Digital Transformation in Aftersales and Warranty Management: A Review of Advanced Technologies in I4.0

Abstract

This research examines how Industry 4.0 technologies such as artificial intelligence (AI), the Internet of Things (IoT), and digital twins (DT) are used in the digital transformation process of warranty management. This research focuses on converting traditional warranty management practices from reactive systems to predictive and proactive ones, improving operational performance and customer experiences. Based on an already established eight-phase framework for warranty management, this paper reviews machine learning (ML), natural language processing (NLP), and predictive analytics, among other advanced technologies, to enhance warranty optimization processes. Best practices in the automotive sector, as well as in the railway and aeronautics industries, have experienced substantial achievements, including optimized resource utilization and savings, together with tailored services. This study describes the limitations of capital investments, labor training requirements, and data protection issues. Therefore, it suggests implementation sequencing and staff education approaches as solutions. In addition to the current evolution of Industry 4.0, this research’s conclusion highlights how digital warranty management advancements optimize resources and reduce costs while adhering to international standards and ethical data practices.

1. Introduction

This paper aims to review the impact of digitalization and AI on warranty management, addressing the practical implications and associated challenges. To achieve this, a revision to a warranty management framework is proposed, enabling transformation towards a proactive and predictive approach that maximizes operational efficiency and customer satisfaction. The warranty management framework described in [1] is a structured methodological approach that integrates digital enablers across an eight-phase life cycle, ensuring predictive and proactive warranty management. Unlike PMBOK, which defines knowledge areas and best practices without prescribing a specific implementation methodology, our framework serves as an applied model for organizations to systematically incorporate AI, IoT, and digital twins into warranty processes. This distinction is essential, as the study does not merely present generalized theoretical principles, but rather a structured implementation strategy tailored for digital transformation in aftersales and 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. This adaptation delves into an approach based on technologies such as AI and IoT, which allows companies to respond in an agile and proactive manner to both maintenance needs and customer demands. This contributes to developing a more efficient warranty management model aligned with international best practices and adapted to the challenges of the current industrial sector. The topic of this paper is aligned with the knowledge and skills developed in the field of industrial organization and business management. A fundamental theoretical and practical basis is provided that addresses failure modelling, maintenance policy optimization, and the design of risk and replacement models. These concepts are essential for advanced warranty management, facilitating the prediction and resolution of failures more effectively and reducing the costs associated with unexpected maintenance. In addition, this approach is complemented by providing critical tools for the interpretation of real-time data, which is necessary in the management of performance and maintenance indicators. By employing emerging technologies like machine learning and digital twins (DT), organizations can employ predictive models in the aftersales service value chain, improving the accuracy of interventions and optimizing the life cycle of assets. In this way, the review applied in this study not only modernizes the warranty management framework, but also responds to the strategic needs of the industrial sector, promoting the competitiveness and sustainability of organizations in an environment of constant change.

The objective of this study is to provide a structured review of existing methodologies, synthesizing current research and best practices in digital transformation for warranty management. Rather than introducing entirely new approaches, this work integrates and systematizes knowledge on how artificial intelligence (AI), the Internet of Things (IoT), and digital twins enhance predictive and proactive warranty strategies. By analyzing key frameworks and industry applications, this study aims to offer a comprehensive perspective on optimizing warranty management through digital enablers. To achieve this purpose, the following specific objectives are set:

O1

Identify and evaluate key technologies (AI, IoT, digital twins, and Computer Maintenance Management Systems) in warranty management, emphasizing their role in transitioning from reactive to predictive maintenance. This objective is achieved through a structured synthesis of the specialized literature and industry applications.

O2

Assess the impact of advanced digital technologies on reducing operational costs, improving response times, and enhancing customer satisfaction in warranty services. This involves analyzing case studies and empirical research to illustrate how digital tools optimize warranty-related decision-making.

O3

Examine how digital enablers align with industry standards and best practices, including ISO 55000 (Asset Management [3]), ISO 9001 (Quality Management [4]), and ISO 42001 (Occupational health and safety Management [5]). This assessment ensures that digital warranty models adhere to regulatory compliance, risk management, and ethical AI implementation across the automotive, railway, and aeronautics sectors.

