## 3 Methodology
### Description of the Phases of the Gonzalez-Prida Warranty Management Framework  
* Phase 1: Balanced Scorecard. In this initial phase, the BSC for warranty management is structured, allowing the organization to analyze key performance indicators (KPIs) that measure the effectiveness of warranty policies. Through AI and data analysis tools, the system obtains and visualizes metrics in real time, allowing for informed and agile decision-making. This phase also includes the use of machine learning and predictive analysis algorithms to anticipate performance issues and adjust resources according to operational and customer needs. * Phase 2: Criticality Analysis. Criticality Analysis establishes an assessment of the key components within the asset life cycle, identifying those with the greatest potential impact on the system [52]. This process allows assets to be classified according to their relevance to the operation, and this is where the use of AI and machine learning algorithms is crucial to analyze large volumes of data and define the criticality of each component. This phase is supported by IoT sensors that collect data on operating conditions in real time, allowing maintenance strategies and warranty coverage to be adjusted for the most critical components. * Phase 3: Failure Root Cause Analysis. In this phase, the root cause of failures is investigated and documented through a detailed analysis of recurring failure patterns using predictive models. AI in combination with big data is an essential enabler to extract failure patterns and determine preventive actions. According to [52], this technology optimizes root cause analysis through algorithms that quickly process historical incidents, allowing possible failures to be anticipated before they occur and thus reducing warranty claims. * Phase 4: Maintenance Design Tools Adapted to Warranty. This phase integrates maintenance design tools, such as digital twins, to simulate the asset's life cycle under different usage conditions and maintenance scenarios. Simulation and RCM allow for the adjustment of warranty and preventive maintenance programs, minimizing costs and maximizing equipment availability. Digital twins act as virtual models that reflect the asset's actual behavior, adjusting intervention times and prolonging component life through simulation-based recommendations. * Phase 5: Warranty Policy Risk-Cost-Benefit Analysis. Warranty policy analysis involves a detailed assessment of the risk, cost, and benefit of different warranty policies. This phase is supported by modelling and simulation tools that assess the economic impact of warranty decisions [52]. Using optimization algorithms and risk analysis, policies are developed that balance the cost of aftersales service with the expected benefits in terms of customer loyalty and satisfaction. * Phase 6: Reliability, Availability, Maintenance, and Safety (RAMS). Reliability and availability are critical aspects in warranty design, as they determine the asset's ability to meet customer expectations over time [53]. This phase integrates IoT-based real-time monitoring systems to assess the asset's reliability and operational availability. AI and data analytics make it possible to anticipate and schedule the interventions necessary to maintain uninterrupted operation, ensuring that warranty policies are consistent with the equipment's actual performance. This phase is essential to meet the quality and performance expectations set out in the warranties. Traditionally, the reliability and availability of assets determine their ability to operate without interruptions, maximizing customer satisfaction and minimizing corrective interventionsthat can be costly and affect the company's reputation. To respond to today's digital transformation requirements, this phase is evolving towards the concept of DRAMS (Digital Reliability, Availability, Maintainability, and Safety).