Automatic simulation models generation in industrial systems: A systematic literature review and outlook towards simulation technology in the Industry 5.0

Simulation models are a crucial enabling technology for decision support in the ongoing industrial digitalization hype. Within Industry 4.0, simulations are extensively utilized, providing insights into industrial behavior and responses. As we progress towards Industry 5.0, simulation models continue to play a pivotal role in achieving sustainable, resilient, and human-oriented industrial systems. However, a persistent challenge within Industry 4.0/5.0 is the substantial dynamism of industrial environments. This dynamic and complex landscape necessitates the development of adaptive solutions capable of swiftly responding to the volatile process requirements of modern industrial systems. To this end, Automatic Simulation Model Generation (ASMG) offers an innovative methodological framework to address this practical challenge in the development of industrial simulation models. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this research systematically reviews the current state-of-the-art in ASMG. Complemented by a bibliometric and content analysis of 61 articles spanning more than two decades (from 2000 to 2023), the paper evaluates ASMG’s progression and application in manufacturing through four research questions focusing on ASMG development strategies, objectives, essential data, and developing environments. Ultimately, this article provides valuable insights into ASMG perspective for industrial simulation specialists and offers guidelines for future developments in the era of Industry 5.0. • Automatic Simulation Model Generation (ASMG) for industrial and manufacturing systems 4.0/5.0. • PRISMA methodology to systematically review and evaluate the state-of-the-art in ASMG. • ASMG modeling strategies, applications, and data integration. • Standardization of definitions and terminology for ASMG in industrial systems simulation. • Future insights and guidelines for academic researchers and industrial practitioners.