Digital Twins in Industrial Transformation: A PRISMA-Based Review with Human Resource Implications

Abstract

Abstract: The rapid advancement of Industry 4.0 technologies has transformed traditional industrial systems into intelligent, data-driven ecosystems. Among these, digital twins have emerged as key enablers of industrial transformation by integrating real-time data, simulation, and predictive analytics. This study presents a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to examine the role of digital twins in driving innovation, operational efficiency, and sustainability across industries. Using the Scopus database, an initial pool of 220 articles was identified, of which 15 studies were selected through a rigorous screening and eligibility process. The findings indicate that digital twins function as multidimensional strategic tools that enhance predictive maintenance, optimize resource utilization, and accelerate product development through virtual prototyping. In addition, digital twins contribute to sustainability by supporting energy efficiency, circular economy practices, and environmental, social, and governance (ESG) objectives. These capabilities highlight their role in enabling organizations to transition from reactive to proactive and data-driven decision-making systems in complex industrial environments globally and across diverse sectors. Importantly, the review highlights the organizational and human implications of digital twin adoption. The integration of digital twins creates hybrid decision-making environments where human judgment is augmented by algorithm-driven insights, influencing accountability, trust, and workforce roles. This transformation necessitates the development of new competencies, including data literacy, digital collaboration, and adaptive decision-making, while emphasizing the role of human resource management in facilitating reskilling, change management, and the development of data-driven organizational cultures. The study also identifies key challenges, including high implementation costs, interoperability issues, data security concerns, and resistance to change. By synthesizing technological, strategic, and human perspectives, this research positions digital twins as drivers of both operational and organizational transformation. The findings provide valuable insights for practitioners and policymakers seeking to leverage digital twin technologies for sustainable and competitive industrial development.