## 2 Literature Review  
This section examines how advanced technologies are driving transformation in warranty management and details key international models and standards for their effective implementation in today's industry. To ensure a comprehensive and systematic literature review, a structured search strategy was employed to identify relevant academic and industry sources. The search process was conducted across multiple scholarly databases, including Scopus, Web of Science, IEEE Xplore, and ScienceDirect, as well as industry-specific sources such as MDPI, SpringerLink, and Wiley Online Library. The selection of the literature was based on predefined keywords and Boolean operators, including \"warranty management\" AND \"predictive maintenance\", \"digital twins\" AND \"warranty optimization\", \"servitization\" AND \"after-sales service\", \"blockchain\" AND \"warranty traceability\", and \"Industry 4.0\" AND \"artificial intelligence in warranty\". Inclusion criteria were established to ensure relevance and credibility. Articles published within the last 10 years (2014-2024) were prioritized to capture the latest advancements, with exceptions made for seminal works in warranty management and predictive maintenance. Studies were selected based on their empirical contributions, case studies, and theoretical advancements in digital warranty management. Exclusion criteria included non-peer-reviewed sources, duplicated studies, articles lacking methodological rigor, and papers focused on unrelated industries or non-digital warranty models. The final selection process involved screening abstracts, reviewing full texts, and conducting cross-referencing to identify additional relevant sources. This methodological approach ensured a robust and transparent review of the existing literature, forming the foundation for the study's analysis of AI, IoT, and digital twin applications in warranty management. The references used in this study include a total of 71 peer-reviewed articles and reports, selected based on relevance, methodological rigor, and contribution to the field. These references encompass empirical studies, case analyses, and theoretical frameworks from academic and industrial sources. This review focuses on three key technologies: artificial intelligence (AI), the Internet of Things (IoT), and Computer Aided Maintenance Management (CMMS) systems, which enable a predictive and proactive approach to warranty management. Warranty management has evolved significantly with digital technologies. Previously, it followed a reactive approach, addressing failures only after they occurred, resulting in high costs and downtime, especially in critical sectors such as the automotive and aviation industries [2]. Today, the digitalized model allows for continuous monitoring and preventive intervention, adapting warranty policies to the actual use of each asset and improving customer satisfaction. Technologies such as AI, IoT, and DT make this transition to a predictive approach possible by anticipating failures, optimizing asset life cycles, and reducing maintenance costs [2]. It is important to introduce at this point the concept of Industry 4.0, and how its advancement has driven digital transformation by integrating advanced technologies into industrial processes. The new evolutionary phase in which we are immersed is called Industry 5.0, and it takes digital evolution a step further, proposing an approach that, in addition to prioritizing efficiency and connectivity, seeks to focus on sustainability, resilience and a human approach [6]. In this context, warranty management not only optimizes resources and reduces costs, but also fits into a broader framework that considers the impact on the worker, the environment, and the community at large. Industry 5.0, according to the European Commission, complements Industry 4.0 by emphasizing a production model that prioritizes worker well-being and respect for planetary boundaries, reinforcing sustainability and adapting technology to human needs [6].