## 4 Discussion
BMW, for example, has integrated AI into its claims management system, which has resulted in a notable reduction in response times and a considerable increase in customer satisfaction [63]. In terms of impact on the warranty model, the adoption of a proactive and personalized approach contributes to increasing customer loyalty and improves the perception of aftersales service. To effectively implement AI, IoT, and digital twins in warranty management, practitioners should adopt a phased approach, beginning with pilot programs that integrate predictive analytics into existing maintenance frameworks. Investing in staff training for AI-driven diagnostics and IoT-enabled condition monitoring will be crucial to ensure seamless adoption. Additionally, organizations should establish interoperability between legacy systems and digital twin platforms to enhance data-driven decision-making. Future research should explore the integration of blockchain technology for warranty traceability, ensuring secure and immutable records of maintenance and claims. Moreover, adaptive warranty models based on real-time asset performance data could revolutionize warranty policies by dynamically adjusting coverage based on predictive analytics, ultimately optimizing both cost and service efficiency. Asset life-cycle optimization is facilitated by continuous monitoring, enabled by digital twins, which provide essential data for decision-making on repairs, replacements, and adjustments to warranty policies. An example of this practice is Rolls-Royce's TotalCare program, which uses digital twins to monitor the life cycle of its aircraft engines. This not only reduces the costs associated with the asset life cycle, but also prolongs its usefulness and functionality. In the warranty model, the ability to adjust policies in real time benefits both the company and the customer, providing more accurate and efficient warranty management that responds to the current operating conditions of each asset. Warranty management strategies vary significantly across industries, with each sector adopting unique approaches to optimize operational performance, minimize costs, and enhance customer satisfaction. The automotive, railway, and aeronautics industries exemplify this diversity through their application of predictive maintenance strategies, digital twin technology, and servitization-based warranty frameworks. * Automotive Industry: Predictive Maintenance for Proactive Warranty Management: In the automotive sector, predictive maintenance has revolutionized warranty management by shifting from reactive to proactive service models. By leveraging IoT-enabled sensors and machine learning algorithms, manufacturers can monitor vehicle components in real time, predicting failures before they occur. Companies such as BMW and Tesla integrate AI-driven diagnostics within their afterscales service, ensuring that warranty claims align with the actual condition and usage patterns of vehicles. Additionally, digital servitization plays a crucial role, as OEMs increasingly offer extended service contracts that bundle predictive maintenance with warranty policies, ensuring reduced downtime and improved customer experience. * Railway Industry: Digital Twins for Life-Cycle Optimization: The railway industry has embraced digital twin technology to optimize warranty management and maintenance cycles. Platforms like Siemens' Railigent utilize digital twins to create virtual replicas of rolling stock, enabling real-time monitoring and predictive analytics. This approach allows railway operators to anticipate component failures, reducing unexpected downtime and optimizing maintenance scheduling. Furthermore, the application of reliability-centered maintenance (RCM) ensures that warranty policies are adapted to the criticality of railway assets, balancing cost, risk, and operational efficiency. The integration of digital twins into warranty frameworks enhances decision-making by providing a comprehensive life-cycle assessment of railway components.