Prioritizing Human Digital Twin Applications to Improve Workforce Performance: A Multi-Criteria Decision-Making Approach

This study aims to rank the applications of Human Digital Twin in improving workforce performance using crisp multi-criteria decision-making methods. Human Digital Twin is an emerging concept in Industry ۵.۰ that creates a digital representation of workers' physical, cognitive, behavioral, and performance-related characteristics. Although this technology has various applications in workforce management, occupational safety, productivity improvement, training, and human-machine collaboration, organizations often face uncertainty in determining which applications should be prioritized. Therefore, this study provides a structured decision-making framework for identifying and ranking the most important applications of Human Digital Twin in the context of Iranian organizations. The research is applied in terms of purpose and descriptive-survey in terms of data collection. The statistical population consisted of academic and industrial experts in digital transformation, smart manufacturing, human resource management, occupational safety, production management, and industrial engineering. Using purposive sampling, ۲۱ experts were selected as the final expert panel. The Best-Worst Method was used to determine the weights of the evaluation criteria, and the TOPSIS method was applied to rank the alternatives. The results showed that productivity and efficiency improvement ranked first among the applications of Human Digital Twin. Error reduction and quality improvement, occupational safety and risk prevention, human-machine collaboration improvement, and predictive workforce performance analysis were ranked next. The findings indicate that Iranian organizations should initially focus on applications that provide direct, measurable, and operational benefits. This study contributes to the literature by offering a structured prioritization of Human Digital Twin applications and provides practical guidance for organizations seeking to adopt this technology for workforce performance improvement.

کلیدواژه ها:

Human Digital Twin ، Workforce Performance ، Industry ۵.۰ ، Multi-Criteria Decision-Making ، Best-Worst Method ، TOPSIS ، Digital Transformation