## 4 Conclusions  
This study aimed to establish a complete and reproducible methodology for leveraging operational data in scenarios characterized by data scarcity, as commonly encountered in urban bus fleets undergoing the early stages of maintenance digitalization. Building upon a previously proposed framework, the methodology was adapted to include specific steps, tools, and practical considerations to guide maintenance teams in initiating data-driven analysis even with limited resources. A key enhancement introduced was the incorporation of a scheduled batch retraining strategy, helping maintain predictive performance in the face of evolving conditions. To demonstrate its applicability, a real-world case study was conducted focusing on the prediction of the duration of soot load levels in the Diesel Particulate Filter (DPF), specifically Zones 3 and 4, which precede conditions that can significantly impact bus operability. Once the training and deployment simulations were completed, two distinct performance scenarios emerged. In Zone 4, despite data limitations, the deployed model continued to outperform the updated baseline (MAE: 0.6758 vs. 0.9612, RMSE: 0.9330 vs. 1.0722, R\({}^{2}\): 0.24), demonstrating a certain level of robustness. This suggests that while model updates could enhance performance, the current model remains sufficiently accurate for operational use until the next validation cycle. However, in Zone 3, a substantial drop in evaluation metrics was observed during the new test batch (R\({}^{2}\approx-0.10\), MAE \(\approx\) 1.01 h, RMSE \(\approx\) 1.16 h), indicating that the previously deployed model was no longer capable of capturing the underlying dynamics. This highlights the need for a model revision process, including an updated exploratory analysis and retraining with the most recent data. The overall results confirm the potential and practical utility of the proposed methodology. While predictive accuracy varied across zones, particularly with a notable drop observed in Zone 3, the framework proved effective in identifying performance degradation, guiding necessary revisions, and supporting decision-making under data-scarce and evolving operational conditions. This adaptability highlights the methodology's practical value for fleet managers operating in the early stages of digital transformation. Several limitations emerged during the application of the proposed approach. One key challenge is the potential for operational changes in the bus fleet, such as new routes, different traffic conditions, or driving behaviors, which can introduce data drift--i.e., shifts in the statistical distribution of the input data that degrade model performance over time. Additionally, the emergence of new outliers highlights the need for ongoing monitoring and data validation. Another limitation is the lack of data in specific ranges; for example, in Zone 4, the model tended to overestimate when the real duration was below 2.5 h, indicating reduced accuracy for short-duration events. These findings emphasize the importance of continuous evaluation and model adaptation. Looking ahead, the next steps for the case study focus on operational deployment. First, the predictive models will be implemented using the existing API connection to access the necessary data streams in real time. Additionally, since the data analysis tools have already been defined and validated, the next logical automation step is to link the database to a dedicated processing module that can automatically generate the performance indicators and plots outlined in this study. This would significantly accelerate the review process for maintenance managers. Integrating a notification system or dashboard could further enhance usability, ensuring that key insights are delivered promptly to the right stakeholders. Moreover, as data availability grows, the pipeline could also be scaled to incorporate more complex models and additional variables, further improving predictive performance.