Decision Intelligence in Collaborative Intelligent Manufacturing of smart and sustainable materials: A Comprehensive Review of Artificial Intelligence, Optimization, Digital Twins, and Human-Centered Decision-Making
TL;DR
The findings indicate that effective Decision Intelligence requires the seamless integration of artificial intelligence, optimization, digital twins, knowledge graphs, explainable AI, and Human-in-the-Loop AI within a unified decision ecosystem that combines computational intelligence with human judgment.
Abstract
The rapid transition from Industry 4.0 to Industry 5.0 has significantly increased the complexity of manufacturing systems while accelerating the development and deployment of smart and sustainable materials in advanced industrial applications. These emerging materials require intelligent decision-making approaches that integrate data, advanced analytics, optimization, digital twins, and human expertise throughout their design, production, monitoring, and lifecycle management. Although artificial intelligence, machine learning, digital twins, and mathematical optimization have independently advanced intelligent manufacturing, their isolated application often limits adaptability, explainability, and resilience. This study presents a systematic literature review of Decision Intelligence (DI) in collaborative intelligent manufacturing following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, synthesizing evidence from 140 peer-reviewed publications. The review comprehensively examines the evolution of Decision Intelligence, core enabling technologies, layered architectures, industrial applications, human-centered decision-making, implementation challenges, and emerging research trends. The findings indicate that effective Decision Intelligence requires the seamless integration of artificial intelligence, optimization, digital twins, knowledge graphs, explainable AI, and Human-in-the-Loop AI within a unified decision ecosystem that combines computational intelligence with human judgment. The proposed framework and research roadmap provide valuable theoretical insights and practical guidance for developing resilient, explainable, and human-centric Decision Intelligence systems that support the realization of Industry 5.0 manufacturing for smart and sustainable materials.