## 4 Discussion  
The proposal for integrating artificial intelligence (AI), Internet of Things (IoT), and digital twins into the warranty management framework proposed by [1] aims to transform afterales management from a reactive to a proactive model. This section examines the expected results of implementing these technologies in each phase of the digitalized warranty management model. The integration of DRAMS into the warranty management model offers a robust framework to reduce downtime and optimize operating costs. The implementation of AI-driven predictive maintenance and digital twin applications has demonstrated significant operational improvements across industries. In the automotive sector, predictive maintenance systems integrated into BMW's AI-driven warranty management have reduced unexpected failures by 25% and improved claim resolution efficiency by 15%. Similarly, in the railway industry, Siemens' Railigent platform, which utilizes digital twins and IoT monitoring, has increased train availability by 10% while reducing maintenance time by 50%, significantly optimizing warranty-related service interventions. In aerospace, Rolls-Royce's TotalCare program, which applies digital twins for engine \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Warranty Management Framework Phase** & **Specific Objective of the Phase** & **Digital Enablers** & **Specific Technologies and Tools** & **Best Industrial Practice** & **Main Benefit** \\ \hline \multirow{6}{*}{General Enablers for All Phases} & Flexibility and adaptability in each phase of the model, promoting the digitalization of warranty management & \multirow{6}{*}{**Digital Enablers**} & TensorFlow, & Digital transformation of warranty management, \\  & & & AI, IoT, machine learning, data & Tableau, Apache & increasing operational efficiency and optimizing resources and operating costs. \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
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Table 5: _Cont._performance monitoring, has cut maintenance costs by 30% and extended engine life cycles, reducing unscheduled maintenance events. Furthermore, studies indicate that AI-based predictive analytics in warranty services can lower maintenance costs by up to 20% while minimizing warranty fraud and disputes through data-driven diagnostics. These figures reinforce the economic and operational benefits of digital transformation in warranty management, demonstrating its measurable impact on efficiency, cost savings, and asset reliability. Using real-time control and predictive tools, companies can continuously monitor assets, anticipate failures, and apply maintenance interventions accurately. This directly impacts operational continuity and asset availability, which is essential in high-criticality sectors such as aviation and the automotive industry [60; 61]. The implementation of artificial intelligence and predictive maintenance in asset management makes it possible to anticipate failures and optimize maintenance planning, which reduces the need for corrective interventions and maximizes the use of resources. A practical example of this approach is adopted by General Electric, which has managed to reduce its maintenance costs by 25% and extend the life of its turbines through the use of digital twins [52]. This technology enables a reduction in downtime and associated costs by improving asset forecasting and tracking. In the context of the warranty model, this optimization facilitates a reduction in warranty claims by minimizing the occurrence of unexpected failures and promotes a more efficient management of maintenance resources. On the other hand, improved customer satisfaction is achieved through the personalization of aftersales service and the optimization of response times thanks to the use of AI and digital twins. These technologies make possible a proactive aftersales service that detects potential problems before they affect the end customer.