The emergence of Industry 5.0 represents a significant transformation in industrial paradigms by shifting emphasis from automation-centered productivity toward human-centric, sustainable, and resilient manufacturing ecosystems. Unlike previous industrial approaches that primarily focused on machine efficiency and large-scale automation, Industry 5.0 integrates human intelligence with advanced cyber-physical technologies to create adaptive and collaborative environments. This research paper explores the role of cyber-enabled modeling and intelligent decision support systems in advancing human-centric Industry 5.0 practices. The study conceptualizes how artificial intelligence, digital twinning, collaborative intelligence, social recommendation mechanisms, and computational decision models contribute to improved industrial decision-making and human-machine collaboration.
The paper develops a conceptual framework based on the synthesis of existing studies related to scholarly data mining, recommendation systems, social decision mechanisms, fuzzy decision models, and digital transformation practices. The research examines how cyber-enabled models facilitate real-time data interpretation, predictive analysis, and context-aware decision support in complex industrial environments. Digital twin-based approaches are analyzed as an essential foundation for representing physical assets, processes, and human interactions within intelligent industrial ecosystems. As highlighted by Philip (2024), the integration of digital twinning and artificial intelligence provides a pathway toward intelligent project delivery and Industry 5.0-oriented operational models by enabling simulation-driven planning and adaptive decision processes.
The study identifies that intelligent decision support systems can enhance industrial flexibility by combining machine intelligence with human expertise rather than replacing human participation. Context-aware recommendation approaches support knowledge discovery, while social network-based decision models enable collaborative problem-solving among distributed stakeholders. Furthermore, fuzzy decision-making techniques provide mechanisms for handling uncertainty and subjective judgments, which are critical characteristics of human-centric industrial environments.
The findings indicate that cyber-enabled modeling creates opportunities for improved operational visibility, proactive decision-making, and sustainable industrial development. However, challenges related to data quality, algorithmic transparency, cybersecurity risks, and human acceptance remain significant barriers to widespread implementation. This research contributes a theoretical perspective on integrating cyber intelligence with human-centered industrial practices and provides recommendations for future Industry 5.0 systems that balance technological capability with human creativity, ethical considerations, and organizational adaptability.