## 3 Methodology
### Industry Best Practices for Digitalized Warranty Management  
In this section, we explore the best practices applied by large companies in each phase of the digitalized warranty management model according to the framework proposed by [1], which adapts the originally developed eight-phase model [41]. In other words, leading companies' practices will be utilized to establish best practices for the improvement of successful warranty management strategies, operational enhancement, and customer satisfaction. The approach keeps the established framework in harmony with industry guidelines and emerging technological advancements. Particularly, this analysis focuses on BMW, Rolls-Royce, and General Electric practices, and how the Internet of Things (IoT), artificial intelligence (AI), Computer Aided Maintenance Management (CMMS) systems, and digital twins have transformed their warranty management practices towards a more predictive and proactive approach. The implementation of the Balanced Scorecard (BSC) strategy at BMW seeks to evaluate warranty performance in real time and optimize aftersales management by aligning customer satisfaction metrics with the company's strategic objectives. To undertake this, BMW uses technologies such as machine learning and BI tools, including Tableau and Power BI, integrated into its AI-based CRM system, which allows for real-time data analysis and personalized service based on customer history [58]. This strategy has allowed them to improve efficiency in resolving claims by 15% and increase customer satisfaction thanks to a fast and personalized service [59]. However, the high cost of infrastructure represents a barrier to its implementation in emerging markets. In the Criticality Analysis phase implemented by Caterpillar, the objective is to prioritize critical assets to optimize resources and reduce the risk of operational failures. To undertake this, Caterpillar uses IoT sensors and machine learning algorithms in its Cat(r) Connect Technology platform, which allows real-time monitoring of the status of assets [33]. This has achieved a 20% reduction in maintenance costs and extended the useful life of critical assets. However, the integration of multiple sensors and lack of connectivity in remote areas complicate predictive monitoring in certain regions [60]. For Failure Root Cause Analysis, Siemens employs its Railigent platform, which uses AI and natural language processing (NLP) to analyze large volumes of data from IoT sensors, identifying failure patterns that enable preventative interventions [26]. This approach has succeeded in reducing maintenance time by 50% and increasing train availability by 10% [61]. However, complying with international regulations and training staff to handle complex data poses additional challenges. In the aeronautical field, Rolls-Royce adjusts its maintenance policies in the Maintenance Design Tools Adapted to Warranty phase, using digital twins and AI algorithms in its TotalCare program. This technology allows real-time performance monitoring and predicting the future state of engines, which improves service reliability by reducing claim resolution times by 30% and increasing engine availability [62]. However,