# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 2 Related Works
### Predictive Maintenance Studies
Their survey categorizes current methods of explainable AI (XAI) applied to PdM, discussing the necessity for transparent and interpretable models to build trust among stakeholders and ensure compliance with industry standards. Ref. [27] introduces an explainable artificial intelligence (XAI) framework tailored for imaging-based predictive maintenance, with a specific focus on automotive applications. The framework employs a novel light transmission image processing methodology, utilizing statistical distance metrics such as the Wasserstein distance and Kolmogorov-Smirnov statistic for discriminative classification of unstructured images. To estimate component wear levels, particularly for air filters, the study integrates Bayesian inference and regression techniques. A notable feature of this framework is the incorporation of neural network-based models that maintain a high level of explainability. This is achieved through the generation of a statistical distance pseudometric via a feedforward neural network, a spatial block bootstrapping approach for synthetic training data generation, and the application of the discriminant classifier as a predictor in the Bayesian inference model. The authors highlight the framework's adaptability, suggesting its potential extension to other applications involving synthetically generated unstructured and structured images in predictive maintenance and health monitoring contexts. These contributions mark a shift in PdM research from merely predictive performance to model transparency, enabling actionable insights in industrial contexts. In their comprehensive review, ref. [28] explores the multifaceted applications of artificial intelligence (AI) in the automotive industry, extending beyond traditional predictive maintenance. The study synthesizes pertinent research encompassing AI's impact on vehicle emissions, security systems, and the realm of connected vehicles. By examining these diverse applications, the authors highlight the transformative potential of AI in enhancing vehicle performance, safety, and environmental sustainability. The review underscoresthe importance of integrating AI technologies to address contemporary challenges in the automotive sector and to pave the way for innovative solutions in the future. While prior studies have provided significant advancements in predictive maintenance, particularly in component-level diagnostics, fault detection, and RUL estimation, there remains a gap in addressing the convergence of customer behavior and service demand forecasting within the automotive after-sales context. Most existing models are either applied in different industrial settings (e.g., wind turbines, manufacturing) or focus on isolated technical failures rather than service visit patterns shaped by real-world customer behaviors. Moreover, although several works utilize ensemble methods such as Random Forests or Gradient Boosting Machines, few incorporate vehicle usage patterns, warranty periods, or customer-specific temporal behaviors into predictive frameworks. In contrast, our proposed framework bridges this gap by integrating customer retention dynamics with predictive maintenance forecasting. Leveraging a decade-long vehicle maintenance dataset, our model combines domain-specific features (e.g., service interval patterns, cumulative service cost) with interpretable tree-based machine learning algorithms (XGBoost, LightGBM), enabling accurate and scalable service visit prediction tailored to the after-sales environment. This integration provides a novel contribution to the PdM literature by addressing both operational and behavioral dimensions of maintenance forecasting--an aspect largely overlooked in existing comparative analyses.