# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 4 Methodology
### Model Training  
To ensure robust and generalizable performance across diverse customer-vehicle records, we implemented a multi-model framework based on tree-based ensemble algorithms, including Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). These models were selected not only for their predictive strength on structured tabular data, but also for their capacity to accommodate the specific statistical characteristics observed in the dataset. These algorithms were specifically selected for the following reasons.  
* **Suitability for High-Dimensional and Heterogeneous Data** The dataset contains a mix of continuous (e.g., Days Between Service Visits) and categorical variables (e.g., Service Month, Charned Customer). Tree-based models inherently handle heterogeneous feature types without the need for extensive preprocessing or normalization, unlike models based on distance metrics (e.g., k-NN, SVM with RBF kernel).
* **Handling of Non-linear Interactions** These models are capable of capturing non-linear relationships through recursive binary splits. For example, Decision Trees optimize a cost function such as the Gini index or Entropy: \[\text{Gini}=1-\sum_{i=1}^{\text{C}}p_{i}^{2}\text{ or Entropy}=-\sum_{i=1}^{ \text{C}}p_{i}\text{log}(p_{i})\] (3) where \(p_{i}\) is the proportion of class \(i\) in a node. These criteria help models identify complex interaction patterns in the data, which are likely present given the diverse nature of service history and user behavior.
* **Robustness to Multicollinearity and Outliers** Random Forest and Gradient Boosting algorithms (LightGBM, XGBoost) reduce over-fitting and variance by aggregating multiple base learners. LightGBM and XGBoost further improve generalization through regularization terms in their objective functions: \[\text{Obj}(\theta)=\mathcal{L}(\theta)+\Omega(\theta),\ \ \ \Omega(\theta)= \gamma T+\frac{1}{2}\lambda\sum_{j=1}^{T}w_{j}^{2}\] (4)where \(T\) is the number of leaves, \(w_{j}\) represents leaf weights, and \(\lambda\) and \(\gamma\) are regularization parameters.
* **Performance on Imbalanced and Sparse Labels** Given the imbalance in part replacement labels (binary flags for each part changed during service), boosting algorithms like XGBoost and LightGBM are advantageous due to their ability to focus on harder-to-classify examples via gradient-based loss optimization: \[1(y_{i},\hat{y_{i}})=-(y_{i}\text{log}(\hat{y_{i}})+(1-y_{i})\text{log}(1-\hat{ y_{i}}))\] (5) where \(y_{i}\in\{0,1\}\) is the label, and \(\hat{y_{i}}\) is the predicted probability.
* **Computational Efficiency and Scalability** LightGBM uses histogram-based learning and leaf-wise tree growth with depth constraints, which improves speed and memory efficiency--important for production deployment and rapid retraining scenarios. A key rationale for favoring tree-based methods was the non-Gaussian and often skewed nature of several critical numerical features, such as Cumulative Total Net Amount (EUR) and Days Between Service Visits, which deviated significantly from normality (see Figure 16).  
In contrast to algorithms relying on distance metrics or feature scaling, ensemble tree models are inherently robust to such irregularities due to their use of threshold-based, greedy splits. Additionally, these models handle both high-cardinality categorical variables and missing values natively, reducing the need for aggressive preprocessing. Each of the four machine learning models was trained independently using historical data: For workforce transition modeling, historical employee records were used to learn the likelihood of job role changes or reskilling needs. For customer maintenance modeling, service visit histories were used to predict the probability of future visits to authorized maintenance centers.