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The review investigates the adoption of digital twins and how the technology affects sustainable manufacturing performance in developing countries. In particular, it analyses the scope and application of digital twins, impact of the technology on manufacturing performance, including resource efficiency, efficiency of energy usage, efficiency of production, efficiency of material usage, and environmental sustainability, and highlights the key factors that drive the adoption of digital twins, inhibit it, and pose implementation challenges. A structured literature review was carried out based on journal articles, conference papers, book chapters, reviews, simulation, frameworks, and empirical studies on digital twins published between 2019 and 2026. According to the literature, the application of digital twins has evolved from virtual representation and workshop modeling to real-time monitoring, prediction, self-maintenance, artificial intelligence-based management, adaptive production planning and scheduling, coordination within the supply chain, circular economy applications, and resilience. Digital twins help to improve manufacturing decision-making through enhancing process visibility, conducting tests under different scenarios, failure prediction, ensuring quality, and optimization of energy and materials usage. However, evidence of environmental and economic benefits of digital twins is still scarce since many studies rely on simulation, prototype implementation, laboratory experiments, and individual cases instead of enterprise wide implementations throughout several years. Implementation barriers include high investment cost, uncertainty about the payback period, weak infrastructure, lack of necessary skills, low data quality, cybersecurity concerns, inability of the legacy systems to work with the new system, change aversion, and poor interoperability. Barriers are especially significant for small and medium-sized companies operating in developing countries. The results of the research show that implementation of digital twins can help to achieve sustainable manufacturing performance if it is facilitated by reliable infrastructure, necessary skills, organizational readiness, adequate governance, and sustainability metrics.
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Dr. Cassius A. Ogar (2026) studied this question.
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