# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 2 Related Works
### Customer Retention Studies
In [8], ANN and DT are applied to identify lost customers in the automotive supplier industry, with results indicating that while both algorithms are efficient, Decision Trees are complicated by the need for buck tuning in loss detection. The above studies collectively demonstrate that hybrid and ensemble methods often outperform standalone models, particularly when dealing with non-linear and high-noise datasets. Ref. [9] provides an empirical analysis of the impact of hyperparameters on churn prediction using Deep Neural Networks (DNNs) in the banking industry. The DNN model, with a rectifier function in hidden layers and a sigmoid function in the output layer, outperforms the Multilayer Perceptron (MLP) model in predicting customer loss. Ref. [10] proposes a framework for detecting churn in the telecom industry using classification and clustering methods, categorizing Churned Customers using the Divergence Kernel-based SVM (DKSVM) algorithm. Additionally, the Hybrid Core Distance-Based Probabilistic Fuzzy Local Information C-Means (HKD-PFLICM) method segments customers into Low, Medium, and Risky categories based on customer activities and profiles. Ref. [11] analyzes the performance of several widely used classification algorithms, including DT, Random Forest (RF), LR, Stochastic Gradient Descent (SGD), SVM, MLP, k-Nearest Neighbors (KNN), Gradient Boosting Model (GBM), and Naive Bayes (NB), on the customer churn problem in the telecommunications industry using a public dataset. These comparative analyses underscore the importance of algorithmic benchmarking and feature engineering in model selection processes for churn analytics. Ref. [12] discusses how AI enables dealerships to personalize customer interactions, predict service needs, and tailor marketing strategies, thereby improving customer satisfaction and retention rates. Ref. [13] emphasizes the impact of AI on customer retention strategies, highlighting that businesses leveraging AI for personalized experiences and proactive support witness significant improvements in customer loyalty. The study notes that AI facilitates better understanding of customer behaviors and preferences, allowing for more effective engagement strategies. Ref. [14] explores the broader transformation of the automotive industry towards customer-centric models, driven by AI integration. The article illustrates how AI enhances various aspects of the customer journey, from personalized vehicle recommendations to proactive maintenance alerts, contributing to a more engaging and satisfying customer experience. Ref. [15] presents an AI-enhanced framework aimed at improving predictive failure analytics in automotive safety, specifically targeting school buses and commercial trucks. The study emphasizes the integration of machine learning techniques, including deep learning neural networks and 3D visualization, to analyze diverse datasets such as traffic collision data and shapefiles. By addressing the dual challenges of anomaly detection and cyber threat response, the proposed framework seeks to enhance the integrity and functionality of transportation and logistics systems. While these studies provide a broad spectrum of AI applications in customer management and safety analytics, they primarily focus on churn prediction or customer personalization--distinct from the objective of the current study. Although these studies address various aspects of customer churn and propose different methodologies, they do not offer a solution relevant to the current work. The focus here is on predicting the need for service visits by vehicles during the warranty period, considering vehicle usage characteristics. A key aspect is that frequent vehicle usage implies more frequent service visits, potentially occurring within a year. The proposed model aims to estimate which customers require service based on past usage patterns and previous maintenance behaviors.