## 3 Methodology
### Implementation and Development of the Presented Methodology  
The implementation of this methodology involves the deployment of digital enablers corresponding to each phase. These enablers are specifically selected to align warranty management practices with the organization's strategic objectives. The project will require a collaborative approach between IT, operations, and customer service departments, as well as a robust infrastructure that allows the integration of the aforementioned technologies. Depending on the specific objectives of each phase, AI technologies are organized into various categories, ranging from machine learning models to continuous improvement tools. Choosing these categories represents an essential step for improving system functionality and efficiency specifically within warranty management. Every category of the model provides unique operational and management capabilities that optimize different model stages. 1. Machine learning models that contain artificial neural networks (ANNs), SVMs, and Decision Trees help systems extract information from past data and current data for classification and prediction tasks. 2. Data Mining and Association Rules, together with other data analysis techniques, provide capabilities to identify concealed patterns within extensive datasets, thus aiding root failure cause identification and warranty policy optimization. 3. The visualization tools alongside analytical tools assist organizations in tracking KPIs to support better decision-making that avoids strategic objective misalignment. 4. NLP (natural language processing) analyses customer feedback to help businesses enhance satisfaction levels, as well as enhance process operations. 5. The categories within this section ensure systems run efficiently to maintain the quality standards needed for optimal performance. 6. System quality and performance standards need maintenance to deliver high satisfaction levels to customers. 7. The life-cycle management approach optimizes performance alongside cost reduction for all product stages, especially in the Warranty Policy Risk-Cost-Benefit Analysis phase. 8. The system receives constant upgrades through collaborative learning, which keeps it active and oriented toward customer needs. 9. Utilizing optimization models allows companies to enhance asset performance alongside warranty policy execution, through which they lower their costs while maximizing their efficiency. 10. This category involves ongoing process adjustment according to altering circumstances, which enables businesses to preserve their market competitiveness in changing conditions. Table 2 proposes a distribution of each technology in the phases of the warranty management model. Each category of digital enablers has been selected based on its ability to meet the specific operational and management needs of each phase of the model, thereby optimizing the functionality of warranty management. The objectives of each phase and the technologies selected to achieve these objectives are detailed below. In Phase 1, focused on the Balanced Scorecard (BSC), the objective is to monitor and improve the performance of warranty management KPIs, aligning them with the organization's strategic objectives. Enablers such as machine learning models, data analysis techniques, predictive and analytical tools, natural language processing (NLP), and monitoring and control are used. Tools such as Power BI and Tableau facilitate the visualization of KPIs, and predictive models anticipate performance variations. In addition, NLP allows the analysis of customer feedback, which helps improve satisfaction and adjust processes. Phase 2, called Criticality Analysis, aims to identify critical assets by analyzing their operational relevance and impact on the system. Machine learning models and data analysis techniques are used to do this.