# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 1 Introduction
However, this approach is problematic as customer behaviors can vary significantly due to factors such as economic conditions, social trends, and external events like the COVID-19 pandemic. Moreover, the previous system was entirely dependent on human effort, where identifying potential customer-vehicle visits required rule-based filtering and weeks of manual analysis. This labor-intensive process not only led to inefficiencies but also limited scalability and adaptability. By integrating AI-driven methodologies, particularly through AI4 LABOUR (reshaping labor force participation with artificial intelligence), we have transformed this process into an automated, data-driven system, significantly reducing the time and effort required while improving prediction accuracy and operational efficiency. This study aligns with AI-driven workforce optimization efforts, such as those explored in the AI4 LABOUR project, to enhance efficiency in after-sales service management. Machine learning and artificial intelligence (AI) offer promising solutions to these challenges by enabling more precise demand forecasts, optimized customer segmentation, and improved resource management. By incorporating advanced AI techniques, businesses can reduce costs and enhance the efficiency of their after-sales operations, leading to higher customer retention rates and better overall service outcomes. The dataset utilized in this study spans over a decade of maintenance records for a diverse set of vehicles, providing a rich foundation for training machine learning models. This comprehensive dataset is carefully preprocessed to derive meaningful features such as service interval patterns, vehicle age, and service fee increases, ensuring that the resulting models are both robust and accurate in predicting future service visits. The key contributions of this paper are as follows:  
1. Novel Use Case: We present a unique approach to predictive maintenance by focusing on forecasting service visits within the automotive after-sales domain. This framework provides valuable insights for future studies in the area of maintenance demand forecasting. 2. Enriched Feature Set: We develop and analyze a set of features that significantly improve the predictive accuracy of our models, including patterns in service intervals, vehicle age dynamics, and customer segmentation based on service behaviors. 3. Machine Learning Comparison: Through a comparative evaluation of four different tree-based algorithms, we identify XGBost as the most effective model for this particular application, outperforming other methods in terms of predictive accuracy and scalability. 4. Practical Implications for CRM and Workforce Planning: The findings of this study offer valuable, actionable insights for CRM teams by enabling the optimization of customer outreach strategies, minimizing resource inefficiencies, and enhancing customer engagement through personalized communication channels such as text messages, emails, and phone calls. Moreover, beyond its immediate application in predictive maintenance, the machine learning approach adopted in this research aligns with the broader objectives of the AI4 LABOUR project, particularly in the context of workforce analytics and planning. By leveraging AI to anticipate service demand fluctuations, the underlying methodology demonstrates strong potential for adaptation to labor market forecasting--empowering organizations and policymakers to identify emerging skill gaps, respond proactively to workforce transitions, and support more efficient workforce participation strategies. The remainder of the paper is organized as follows: Section 2 provides a comprehensive review of related works. Section 3 details the dataset used, including data collection methods and exploratory analysis. Section 4 outlines the methodology employed in this study, including performance evaluation metrics. Section 5 presents the experimental results and analysis.