## 5 Conclusions and Future Work  
This study explored the implementation of an AI-enhanced digital twin framework in a real-world small-batch manufacturing environment. The approach combined real-time data acquisition, clustering-based analysis, and simulation modeling to support human-centered decision-making and improve production transparency. The integration of the DIMUSA enabled the automated collection, processing, and visualization of workstation-level performance data, bridging the gap between manual practices and intelligent monitoring. By applying clustering algorithms such as K-means and DBSCAN, the system successfully identified operational states and process anomalies that traditional KPI reporting would have overlooked. These insights helped isolate inefficiencies related to cycle variation, workstation synchronization, and operator-driven fluctuations. In parallel, the use of virtual factory simulation provided a low-risk environment for validating hypotheses and exploring improvements, allowing the company to test and refine operational strategies before applying them in live production. The developed framework emphasized modularity, interpretability, and scalability factors for successful deployment in SMEs with limited digital infrastructure and technical capacity. Beyond technical performance, the solution supported collaborative learning and team engagement by offering accessible visualizations and structured feedback mechanisms. These characteristics resonate strongly with the principles of Industry 5.0, where human involvement, adaptability, and sustainable improvement are prioritized. Future development will focus on expanding system coverage across additional production areas, integrating predictive components, and refining clustering logic through the application of supervised learning techniques. Integration with enterprise systems, such as ERP and Manufacturing Execution System MES platforms, is also planned to ensure seamless data continuity and richer contextual awareness. Longer-term studies could investigate how such systems affect organizational learning, routine adaptation, and continuous improvement within SME environments. The findings of this case study reinforce the conclusion that digital twin technologies, adapted to real-world constraints and deployed incrementally, can offer measurable value even in resource-constrained industrial settings. The key is aligning technology with operational realities and empowering human decision-makers through interpretable, actionable data.  
Conceptualization, T.R.; methodology, T.R. and J.M.; software, J.M.; validation, T.R. and A.H.; formal analysis, T.R. and J.M.; investigation, T.R.; resources, T.R.; data curation, T.R.; writing-original draft preparation, T.R.; writing-review and editing, K.K. and J.M.; visualization, T.R.; supervision, K.K.; project administration, T.R.; funding acquisition, T.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by AI and Robotics Estonia--EDIH, project number 101083677; Master of Science in Smart, Secure and Interconnected Systems (MERIT)--Development of a new pan European educational ecosystem for training of digital specialists (Co-funded by European Union under grant agreement No. 101083531); the Estonian Education and Youth Board project \"Development and manufacturing of complex products\" No. OUF10 co-funded by the European Union; The project \"Increasing the knowledge intensity of Ida-Viru entrepreneurship\" is co-funded by the European Union.  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.