This study contributes to the field by providing a structured, integrative review that consolidates state-of-the-art research and industry applications, supporting the practical implementation of digital transformation in warranty management. The document is organized into the following main sections: Introduction, which provides the context, justification, and objectives of the study; Literature review, which explores the advanced technologies and management techniques applicable to the digital transformation of warranty management; Methodology, which describes the process of integrating advanced technologies into the aftersales and warranty management framework; Discussion, which analyses the findings obtained previously; and Conclusions, which summarizes the main aspects of the study and offers recommendations for the transition to a digital warranty management framework in other industrial sectors. Appendix A provides a glossary of terms, and Appendix B a list of acronyms.

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]. In warranty management, this approach involves using advanced technologies in ways that not only anticipate technical issues, but also foster a safe working environment and minimize environmental impact, aligning operations with sustainable development goals and corporate social responsibility.

2.1. Advanced Technologies for Aftersales and Warranty Management

Digital transformation has revolutionized warranty management, enabling a more predictive and efficient approach that reduces costs and improves customer experience. To achieve this shift, the integration of advanced technologies such as artificial intelligence, the Internet of Things (IoT), digital twins, and blockchain has proven to be instrumental. Each of these technologies is explored below, detailing their specific application in warranty management and their contribution to a more effective and adaptable aftersales service model.

Artificial intelligence (AI) is a key pillar in the modernization of warranty management, enabling accurate failure prediction and the optimization of maintenance policies through real-time data analysis. Machine learning (ML) algorithms collect and analyze operational data from assets to identify patterns that could signal future problems before they arise. For example, in the automotive industry, ML systems can continuously analyze vehicle performance and detect wear patterns in specific components, such as the brakes or the engine. This anticipatory ability not only reduces downtime, but allows companies to adjust warranty policies to the actual usage conditions of each asset, thereby personalizing aftersales services [2]. AI, therefore, brings a proactive approach to warranty management, transforming reactive service into a preventive and optimized one. This approach not only benefits the company by reducing costs, but also increases customer satisfaction by offering a fast response tailored to their specific needs [7].

The Internet of Things (IoT) has transformed the way assets are monitored in industry, enabling the use of connected sensors that provide real-time data on the status and performance of equipment and components [8]. These sensors installed on assets make it possible to detect operating anomalies early, facilitating preventive maintenance planning and reducing the need for corrective interventions. In sectors such as heavy machinery, IoT is particularly useful for monitoring the wear of critical parts, such as excavator chains or hydraulic systems. This allows maintenance personnel to proactively intervene before a significant failure occurs, minimizing production interruptions and optimizing resource allocation [9]. By offering an accurate, real-time picture of asset status, IoT improves warranty and maintenance decision-making [10]. In the context of the Fourth Industrial Revolution, IoT thus becomes a key element to optimize the life cycle of assets, as it enables massive data collection and analysis to improve forecasting and long-term performance [11,12]. In the aeronautical industry, digital twins applied to aircraft turbines allow engineers to simulate multiple operating conditions and assess wear at critical points, anticipating potential failures. This advanced simulation capability not only reduces risks and downtime, but also allows warranty policies to be adjusted based on the actual conditions of use of the asset. As a result, more accurate and customized warranty coverage is offered, aligned with the life cycle and condition of each specific asset [13,14]. In this way, digital twins become a strategic tool that not only optimizes maintenance, but also adds value to the customer, improving the accuracy of warranty coverage and increasing the reliability and efficiency of assets during their life cycle [11].

Blockchain technology adds an unprecedented level of transparency and security to the traceability of warranty events, from the manufacturing phase to aftersales service. By using blockchain, each maintenance intervention is recorded in an unalterable way, allowing all interested parties to access a common and secure version of warranty information. This traceability reduces disputes and facilitates the resolution of claims, bringing greater confidence and security to the process [15]. In addition, advanced data analytics allows companies to study asset usage and performance patterns, identifying trends that anticipate future failures. Predictive models generated from advanced analytics allow warranty policies to be adjusted based on the actual operating conditions of assets. For example, digital twins facilitate detailed simulations of asset status, which is essential in complex industrial sectors such as aeronautics, where engineers can analyze wear and tear and anticipate failures in aircraft turbines without affecting physical operations [16]. Together, blockchain and advanced analytics can be said to strengthen warranty management by providing reliable traceability and predictive capabilities that optimize maintenance resources and minimize risks.

