# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 2 Related Works
### Automotive Industry Transformation with New Technological Developments in Recent Times  
As the automotive industry undergoes a rapid digital transformation, recent studies have increasingly focused on the integration of advanced AI and machine learning techniques not only for predictive maintenance but also for redefining the future of manufacturing and vehicle intelligence. Ref. [29] presents a comprehensive analysis of how artificial intelligence (AI), machine learning (ML), and generative AI are revolutionizing automotive manufacturing. The study highlights the transformative impact of these technologies on various facets of the industry, including design innovation, production efficiency, and operational optimization. Specifically, AI and ML are employed to enhance predictive maintenance, improve quality control, and streamline supply chain management through data-driven insights. Generative AI is noted for its role in pushing the boundaries of vehicle design, enabling the creation of novel and efficient structures that were previously inconceivable. Furthermore, the integration of smart technologies such as the Internet of Things (IoT) facilitates real-time monitoring and predictive analytics, bolstering operational efficiency and responsiveness to market demands. The authors also address the ethical considerations and challenges associated with these advancements, including data privacy, cybersecurity, and potential workforce displacement. Through a forward-looking perspective, the paper underscores the necessity for the automotive industry to adapt to these technological shifts to remain competitive and meet evolving consumer expectations. Building on this broader perspective of technological transformation in the automotive sector, other studies have specifically focused on the integration of machine learning in after-sales services. Ref. [30] conducts a comprehensive systematic literature review on the implementation of machine learning-based data strategies within the scope of automotive after-sales services. Their review encompassed 23 peer-reviewed studies published over the last decade, aiming to classify the technological, methodological, and managerial trends influencing the deployment of ML techniques in the automotive domain. The study found that predictive maintenance (PdM), customer behavior modeling, and automated diagnostics are the primary areas where ML is being actively applied. Notably, supervised learning algorithms (e.g., Random Forest, SVM, and Gradient Boosting) dominate the application landscape due to their interpretability and reliability in production environments. The authors also observed a shift toward the use of deep learning models (e.g., LSTMs and CNNs) for processing unstructured data, such as text from service reports and sensor signals. A major output of the review was a framework that links key operational goals (e.g., cost reduction, service time minimization, and personalized maintenance scheduling) with specific ML approaches and data types (e.g., telematics, historical maintenance logs, customer interaction data). The authors emphasized data quality, model interpretability, and integration with existing ERP/CRM systems as critical challenges. In parallel with technical applications, recent literature has also turned its focus to organizational readiness for AI adoption in automotive services. In [31], the authors present a systematic literature review that investigates the extent to which organizations in the automotive after-sales sector are prepared to implement artificial intelligence (AI) technologies. Drawing upon 78 scholarly sources spanning a decade (2014-2024), the review maps the landscape of AI readiness by synthesizing prevalent theoretical frameworks, key enablers, and sector-specific dynamics. Among the frameworks identified, the Technology-Organization-Environment (TOE) model emerges as the most frequently employed lens to assess readiness.