## 3 Methodology  
This section focuses on the selection and integration of digital enablers into a warranty management framework based on [PERSON]'s eight-phase model [37; 38] (Figure 1), adapted by [1] (Figure 2) for warranty management. This methodological framework, initially designed for asset management, is redefined to respond to the complexity and digitalization demands of the present day in the industrial field, where efficient aftersales service is essential for customer satisfaction and operational sustainability [41]. The adaptation of this model allows a transition from a reactive approach, aimed at resolving failures after they occur, to a proactive and predictive model, which allows anticipating possible problems and optimizing maintenance resources. Figure 1: Input–process–output diagram of the DMM Framework. (Source: [42]). Figure 2: Aftersales warranty management framework model. (Source: [1]). Modern warranty management heavily relies on advanced machine learning algorithms and optimization models to enhance predictive capabilities, classify failures, and optimize cost-benefit trade-offs:  
* Artificial Neural Networks (ANNs) for Predictive Maintenance: ANNs are widely used to predict asset failures by analyzing historical performance data and identifying degradation patterns. These networks simulate the functioning of human neural structures to detect complex correlations in large datasets. In warranty management, ANNs enable early fault detection and maintenance scheduling, reducing unexpected failures and optimizing warranty claims [43; 44]. * Support Vector Machines (SVM) for Failure Classification: SVM is a supervised learning algorithm that classifies data points by identifying hyperplanes that best separate failure modes from normal operational conditions. In warranty systems, SVM models are employed to categorize defects based on sensor data, maintenance logs, and operational parameters. This classification enhances root cause analysis, allowing manufacturers to refine warranty policies based on recurring failure types and severity [45]. * Bayesian Networks for Probabilistic Risk Assessment: Bayesian networks model dependencies between multiple variables to estimate the probability of failure under different operating conditions. This probabilistic approach is particularly useful in risk-based warranty decision-making, allowing companies to dynamically adjust their coverage and optimize maintenance intervals based on evolving asset conditions and environmental factors [46; 47]. * Optimization Models for Cost-Benefit Trade-Offs in Warranty Policies: Various optimization models, such as stochastic programming, genetic algorithms, and mixed-integer linear programming (MILP), are utilized to determine the most cost-effective balance between warranty costs and service performance. These models assess variables such as failure rates, repair costs, downtime penalties, and warranty extension probabilities to establish optimal service contract structures that maximize customer satisfaction while minimizing operational expenses. For instance, MILP models help manufacturers dynamically adjust warranty coverage based on real-time asset performance, ensuring financial sustainability while maintaining reliability commitments [48; 49]. These digital enablers offer real-time analysis and prediction capabilities that optimize maintenance, reduce operating costs, and maximize asset availability, generating benefits for both the organization and customers [1]. This proactive approach to warranty management directly responds to the findings of [1], who argue that implementing digital technologies in asset life-cycle management not only optimizes operational efficiency, but also improves customer experience by offering agile aftersales service that is necessities-oriented. The incorporation of digital twins is another essential enabler within this framework.