Large Language Models (LLMs), together with natural language processing (NLP) techniques, greatly enhance warranty management through advanced data interpretation capabilities targeting unstructured large volumes. In order to generate individualized CRM solutions through experience and issue-based responses, tools under NLP extraction systems assist in modifying consumer feedback and failure report data into usable insights [11,16]. The addition of LLMs extends these capabilities through advanced human-like text analytical and text generation functionalities that generate more precise predictive maintenance approaches, along with enhanced decision-making systems.

2.2. Asset Management Methodologies and Models Applied to Warranty Management

Warranty management has evolved beyond traditional approaches that reacted to failures and claims after they occurred. Today, digital transformation has opened the door to advanced asset management models and methodologies, whose purpose is to anticipate problems and optimize the life cycle of products through prevention and predictive maintenance. These methodologies not only optimize companies’ internal processes, but also increase customer satisfaction by reducing downtime and offering more effective aftersales services. The following subsections describe the key models that underpin this advanced approach to warranty management.

2.2.1. Balanced Scorecard (BSC) Model

The Balanced Scorecard (BSC) is a strategic management tool that allows organizations to align their organizational objectives with specific performance indicators [17]. In the context of warranty management, the BSC provides a comprehensive view of asset performance across four key dimensions: financial, customer, internal processes, and learning and growth. This balanced approach enables companies to proactively adapt their warranty policies, ensuring that they respond to both operational needs and customer expectations.

• Financial Perspective: The BSC helps monitor and optimize the financial impact of warranty management through predictive maintenance. The ability to anticipate and avoid failures minimizes downtime and reduces the operational costs associated with claims, increasing asset profitability and its contribution to the company’s bottom line.
• Customer Perspective: In this dimension, the BSC focuses on customer satisfaction by evaluating both the speed and quality of claims resolution. Through AI and data analysis, warranty policies can be tailored to individual customer needs, offering agile aftersales services aligned with each user’s expectations, increasing their loyalty and perception of value.
• Internal Process Perspective: Integrating technologies such as AI and automation enables the optimization of internal warranty management processes. Continuous asset monitoring facilitates the early detection of potential failures, allowing intervention before significant issues occur. This proactive approach maximizes asset availability and reliability, ensuring efficient aftersales services.
• Learning and Growth Perspective: Constant training of staff in the use of digital technologies is essential to maintain advanced and sustainable warranty management. Through the BSC, companies can measure and strengthen their team’s ability to adapt to new technologies and real-time analysis systems, ensuring that human talent is prepared to respond to the demands of a digitalized environment.

Broadly speaking, the BSC provides a structured and balanced framework that allows monitoring the performance and effectiveness of warranty policies, maintaining constant alignment with the strategic objectives of the organization and with customer expectations [18]. In addition to being a measurement tool, the BSC facilitates the alignment of digitalization and AI initiatives with the company’s strategic objectives. This is especially valuable in sectors such as the automotive or aeronautics industries, where assets represent significant investments and must be kept in optimal operation to justify the cost. Continuous feedback through KPIs allows areas for improvement to be identified—adjusting warranty policies according to the evolution of market demands—and promotes sustainability and technological innovation, essential factors in modern warranty management [18].

2.2.2. Enterprise Asset Management (EAM) Methodology

Enterprise Asset Management (EAM) is a comprehensive approach that covers the entire asset life cycle from acquisition to retirement and relies on advanced technologies such as the Internet of Things (IoT) and predictive analytics to optimize asset performance. This model enables not only continuous, real-time asset monitoring, but also predictive analytics, which is critical in warranty management to reduce downtime and improve system reliability. In the context of warranty management, EAM offers several advantages that help transform traditional approaches into more proactive and personalized ones, thus improving customer experience and operational efficiency:

