# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 3 Preliminaries
### Data Analysis
#### 3.3.1 Descriptive Statistics  
The dataset used in this study was obtained from the CRM and after-sales service records of a leading automotive company, spanning a timeframe from January 2012 to December 2021. It contains monthly snapshots, representing customer-service interaction records over a span of 120 months. It consists of 108,596 visited service records, involving 3872 unique customers and 13,201 vehicles. While the company database contains a significantly larger volume of customer and vehicle records, this subset was selected as a pilot dataset to validate the modeling approach and conduct controlled experimentation. Importantly, the entire dataset was used in the exploratory data analysis (EDA) and feature engineering stages to capture overall behavioral patterns and ensure the robustness of derived features. However, only the pilot subset was utilized in the model training and testing phases to maintain consistency and interpretability, as well as to enable efficient experimentation under a controlled scope. Although a single customer may own multiple vehicles and vehicle ownership can change over time, our modeling strategy is structured around the last known owner of each  
Figure 2: The workflow for predicting vehicle maintenance needs. vehicle. This decision was made to avoid behavioral inconsistencies, as the maintenance patterns and service usage habits may differ significantly between previous and current owners. While historical records--including data from previous owners--are utilized during model training to enrich the feature space and learn general patterns, the test set is exclusively constructed using actions and interactions associated with the final owner of each vehicle. This approach ensures that model predictions are contextually relevant and aligned with the actual decision-making behavior of the most recent customer, which is critical for operational deployment. The primary aim was to predict whether a customer would visit the service center in the subsequent month, modeled as a binary classification problem (VISITED = 1 or 0). As illustrated in Figure 3, the dataset exhibits a highly imbalanced distribution of the target variable VISITED, which indicates whether a customer has visited the service center in a given period. The initial dataset consisted of 60 features, which are detailed in Table A1 in Appendix A along with their definitions and data sources. Among the 60 features, 7 are identifier/date (see Table 1). During the preprocessing phase, identifiers (e.g., Customer ID, Chassis ID) and date-type variables (e.g., Service Visit Date, Guarantee End Date) were excluded from modeling, as they are not directly informative for prediction or are used for partitioning purposes. Among the 60 features, 11 are categorical variables (see Table 2). Categorical variables in the dataset (such as gender, Customer Type, Plate Province, Brand Code, etc.) were \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute** & **Data Source** & **Inferred Type** \\ \hline Customer ID & Customer Data & Identifier/Date \\ Chassis ID & Vehicle Data & Identifier/Date \\ Vehicle ID & Vehicle Data & Identifier/Date \\ Work Order ID & Maintenance Data & Identifier/Date \\ Service Visit Date & Maintenance Data & Identifier/Date \\ Previous Service Visit Date & Derived Data & Identifier/Date \\ Guarantee End Date & Vehicle Data & Identifier/Date \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Type of identifier/date features. Figure 3: Distribution of the target variable (visited vs. not visited). transformed into numerical representations to enable their use in machine learning models. The transformation is described in Section 4.1.5. Some categorical variables were eliminated on a model basis, as explained in Section 4.2.8, by evaluating the significance they added to the model. Among the 60 features, 41 are numeric variables (see Table 3). A feature selection process was applied on a per-model basis among some numerical features.