# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 1 Introduction  
After-sales service for high-value equipment is a key element of competitive strategy, with manufacturers offering warranties that provide maintenance services from authorized centers. However, after the warranty expires, many asset owners shift to lower-cost, unauthorized service centers, impacting long-term revenue and customer loyalty. [PERSON] et al. 's research shows that while 84% of vehicles stay loyal to authorized centers within the first year of the warranty, this figure plummets to 29% in the second year, highlighting significant revenue loss and market share erosion for manufacturers. Customer retention is critical, as acquiring new customers is five times more costly than retaining existing ones. Despite substantial investments in CRM programs across industries, many organizations still use simple, rule-based segmentation methods, which fail to capture the complexity of customer behavior, leading to inefficiencies and inaccurate predictions. In the automotive after-sales domain, customer retention presents a particularly underexplored challenge. While substantial research has been devoted to general customer retention, the specific intersection of vehicle usage patterns and customer behavior within the after-sales service sector remains relatively untapped. This paper aims to address this gap by leveraging advanced machine learning techniques to predict future service visits with high accuracy, enhancing decision-making in the management of after-sales services. Unlike existing studies that primarily focus on fault detection or generalized customer churn, our approach integrates both maintenance demand forecasting and customer behavior analysis in a way that accounts for the complex interplay of these factors. To this end, we propose a novel machine learning framework tailored for the prediction of vehicle visits to authorized service centers within a predefined period. The model employs several tree-based algorithms--Decision Trees, Random Forests, Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XGBoost)--to capture the intricate relationships between customer behaviors and maintenance needs. These algorithms were specifically selected for their ability to handle complex datasets and to provide statistically interpretable results. Among these models, XGBoost demonstrated the best performance, showcasing its potential in solving real-world business problems. By implementing this machine learning framework, we have achieved several key operational improvements. Information delivery times during service operations have been reduced by 20%, and survey completion times have decreased from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. Moreover, timely vehicle maintenance and service appointment scheduling have contributed to reducing potential accidents. The transition from a rule-based maintenance prediction system to machine learning has also led to better resource utilization and increased prediction accuracy. As a result, individual customer service visit rates have increased by 30%, while corporate customer visits have risen by 37%, highlighting the significant impact of this approach on both customer retention and service efficiency. This framework has the potential to be widely adopted across the automotive industry as a scalable and effective solution for optimizing after-sales service operations. Its machine learning-driven approach can be easily customized and extended to accommodate diverse vehicle brands, models, and customer segments, making it applicable to various contexts within the automotive sector. This study is motivated by the challenges faced by the after-sales services (ASS) department of a distributor for a German automotive brand in Turkey, which currently utilizesa costly and inefficient rule-based system to predict customer visits. The existing system relies on historical visit data and attempts to predict future service visits by contacting customers who are most likely to visit within a year.