• Maintenance Planning: EAM platforms allow maintenance interventions to be planned based on the current status and performance of assets, thereby reducing unexpected claims. According to [19], EAM facilitates advanced diagnostics and condition-based maintenance, ensuring that interventions are carried out accurately and in a timely manner. This condition-focused approach helps avoid costly downtime and maintain equipment availability.
• Asset Life-Cycle Management: This approach ensures that each phase of the asset life cycle (acquisition, operation, maintenance, and replacement) is geared towards maximizing its performance and availability over the warranty period. By managing the entire life cycle, EAM enables maintenance policies to be tailored and adjusted to the operating conditions of the asset, promoting a reduction in the number of claims and increased customer satisfaction [20].
• Data Analytics and Prediction: With artificial intelligence (AI) and machine learning tools, EAM systems make it possible to anticipate failures and customize warranty policies based on the asset’s actual condition and usage. This predictive analytics capability helps identify wear patterns and optimize warranty coverage, ensuring that interventions are timely and effective [7]. Through this customization, EAM improves asset reliability and ensures that maintenance resources are used effectively.

To implement an effective EAM system, specialized applications, such as Maximo^®, integrate maintenance management and asset life cycle into a unified platform. Maximo facilitates data analysis and maintenance planning, maximizing operational efficiency and ensuring optimal asset performance throughout the life cycle [21]. For all these reasons, it can be said that EAM facilitates efficient warranty management by maintaining the precise control of an asset’s performance throughout its life cycle, allowing for reduced costs associated with claims, improved asset reliability, and, ultimately, increased customer satisfaction. In addition, it should be noted that this tool reduces maintenance costs and allows for a quick response to claims, improving customer satisfaction and adjusting warranty policies to the actual state of the assets [4].

2.2.3. RCM and CBM: Focus on Asset Criticality in Warranty Management

Reliability-centered maintenance (RCM) and condition-based maintenance (CBM) are advanced maintenance methodologies that focus on the criticality of assets, allowing for more timely and effective interventions. Both provide great value to warranty management, as they allow warranty policies to be adjusted based on operational needs and the specific status of each asset, thus achieving more precise and tailored management [22].

RCM is geared towards identifying the critical functions of each asset and analyzing possible failure modes, which facilitates the prioritization of maintenance resources on those assets whose criticality is most important. This approach optimizes performance and reduces the frequency of claims, ensuring that key assets operate without unnecessary interruptions. The application of RCM in warranty management offers a preventive structure that reduces maintenance costs and improves customer satisfaction by avoiding failures in assets essential to the business.

CBM, on the other hand, enables maintenance interventions to be carried out only when data indicate a real need, based on the current state of the asset. This approach is supported by continuous monitoring using sensors and real-time data analysis, which detect wear patterns or unusual conditions before critical failures occur. In the context of warranty management, CBM adapts coverage for critical assets according to their operational status, thus optimizing resource allocation and personalizing warranty service. In this way, CBM not only improves operational efficiency, but also reduces the downtime and costs associated with corrective maintenance. Condition-based maintenance (CBM) focuses primarily on determining the optimal time to perform maintenance, based on the specific condition of the equipment. This approach is precise and data-driven. On the other hand, RCM is oriented towards selecting the most appropriate maintenance strategy.

Both methodologies, by prioritizing critical assets, can be said to ensure efficient and timely intervention that optimizes the asset life cycle and improves customer experience. The use of RCM and CBM facilitates adaptive and cost-effective warranty management, which responds to the real needs of assets and allows companies to reduce costs and downtime, thus promoting a competitive advantage in the market.

2.2.4. Lean Asset Management Methodology

Lean Asset Management is a methodology that focuses on eliminating waste and maximizing added value at each stage of the asset life cycle. Inspired by the principles of lean manufacturing, lean management focuses on continuous improvement, reducing downtime, and the efficient use of resources. According to [1], lean and efficient management is crucial to optimizing response times and reducing costs associated with returns and repairs. By eliminating inefficiencies and focusing operations on activities that truly add value, lean management also improves customer satisfaction through more agile and efficient aftersales services, which reduces the duration of operational interruptions and improves asset profitability.

