American Journal of Advanced Multidisciplinary Research and Innovation

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Digital Twin Technology for Smart Manufacturing and Industrial Optimisation

Author(s) Barbara Liskov
Country United States
Abstract The manufacturing sector is undergoing a profound transformation driven by the Fourth and Fifth Industrial Revolutions (Industry 4.0 and Industry 5.0). Rapid advancements in Digital Twin Technology (DTT), Artificial Intelligence (AI), Internet of Things (IoT), Big Data Analytics, Cloud Computing, Edge Computing, Cyber-Physical Systems (CPS), Machine Learning (ML), Robotics, and Industrial Automation have enabled the development of intelligent manufacturing environments capable of real-time monitoring, predictive maintenance, process optimisation, and autonomous decision-making. Traditional manufacturing systems, which often rely on periodic inspections and reactive maintenance, face limitations in efficiency, flexibility, and resource utilisation. Digital Twin Technology addresses these limitations by creating dynamic virtual replicas of physical assets, production lines, and industrial processes that continuously exchange data with real-world systems.
This study presents a comprehensive analysis of Digital Twin Technology for Smart Manufacturing and Industrial Optimisation. A qualitative analytical research methodology based on secondary data is employed to investigate digital twin architectures, predictive maintenance, intelligent production planning, quality control, energy optimisation, supply chain integration, and industrial decision support. The research evaluates how Digital Twin Technology improves manufacturing efficiency, operational resilience, sustainability, and strategic decision-making.
The findings indicate that Digital Twin-enabled manufacturing significantly enhances equipment monitoring, fault prediction, production scheduling, energy efficiency, inventory management, product quality, and operational transparency. Artificial Intelligence and Machine Learning improve predictive analytics and process optimisation, while IoT sensor networks provide continuous real-time data acquisition. Cloud and edge computing facilitate scalable industrial analytics, whereas blockchain strengthens data integrity and traceability across manufacturing ecosystems. Furthermore, integrating Digital Twins with collaborative robotics and autonomous systems supports adaptive production, mass customisation, and sustainable industrial development.
Despite these opportunities, significant challenges remain concerning interoperability, cybersecurity, implementation costs, data governance, workforce readiness, computational complexity, and standardisation. Future research should investigate explainable Digital Twins, quantum-enhanced manufacturing optimisation, autonomous industrial ecosystems, federated industrial intelligence, and carbon-neutral smart factories.
The study concludes that Digital Twin Technology provides a transformative framework for smart manufacturing and industrial optimisation by enabling intelligent, connected, and adaptive production systems capable of improving productivity, operational efficiency, sustainability, and global industrial competitiveness.
Keywords Digital Twin Technology, Smart Manufacturing, Industry 4.0, Industry 5.0, Artificial Intelligence, Industrial Optimisation, Internet of Things, Predictive Maintenance, Cyber-Physical Systems, Industrial Automation.
Field Engineering
Published In Volume 2, Issue 3, May-June 2020
Published On 2020-05-08
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