## 4 Discussion
* Aeronautics Industry: Servitization and Performance-Based Warranty Models: In the aeronautics sector, warranty management is increasingly driven by servitization and performance-based contracts. Companies like Rolls-Royce utilize \"Power-by-the-Hour\" service agreements, wherein airlines pay based on engine performance rather than ownership. This model aligns with digital twin applications, enabling the real-time tracking of aircraft engines and facilitating predictive maintenance to prevent failures. Such warranty frameworks not only reduce operational costs but also ensure asset availability, a critical factor in aviation. Additionally, the stringent regulatory environment in aeronautics necessitates the integration of ISO 55000-based asset management principles within warranty policies, ensuring compliance with industry standards. While all three industries employ predictive analytics and digital transformation to enhance warranty management, their strategies differ based on asset criticality, operational complexity, and customer engagement models. The automotive sector prioritizes customer-centric warranty solutions through predictive diagnostics and extended service contracts. The railway industry focuses on life-cycle cost optimization using digital twins, while aeronautics emphasizes servitization to maximize asset availability and regulatory compliance. These distinct approaches underscore the industry-specific adaptations of advanced technologies in warranty management, demonstrating a shift from reactive to predictive and proactive frameworks. While not designed as a critical comparative analysis of field-tested case studies, the value of this paper lies in providing a unified taxonomy and implementation framework, which can support future empirical studies and benchmarking efforts. As a critical discussion, it is worth mentioning that the implementation of AI, IoT, and digital twins in warranty management presents both benefits and challenges. The main limitations identified are discussed below and solutions are suggested to mitigate these problems. In other words, the implementation of advanced technologies in asset management involves a significant initial investment in infrastructure, sensors, and the development of custom algorithms. According to [64], those companies that manage to overcome this barrier gain a competitive advantage by reducing their costs in the medium and long term. As a solution, the gradual implementation of these technologies is recommended, starting with areas of high initial impact and expanding to other areas as measurable benefits are obtained, thus optimizing the return on investment. Furthermore, the adoption of these technologies requires staff training and adaptation, as they require skills in data analysis and predictive maintenance techniques. Ref. [65] emphasize that investment in staff training is key to maximizing the potential of artificial intelligence and preventing internal resistance. Therefore, it is recommended to establish continuous training programs and collaborate with technology providers to develop and strengthen the digital skills of teams. Integration with legacy systems is another critical challenge, as interoperability between traditional systems and new technologies can be complex due to compatibility issues and data migration. Ref. [66] suggests a gradual transition through the use of compatible platforms that facilitate smooth integration. To this end, an effective solution is the adoption of hybrid solutions, which allow a gradual integration of new systems with traditional ones, thus minimizing operational risks during the process. Another important challenge is data security and privacy, as real-time data collection and analysis increase cybersecurity risks, meaning that compliance with privacy regulations is paramount. While digital transformation enhances efficiency, predictive accuracy, and cost-effectiveness in warranty management, it also introduces significant risks.