## 3 Methodology
### Implementation and Development of the Presented Methodology
Machine learning algorithms assess the status of assets based on historical data and real-time conditions, while tools such as RapidMiner and KNIME allow large volumes of data to be analyzed, generating a criticality matrix that prioritizes maintenance interventions. In Phase 3, known as Failure Root Cause Analysis, the goal is to determine the causes of recurring failures and adjust maintenance policies. Here, the use of LLM/NLP and machine learning models is key. Tools such as spaCy and BERT analyze textual descriptions of failures, identifying common patterns in maintenance reports, which increases the accuracy of corrective maintenance and reduces the recurrence of incidents. Phase 4 focuses on the use of Warranty-Adapted Maintenance Design Tools. Its goal is to adapt maintenance strategies to the current conditions of assets, thus optimizing resources. Enablers such as predictive models and real-time monitoring allow for continuous adjustments to maintenance tasks. Machine learning tools such as Keras and PyTorch allow for real-time adaptation, reducing unnecessary interventions and extending asset life. In Phase 5, Warranty Policy Risk-Cost-Benefit Analysis, the aim is to evaluate warranty policies through risk and cost simulations. Digital twins and optimization models play a crucial role. These digital twins allow simulating the impact of different warranty policies in various scenarios, while tools such as AnyLogic and IBM CPLEX facilitate these simulations, optimizing the return on investment and minimizing risks. Phase 6, focused on Reliability, Availability, Maintainability, and Safety (RAMS), aims to ensure the availability and safety of assets throughout their life cycle. Machine learning and predictive models are used to achieve this. Tools such as IBM Maximo and Infor EAM enable the continuous monitoring of critical variables, optimizing operational availability and reliability and ensuring that the service meets high safety standards. Phase 7 focuses on life-cycle cost \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Phase 1:** & **Phase** & **Phase 3:** & **Phase 4:** & **Phase \%** & **Phase 6:** & **Phase 7:** & **Phase 8:** \\  & **BSC** & **2:CA** & **FRCA** & **RCM** & **RCBA** & **RAMS** & **LCCA** & **eW and CRM** \\ \hline (a) Machine Learning Models & X & X & X & X & X & X & X & X \\ \hline (b) Data Analysis Techniques & X & X & X & & X & & X & \\ \hline (c) Predictive and Analytical Tools & X & X & X & & X & X & X & \\ \hline (d) LLM/NLP & & & X & X & & & & X \\ \hline (e) Monitoring and Control & X & & & X & & & X & \\ \hline (f) Performance and Quality & & & & & X & X & \\ \hline (g) Life-Cycle Management & & & & & & & X & \\ \hline (h) Collaborative Learning & & & & & & & & X \\ \hline (i) Optimization Models & & X & X & X & & & \\ \hline (j) Continuous Improvement & & & & & & & X \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distribution of each technology in the phases of the warranty management model. analysis, which aims to assess cumulative costs over the asset's life cycle. Data analytics techniques and life-cycle cost models assist in this phase. Tools such as IBM SPSS Modeler and Tableau facilitate the detailed analysis of operation and maintenance costs, allowing for adjustments to warranty policies and optimizing financial sustainability. Finally, Phase 8, e-Warranty and Customer Relationship Management (CRM), seeks to improve the customer experience through a digital warranty platform and proactive relationship management. Enablers include continuous improvement tools, user feedback, NLP, and collaborative learning. Tools such as Salesforce Einstein Analytics and Zoho CRM collect real-time data on the customer experience, personalizing care and tailoring warranty policies to their needs, maximizing customer satisfaction and loyalty. To illustrate how each technology can be implemented in the warranty management model, Table 3 presents a list of specific AI methods and tools applicable in each category.