## 3 Methodology
### Industry Best Practices for Digitalized Warranty Management
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Key Indicator** & **Description** & **Expected Impact** \\ \hline Reduction in downtime & Downtime avoided thanks to predictive maintenance. & Greater asset availability and operational continuity. \\ \hline Optimization of operational costs & Savings in maintenance costs through automation and failure prediction. & Cost reduction and increased profitability in asset management. \\ \hline Customer satisfaction & Aftersales satisfaction level measured through customer surveys. & Improved perception of warranty service and increased customer loyalty. \\ \hline Compliance with standards & Alignment with quality standards such as ISO 55000, 42001, and 9001. & Guaranteeing safety, quality, and efficiency throughout the asset life cycle. \\ \hline DRAMS & Reliability, availability, maintainability, and digital security. & Reducing downtime, optimizing operating costs, and complying with safety standards. \\ \hline \hline \end{tabular}  
* These indicators quantify the results of the integration of AI, IoT, and digital twins in each phase of the model, guiding decisions towards efficiency and customer satisfaction.  
\end{table} 
Table 4: KPI table. the high cost of digital twins limits their use to high-value assets, such as aircraft engines. General Electric implements the Warranty Policy Risk-Cost-Benefit Analysis phase, using digital twins to monitor gas turbines in real time and evaluate warranty policies through risk and cost simulations [27]. This has allowed the company to reduce maintenance costs by 25% and optimize asset performance [28]. However, digital twin models require constant investments to adapt to different assets and operating conditions. In the Reliability, Availability, Maintainability, and Safety (RAMS) phase, Siemens ensures the reliability and safety of its railway assets with its Railigent platform, which combines AI, computer-aided maintenance management (CMMS), and IoT sensors [26]. This platform facilitates predictive maintenance, which increases train availability and reduces unforeseen maintenance risks [26]. Training staff in interpreting the data generated is essential to maximize the effectiveness of this solution. Caterpillar, in the Life-Cycle Cost Analysis phase, optimizes the life-cycle costs of its assets through the use of a CMMS system and real-time data analysis. This approach allows maintenance policies to be adjusted according to the specific needs of the equipment, extending the useful life of the assets and reducing cumulative costs throughout the life cycle [28]. Limited connectivity in remote areas poses a challenge for continuous monitoring. Finally, in the e-Warranty and Customer Relationship Management (CRM) phase, Rolls-Royce employs digital twins in its e-Warranty system to monitor engines in real time and adjust warranty policies, thus personalizing aftersales service [62]. BMW also uses AI in its CRM to analyze customer behavior, allowing for personalized aftersales service and improved loyalty. This strategy has improved efficiency and transparency in warranty management, increasing customer satisfaction [39]. However, the high costs of digital twins and AI limit their adoption in lower-value assets and in emerging markets, where infrastructure and financial resources are more limited [59]. This analysis shows how the use of AI, IoT, digital twins, and CMMS systems optimize both operational efficiency and customer satisfaction in critical sectors and high-value assets. The cases of Rolls-Royce and General Electric stand out for their ability to reduce costs and improve operational availability through the use of digital twins, while Siemens and BMW underline the importance of AI and IoT in personalizing services and reducing failures in customer-experience-oriented sectors [3; 25]. However, the success of these implementations depends on factors such as technological infrastructure, budget, and specialized training [58], as demonstrated by Rolls-Royce (2023) [62].