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
Aug 2026· Journal of Sustainable Smart Materials and Structural Systems· 0 citations· 131 references
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.
Findings indicate that AI-enabled digital twins significantly improve equipment reliability, reduce unexpected failures, enhance resource utilization, and enable proactive manufacturing strategies, however, challenges related to interoperability, cybersecurity, computational complexity, data quality, and governance remain critical barriers to widespread industrial adoption.
H. Mahmood· European International Journ...· 0 citations
The rapid advancement of digital technologies has fundamentally transformed how small and medium-sized enterprises (SMEs) make strategic decisions and expand into international markets. This review synthesizes current evidence on the role of artificial intelligence (AI), machine learning (ML), information systems, engineering applications, Industry 4.0 technologies, and big data analytics in supporting smart decision-making for SME internationalization. The article examines the theoretical foundations of SME internationalization, the evolution of AI-enabled decision support, digital transformation strategies, intelligent manufacturing, supply chain digitalization, and integrated decision-making frameworks that enhance organizational agility and competitiveness. It further evaluates the organizational factors influencing technology adoption, including AI readiness, data quality, knowledge management, and dynamic capabilities, while discussing key implementation challenges related to governance, resource constraints, and technological integration. A comparative synthesis of recent studies highlights how integrated digital capabilities improve strategic planning, market intelligence, operational efficiency, risk management, and innovation performance. The review concludes that smart decision-making frameworks provide SMEs with a comprehensive foundation for sustainable digital transformation and international growth by combining intelligent technologies with organizational capabilities. The findings offer a consolidated perspective for researchers, practitioners, and policymakers seeking to strengthen evidence-based managerial decision-making and enhance the global competitiveness of SMEs.
Sevdie Alshiqi, Mehdi Safaei, Juan de Dios Aguilar Sánchez et al.· Journal of Intelligent Decis...· 0 citations
The rapid advancement of Industry 4.0 has transformed conventional manufacturing into intelligent smart factories by integrating Industrial Internet of Things (IIoT), cyber-physical systems, cloud computing, and artificial intelligence (AI). As manufacturing environments become increasingly complex, traditional human-driven decision-making is insufficient for real-time production optimization. Autonomous Decision Support Systems (ADSS) address this challenge by combining AI, machine learning, digital twins, edge computing, and predictive analytics to enable intelligent, data-driven decision-making with minimal human intervention. This paper presents a scalable ADSS framework that integrates IIoT, edge-cloud computing, and digital twin technology for real-time monitoring, predictive maintenance, dynamic scheduling, and autonomous production optimization. The proposed architecture includes data acquisition, preprocessing, feature engineering, predictive analytics, decision optimization, autonomous execution, and continuous learning. Reinforcement learning and explainable AI improve decision accuracy, adaptability, and transparency, while federated learning enhances data privacy and reduces communication latency. Experimental results demonstrate significant improvements in production efficiency, equipment utilization, predictive maintenance, energy efficiency, quality control, and manufacturing responsiveness compared to conventional decision support systems. The proposed framework provides a scalable foundation for Industry 5.0, enabling sustainable, resilient, and intelligent manufacturing through seamless collaboration between human expertise and autonomous AI systems.
Jose Fernandez, Marta Silva· International Journal of Int...· 0 citations
The findings show that machine learning, deep learning, computer vision, computer vision, reinforcement learning, knowledge-driven approaches, digital twins, and explainable AI contribute to improvements in predictive maintenance, quality inspection, production scheduling, adaptive safety, and collaborative decision-making.
Zaliha Baso, N. Yadav, P. Faujdar· Cureus Journal of Computer S...· 0 citations
This review demonstrates that GenAI has substantial potential to support intelligent, sustainable, and data-driven civil engineering practices while emphasizing the need for human expertise and responsible AI implementation.
M. M.· Journal of Structural Techno...· 0 citations
Intelligent process design and the integration of new technologies are necessary for smart manufacturing systems to meet high-performance standards, which include resilience, short lead times, high due date reliability, and tailored items for every customer. The planning and control of such smart manufacturing systems necessitate several instances of human decision-making. The increasing adoption of mobile technologies is transforming operations management by enabling real-time connectivity, responsiveness, and human-centered decision-making in manufacturing environments. While prior smart manufacturing models have largely emphasized automation and digital systems, many overlook the critical role of human judgment, experience, and adaptability supported through mobile technology. This study develops an interactive framework that integrates human intelligence with mobile technology–enabled decision support systems to enhance operational flexibility, productivity, and sustainability in manufacturing operations. Grounded in operations management principles, the framework illustrates how mobile platforms facilitate real-time information access, collaborative problem-solving, and adaptive learning across production processes. Empirical findings indicate that mobile technology–supported human decision-making leads to superior operational performance compared to traditional, technology-independent approaches. Results further demonstrate that mobile-enabled automation and connectivity, alongside human and organizational capital, positively contribute to green value creation in industrial firms. However, excessive reliance on automated mobile systems may constrain human creativity and situational judgment, highlighting the importance of balanced integration between human capabilities and mobile technologies. This study contributes to the operations management literature by clarifying how mobile technology–enabled human augmentation supports efficient, ethical, and sustainable manufacturing performance.
Halim Mad Lazim, Noor Hidayah Abu, Aizul Nahar Harun et al.· Int. J. Online Biomed. Eng.· 0 citations
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