## 4 Discussion  
The implementation of an AI-supported digital twin in a small-batch manufacturing environment demonstrated how advanced data analytics and simulation can enhance production understanding without requiring a full-scale digital infrastructure overhaul. While digital twins have been widely studied in high-volume manufacturing and cyber-physical systems, their application in SMEs remains limited. This study contributes to that gap by showing how modular and lightweight solutions, combined with targeted data collection and stakeholder collaboration, can unlock valuable insights without disrupting daily operations. One of the key findings was the importance of timing coordination in short-run production. Unlike traditional mass production, where variability is minimized through volume and standardization, the small-batch model relies on flexibility and human input, making process synchronization more challenging. The use of \"micro-batches\" as a practical structuring mechanism proved effective, but also exposed the fragility of the system when task sequencing or material preparation was delayed. Clustering revealed recurring inefficiencies that would have been difficult to identify through manual observation or standard key performance indicators (KPIs) alone. In particular, the combined use of DBSCAN and K-means clustering allowed the team to filter out noise, detect state-specific patterns, and highlight the variability in workstation performance. These insights were used to guide process discussions and test improvements virtually, reducing the need for costly trial-and-error adjustments on the production floor. Simulation results aligned with observed bottlenecks, reinforcing the validity of the analytical approach and offering a realistic preview of how even minor adjustments, such as staggered handovers or buffer size changes, could increase throughput. From a broader perspective, the results align with the goals of Industry 5.0, where human-centered decision-making and interpretability are emphasized over full automation. The data visualization features embedded in the DIMUSA platform enabled planners and operators to understand what was happening in the system and why, thereby facilitating more confident and collaborative responses to identified issues. The study also supports the relevance of the \"test before invest\" approach in SME settings, where experimentation capacity is limited and disruption must be minimized. By leveraging a combination of real-time monitoring and virtual validation, the team bridged the gap between abstract digital strategies and grounded operational improvements. Moreover, while the case focused on a sportswear manufacturer, the same digital twin methodology could be generalized to other domains characterized by small-batch variability, manual processes, and frequent order customization, such as artisanal production, medical device assembly, and high-mix electronics. It is also important to emphasize that this study primarily focused on assessing the conceptual applicability of the framework. While the simulation-based analysis and digital shadow system provided important insights, it is essential to note that this study mainly focused on assessing the conceptual applicability of the framework. The goal was not full operational implementation, but rather identifying critical production inefficiencies that could guide future deployment. Therefore, real-time KPIs and data visualizations were utilized to support collaborative analysis with stakeholders; however, long-term effects, such as ROI, capacity utilization, or sustained performance improvements, will require further integration and ongoing tracking. This approach aligns with the \"test before invest\" philosophy promoted in SME innovation environments, where experimental validation is a necessary first step toward more reliable implementation. While simulation alone can offer valuable insights into process flows and bottlenecks, its effectiveness relies heavily on predefined assumptions and manual scenario testing.