Lean Asset Management is based on three key elements that contribute to optimizing performance and efficiency in asset management. The first of these elements is continuous improvement, or Kaizen, which promotes a culture of constant improvement in the organization. Under the Kaizen principle, incremental improvements are implemented in maintenance processes and asset management, allowing for the progressive optimization of operations. This approach helps reduce failures and minimize interruptions, promoting more reliable and efficient operation. Another essential pillar is waste reduction, a central component of the lean methodology that seeks to identify and eliminate all types of inefficiencies in operations. This includes reducing waiting times, eliminating repetitive failures, minimizing excessive inventories, and eliminating any activity that does not add value to the process. In the context of warranty management, waste reduction translates into greater operational efficiency and reduced management costs. The third key element is visual management, which uses visual tools such as dashboards and KPIs to facilitate the continuous monitoring and evaluation of asset performance. This visual management enables an agile response to any deviation in performance and supports the continuous optimization of warranty processes. KPIs used in this strategy may include indicators of downtime, maintenance costs, and number of warranty claims. These key elements of Lean Asset Management drive efficiency and effectiveness in asset management, ensuring that processes are fluid and aligned with strategic objectives. Implementing Lean Asset Management enables companies to ensure their assets operate efficiently and profitably throughout their life cycle, while optimizing warranty policies and improving process profitability. In the context of warranties, Lean Asset Management has multiple benefits, including the following:

• Optimization of Aftersales Service Operations: Eliminating inefficient processes and continuous improvement allows for faster and more effective aftersales service, reducing response times to warranty claims.
• Reduced Operating Costs: By minimizing waste and optimizing resources, companies can reduce costs associated with returns, repairs, and preventive and corrective maintenance.
• Improved Customer Satisfaction: A fast, efficient, and reliable aftersales service improves customer experience, which can translate into greater loyalty and trust in the brand [23].

In summary, implementing lean management improves customer satisfaction by reducing response time and optimizing resources in warranty management.

2.2.5. Digital Servitization and Predictive Maintenance: Adaptation of Warranty Policies

Digital servitization and predictive maintenance transform warranty management by allowing coverage policies to dynamically adapt to the real-world conditions and specific usage of each asset. By using advanced technologies such as artificial intelligence (AI) and the Internet of Things (IoT), companies can monitor asset health in real time and analyze operational data, adjusting warranties based on current equipment wear and condition.

• Real-Time Monitoring and Optimization: Digital servitization turns physical products into smart assets that collect continuous data on their status. This allows for preventive adjustments and resource optimization before a breakdown occurs, improving equipment reliability and, consequently, customer experience.
• Coverage Personalization: With predictive maintenance, warranty policies are tailored to the asset’s specific condition and usage profile. This means that rather than following standard coverages, warranties reflect the actual performance and wear of the equipment, offering protection that better meets customers’ individual needs. This approach creates a personalized experience, increasing user satisfaction by aligning expectations with the asset’s condition and operational capability.
• Cost Reduction and Increased Availability: By anticipating failures and avoiding unnecessary interventions, companies reduce maintenance costs and minimize downtime. This not only reduces the frequency of unexpected claims, but also ensures greater asset availability, optimizing the aftersales service experience and strengthening the customer–company relationship.

Together, digitalization and predictive maintenance turn warranty management into an adaptable and proactive model that provides a more accurate and reliable service. This ensures that warranties not only cover asset failures, but also contribute to maximizing their useful life and improving customer satisfaction, with customers receiving personalized, high-quality service. As a descriptive example, it is shown how digital servitization is applied in the automotive industry through the integration of connected vehicles and preventive maintenance services. In this context, digital servitization facilitates the implementation of five levels of autonomy, from assisted driving to fully autonomous driving. Each level of autonomy contributes to reducing unplanned maintenance interventions and optimizes the customer experience, directly aligning with a warranty model based on real conditions of use. Digital servitization transforms physical products into smart solutions that, thanks to AI and IoT, allow the creation of automated and personalized support services. This improves the customer experience and facilitates warranty management adapted to the real state of the assets, helping to reduce maintenance costs and optimize customer loyalty. This model, in which the product is only one part of the overall value offered, drives companies towards a strategic business approach based on aftersales service and adaptability [24]. Together, digitalization and predictive maintenance turn warranty management into an adaptable and proactive model that provides a more accurate and reliable service. This ensures that warranties not only cover asset failures, but also contribute to maximizing their useful life and improving customer satisfaction, with customers receiving a personalized and high-quality service.

