## 3 Methodology
### Industry Best Practices for Digitalized Warranty Management
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Warranty** & **Specific Objective** & **Digital Enablers** & **Specific Technologies and Tools** & **Best Industrial Practice** & **Main Benefit** \\ \hline Phase 2: & Identify and prioritize critical assets to optimize resource allocation & Machine learning (ML), IoT & IoT & IoT and ML to monitor critical Machines (SVM), RNRIME, RapidMiner & **Sicemens: Railigment platform with AI and NLP for pattern analysis** & **Reduction in recurring failures and maintenance times by 50\%, improving the availability and reliability of the railway system. \\ \hline Phase 3: Failure Root Cause Analysis & Determine patterns and root causes of critical failures & Al, NLP & Bayesian networks, supervised learning, spaCy, NLTK, BERT & Siemens: Railigment platform with AI and NLP for pattern analysis and root cause of failure analysis in rail transport & Reduction in recurring failures and improving the availability and reliability of the railway system. \\ \hline Phase 4: Maintenance Design Tools & Dynamically adjust maintenance design based on design based on operating conditions & Predictive models, IoT & ML models for prediction, Keras, PyTorch, real-time sensors & Rolls-Royce: TotalCare with digital twins to monitor and optimize aircraft & Increased engine availability and reduced downtime by adjusting maintenance costs by reducing costs. \\ \hline Phase 5: Warranty Policy & Evaluate the profitability and risk of guarantee risk of guarantee policies through simulations & Digital twins, simulation, AI & IBM SPSS, AnyLogic, MATLAB, Gurobi Optimization & General Electric: Using digital twins to evaluate warranty policies in gas turbines & Alignment of warranty policies with actual asset performance, reducing maintenance costs by 25\% and increasing profitability. \\ \hline Phase 6: RAMS (Reliability, availability, availability of availability, availability of availability, assets throughout layer life cycle) & Ensuring the reliability and utilization of IoT, ML & Digital twins, MAML, Apache Kafka, Scikit-learn & Siemens: Railigment to maximize train availability and reduction in operational risks, monitoring and medicine and efficiency of rail transport. \\ \hline Phase 7: Life-Cycle Cost Analysis & Optimizing asset life-cycle costs & LCCA, data analytics & IBM SPSS Modeler, Tableau, Infor EAM, SAP PM & Caterpillar: Life-cycle and management with CAMS for heavy machinery & Extending equipment life, reducing cumulative costs, and optimizing resources through data-driven maintenance decisions. \\ \hline Phase 8: e-Warranty and Customer Relationship Management (CRM) & Implement a continuous improvement system in warranty policies, adapting them to the actual behaviour of assets and improving customer relations through a proactive and personalized aftersales service & & Dynamic adjustment of warranty policies, improving transparency and customer satisfaction \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Each column refers to the following aspects:  
1. Phase and Objective: Each phase of the model focuses on a specific objective, aligned with the challenges and needs of digitalized and optimized warranty management. 2. Digital Enablers: Digital enablers applied at each stage, such as machine learning, IoT, NLP, and digital twins, enable a predictive and personalized approach, improving the efficiency of warranty management. 3. Specific Technologies and Tools: At each stage, specialized tools and technologies such as IBM Maximo, AnyLogic, and Salesforce have been selected that integrate AI and data analysis to facilitate the monitoring, simulation, and personalization of afterales services. 4. Best Industrial Practice: Leading companies at every stage have implemented these enablers and technologies to improve their warranty management processes, such as Caterpillar in critical asset identification [28], Siemens in root cause analysis, and Rolls-Royce in adaptive maintenance [26].