A New Framework Guides Factories to Adopt Human Digital Twins

In factories where manual labor dominates, a new approach is emerging to blend technology with human well-being. Human Digital Twins (HDTs)—digital replicas that mirror workers' physical and cognitive states in real-time—offer potential to enhance safety and efficiency, but their adoption in job-shop industries has been slow due to high costs and uncertainty about where to start. This study addresses that gap by providing a strategic prioritization framework, helping manufacturers in emerging economies navigate the early stages of HDT implementation with clarity and confidence. By focusing on applications like posture monitoring and fatigue prediction, the research aims to make digital transformation accessible and impactful for industries reliant on flexible, custom production.

The key finding from this research is that posture monitoring and fatigue prediction are the most influential and practicable HDT use-cases for job-shop industries. Using an integrated Fuzzy AHP-TOPSIS approach, the study evaluated five use-cases—posture monitoring, fatigue prediction, PPE compliance tracking, health-based task assignment, and skill training simulation—against criteria such as safety impact, technological maturity, implementation cost, data requirement complexity, and scalability. Analysis revealed that posture monitoring ranked first with a closeness coefficient of 0.639, followed by fatigue prediction at 0.628, indicating these applications deliver the highest value with the lowest implementation threshold, especially in semi-digital environments. This prioritization helps industries balance innovativeness and practicality, aligning with Industry 5.0 principles that emphasize human-centric technology integration.

Ology centered on a hybrid Multi-Criteria Decision Making (MCDM) model combining Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). First, the researchers identified evaluation criteria and use-cases through a systematic literature review, then validated them with a five-member expert panel including academics and industry practitioners with over ten years of experience. Fuzzy AHP was used to weight the criteria by converting linguistic judgments into Triangular Fuzzy Numbers, ensuring consistency with a Consistency Ratio below 0.1. TOPSIS then ranked the use-cases by calculating their distance from ideal solutions, as illustrated in Figure 1 of the paper, which outlines ological framework from identification to ranking. This approach effectively handles the qualitative and uncertain nature of expert decision-making, providing a robust tool for resource-constrained settings.

, Detailed in Tables 3-7 and Figures 2-4, show that technological maturity and safety impact are the dominant criteria, with weights of 0.352 and 0.343 respectively, accounting for nearly 70% of the prioritization. Implementation cost, data requirement complexity, and scalability had lower weights, indicating that job-shop industries prioritize reliable technology and worker safety over financial concerns initially. The TOPSIS analysis produced closeness coefficients that ranked the use-cases, with posture monitoring and fatigue prediction leading due to their high scores in safety and maturity, while skill training simulation ranked lowest at 0.379. Figure 3 visually represents these rankings, and Figure 4 proposes a strategic adoption roadmap: starting with posture monitoring in the short term, moving to fatigue prediction and PPE compliance in the medium term, and advancing to health-based task assignment and training in the long term. This stepwise strategy supports gradual integration and early wins.

Of this framework are significant for job-shop industries in emerging economies, where high task variability and manual labor pose s. By prioritizing safety-oriented and technologically mature applications, manufacturers can reduce workplace accidents, improve compliance, and enhance productivity without overwhelming their digital infrastructure. The study offers a decision-support system that guides managers in selecting HDT use-cases that align with organizational readiness and long-term goals, fostering a worker-centric digital evolution. This approach not only addresses barriers like poor digital maturity and lack of awareness but also supports resilient operations by enabling predictive maintenance and adaptive workforce management. Ultimately, it paves the way for more humane and efficient manufacturing environments.

However, the research has limitations, primarily its reliance on subjective expert judgments and lack of field validation. The criteria weights were derived from a panel of five experts, which, while consistent with a CR below 0.1, may not capture broader industrial perspectives. Additionally, the study acknowledges that simulation-based validation and pilot tests in real job-shop settings are needed to confirm the framework's effectiveness. Future work should include larger-scale empirical studies, sensitivity analysis on weight distributions, and exploration of interoperability s across diverse contexts. Despite these drawbacks, the framework provides a foundational pathway for industries to adopt HDTs in a safety-first manner, encouraging further research to strengthen generalizability and practical application.