warranty services. By embedding digital technologies within a well-defined life cycle, this framework bridges the gap between theory and practice, providing a replicable and scalable approach to optimizing warranty management in the automotive, railway, and aeronautics industries. Summarizing, this framework is structured into eight clearly defined phases and, rather than introducing a new methodology, this paper intends to embed digital enablers--such as AI, IoT, and digital twins--into each phase. Its detailed structure, supported by comprehensive tables and references throughout this document, aims to offer both researchers and practitioners a replicable blueprint to navigate the shift from reactive to predictive warranty management effectively. In today's industry, warranty management and aftersales services generally play an essential role in customer satisfaction, cost optimization, and the operational efficiency of companies. This study presents a novel contribution by systematically integrating digital transformation technologies into warranty management, bridging the gap between technological advancements and structured warranty frameworks. Unlike previous review papers, which have either analyzed specific digital technologies (e.g., AI, IoT, or digital twins) in isolation or focused on warranty management as a standalone field, this work synthesizes both perspectives, offering a comprehensive analysis of how AI-driven predictive analytics, IoT-enabled monitoring, and digital twin simulations enhance proactive and data-driven warranty management. Moreover, this study situates warranty digitalization within the evolutionary context of Industry 4.0 and Industry 5.0, highlighting its role in servitization strategies and the future of intelligent aftersales services. By aligning digital warranty models with emerging industrial paradigms, this research provides a structured methodology that facilitates the transition from reactive to predictive warranty frameworks, ultimately enhancing asset life-cycle optimization, cost efficiency, and customer satisfaction. Traditional warranty management approaches, usually reactive, intervene only after the occurrence of failures and customer complaints. This approach often increases costs, prolongs downtime, and limits the responsiveness of companies, negatively affecting customer satisfaction and competitiveness in the market [1]. The emergence of technologies such as artificial intelligence (AI) and the Internet of Things (IoT), together with Computer Maintenance Management Systems (CMMS), may profoundly transform the field of warranty management and lead to a significant increase in the number of aftersales services available [2]. These technologies enable a predictive and proactive approach, where maintenance can anticipate failures and aftersales services can be better tailored to customer needs. The integration of AI and IoT provides real-time visibility into asset status, facilitating service customization and reducing both operating costs and downtime. In industries such as the railway, automotive, and aeronautics sectors, where asset reliability and response times are critical, adopting a predictive approach is essential to maintain high levels of competitiveness. This paper addresses the need to transform the traditional approach to warranty management through digitalization and the use of advanced technologies in order to anticipate failures, optimize resources, and improve customer experience. Technologies such as AI, IoT, and DT make this transition to a predictive approach possible, as they can be used to anticipate failures, optimize asset life cycles, and reduce maintenance costs. Such a transition not only enhances asset availability and productivity, but also improves customer experience and promotes long-term operational sustainability. This paper reviews the warranty management framework designed by [1], adapting it to the opportunities of the digital environment by incorporating digital enablers in each phase of the warranty management cycle.