# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 7 Conclusions  
The observed efficiency gains in predictive maintenance echo broader labor market transformations driven by AI. AI4 LABOUR's research into AI-induced job shifts suggests that automation-driven improvements, like those demonstrated in this study, will necessitate workforce reskilling and adaptive labor policies. The ability to predict service demands through AI-driven analytics mirrors the capability of AI4 LABOUR's skill-based modeling to forecast labor market trends. Figure 28: Top 20 features ranked by mean absolute SHAP values for the XGBoost model. This study presents a statistical framework for predicting vehicles most likely to visit authorized service centers by leveraging machine learning techniques, specifically XGBoost. By replacing the traditional rule-based filtration system with a robust data-driven approach, the proposed method demonstrates significant improvements in accuracy and efficiency. A comprehensive benchmarking analysis was conducted, comparing the performance of four algorithms using standard evaluation metrics, including recall, precision, F1-score, and Area Under the Curve (AUC). Among the methods evaluated, XGBoost consistently outperformed others, achieving the highest F1-score. This success can be attributed to two primary factors: the utilization of an enriched dataset during training and the XGBoost algorithm's ability to minimize a regularized objective function that balances model complexity and loss minimization. Additionally, grid search parameter optimization further enhanced the model's performance by fine-tuning its hyperparameters. A comparison between the proposed method and the previously used rule-based system revealed a 42% improvement in prediction accuracy. This substantial increase underscores the value of adopting machine learning techniques to enhance operational processes in after-sales services. Moreover, the proposed method demonstrated consistent accuracy across forecasts conducted for different months, indicating its robustness and reliability in real-world applications. Beyond its methodological contributions, this study has delivered tangible real-world benefits. The implementation of this framework has reduced information delivery times during service operations by 20%, significantly enhancing customer experience. Survey completion times have been optimized, decreasing from an average of 5 min to 4 min per survey. As a result, approximately 5906 h were saved across 354,409 surveys conducted by May 2024. Additionally, timely maintenance facilitated by the proposed system has minimized potential accident risks, contributing to overall vehicle safety. The framework has also significantly impacted customer engagement metrics. Individual customer service visit rates increased by 30%, while corporate customer visit rates rose by 37%. These improvements demonstrate the framework's effectiveness in fostering customer loyalty and driving service center utilization. Furthermore, the transition from rule-based predictions to machine learning has enabled more efficient resource allocation and more accurate maintenance demand forecasting. By integrating AI-driven predictive maintenance models with workforce planning strategies, this study contributes to optimizing service personnel allocation, reducing idle time, and improving overall operational efficiency, in line with findings from the AI4 LABOUR project. The findings of this research reinforce AI4 LABOUR's central mission of forecasting AI's impact on labor force participation and skill development. By demonstrating how predictive maintenance transforms workforce structures in the automotive sector, this study offers a scalable model for analyzing AI-driven disruptions in other industries. The identified sectoral multiplier effects--from increased service efficiency to shifts in job demands--serve as critical input for AI4 LABOUR's recommendation portal, guiding policymakers, companies, and educational institutions in designing adaptive labor strategies.