Digital Twins in Manufacturing: Analysis of Challenges, Successes, and Future Advances

Digital twins (DTs) are transforming the manufacturing sector by enabling real-time monitoring, predictive maintenance, and process optimization. This paper systematically reviews the ten most cited research papers on digital twins in manufacturing, identifying key themes, successes, challenges, and future research directions. While rooted in the manufacturing domain, the findings reveal insights highly transferable to renewable energy and power systems - particularly in areas such as data-driven decision-making, AIdriven predictive modelling, and cyber-physical system integration. Notably, the technologies and methodologies explored - such as real-time data synchronization, simulation-based planning, and AI-enhanced analytics - can support smart grid optimization, renewable energy forecasting, and infrastructure resilience. The paper advocates for greater crosssector adoption of digital twin frameworks, particularly in energy system planning, energy efficiency strategies, and sustainability-driven innovation. This positions digital twins as not only pivotal for Industry 4.0 but also as enablers of the global transition toward secure, efficient, and intelligent energy ecosystems.