Digital twin technology advancing industry 4.0 and industry 5.0 across sectors
Ocident Bongomin, Mwewa Chikonkolo Mwape, Nonsikelelo Sheron Mpofu, Brendah Kembabazi Bahunde, Richard Kidega, Ibrahim Luqman Mpungu, Godias Tumusiime, Cynthia Awuor Owino, Yannick Marnaigue Goussongtogue, Aregawi Yemane, Proscovia Kyokunzire, Clement Malanda, Jimmy Komakech, Dan Tigalana, Onesmas Gumisiriza, George Ngulube
Abstract
Digital Twin (DT) technology is transforming industrial systems by integrating physical assets with digital models, enabling real-time monitoring, predictive analytics, and process optimization, particularly within the framework of Industry 4.0 (I4.0). As the global industrial landscape shifts toward Industry 5.0 (I5.0), DTs are increasingly being redefined to support human-centric innovation, sustainability, and system resilience. This review examines the evolving role of DTs in bridging the efficiency-driven goals of I4.0 with the inclusive, sustainable objectives of I5.0. It explores ten enabling technologies such as artificial intelligence (AI), internet of things, blockchain, cloud and edge computing, and extended reality, while discussing both the opportunities and challenges posed by I5.0. The study emphasizes key principles of DTs, including real-time synchronization, feedback mechanisms, and lifecycle integration. A detailed sectorial analysis across manufacturing, infrastructure, energy, transportation, mining, agriculture, and healthcare illustrates how DTs are being applied in diverse contexts to enhance operational efficiency, product quality, and decision-making. The mapping of applications by country, sector, and industrial focus reveals growing trends toward I5.0 in areas such as logistics and infrastructure. Common application domains include monitoring, optimization, prediction, and decision support. Despite their potential, DT adoption faces challenges including high implementation costs, data integration issues, cybersecurity concerns, and lack of standardization. The review discusses these barriers alongside the importance of validation and security for trusted deployment. It concludes by identifying future directions, including cognitive twins, industrial metaverse integration, and ethical AI. DTs are positioned as foundational technologies for advancing sustainable, resilient, and human-centered industrial systems.