ML) approach for topic modeling to review and analyze advanced DT technology research and development. A systematic review process is developed based on the comprehensive DT in manufacturing systems and engineering literature (i.e., 99 articles). Six dominant topics are identified, namely smart factory, sustainability and product-service systems, construction digital transformation, public infrastructure-centric digital transformation, techno-centric digital transformation, and business model-centric digital transformation. The study also contributes to adopting and demonstrating the ML-based topic modeling for intelligent and systematic bibliometric analysis, particularly for unveiling advanced engineering research trends through domain literature.