2.3. Relevant Norms and Standards in Aftersales and Warranty Management

In order to effectively implement advanced technologies in warranty management, it is essential to follow international standards that govern both the use of artificial intelligence (AI) and asset management. These standards ensure security, transparency, and quality in processes, facilitating an ethical and reliable application of new systems. Among the most prominent standards in this area are the standards represented in Figures 2–10, developed by us, the ISO standard/IEC42001 (AI Systems Management) [25], ISO 55000 (Asset Management) [3], and ISO 9001 (Quality Management) [4], which play a key role in regulating and optimizing warranty management.

ISO/IEC 42001 establishes guidelines for the management of AI systems in organizations that use this technology in critical operations, such as failure prediction and warranty optimization [25]. Its objective is to ensure that AI systems operate in an ethical, transparent and secure manner, minimizing risks associated with automated decisions and protecting data privacy. This standard emphasizes three essential aspects:

• Ethics and Transparency: ISO/IEC 42001 highlights the importance of implementing unbiased AI in automated decision-making. This includes managing data in compliance with privacy regulations, ensuring it is handled fairly and ethically—a vital aspect in customizing assurance services.
• Safety and Reliability: In critical applications, such as predictive maintenance, the standard states that AI systems must be highly reliable and accurate to avoid unforeseen failures. Accurate failure prediction is critical to optimizing asset life cycles and reducing warranty claims.
• Continuous Evaluation: AI systems must undergo regular evaluations to ensure their alignment with continuous improvement principles. This involves constantly monitoring algorithms and adapting their parameters based on the latest data.

A practical example can be seen in [26], which adopted the ISO/IEC 42001 standard for its Railigent platform, using AI for predictive maintenance in trains. This system allows monitoring the status of assets in real time, anticipating the right time for maintenance interventions and improving operational efficiency by complying with the transparency and security principles of the standard. Another example is seen in [3,4], where AI is employed in a customer relationship management (CRM) system using IoT sensors in vehicles. This system allows BMW to personalize warranty management, ensuring that automated decisions about aftersales services remain within the ethical and safety standards of ISO/IEC 42001.

ISO 55000 and ISO 9001 are essential standards for ensuring efficiency and reliability in asset management and the quality of warranty service. Both standards allow organizations to optimally manage the life cycle of assets and offer high-quality service in warranty resolution and aftersales support. ISO 55000 establishes a global framework for asset management throughout the life cycle. This approach encourages preventive and predictive maintenance, minimizing risks and optimizing operating costs. In warranty management, ISO 55000 allows decisions to be made based on the actual performance of assets, maximizing their useful life and reducing claims. An example of its application in the industrial sector can be seen in [27], which applies ISO 55000 together with predictive maintenance platforms to adjust warranty policies. Through real-time analysis and the use of historical data, GE maximizes the performance of its turbines and reduces downtime [28]. This use of ISO 55000 ensures that warranty decisions are based on accurate data, optimizing resources and increasing customer satisfaction [29,30].

ISO 9001, on the other hand, is the international standard for quality management. In the context of warranty management, it ensures that all processes related to aftersales services maintain high quality standards. This not only improves customer satisfaction, but also helps companies reduce errors and improve efficiency in resolving claims. Its application in the industrial sector is visible in companies such as those described in [31,32,33], which have integrated ISO 9001 with real-time monitoring tools to raise quality standards in aftersales services. This approach ensures that warranty and claims processes remain consistent and effective, increasing service reliability and minimizing errors.

The integration of ISO 55000 and ISO 9001 standards enables organizations to comprehensively address asset management and quality in warranty management. While ISO 55000 maximizes asset value and durability, ISO 9001 ensures that all aftersales service processes follow high quality standards. The synergy between both standards fosters operational sustainability, better adapting to regulatory demands and market expectations. In addition, digitalization and the use of technologies such as AI and IoT enable the real-time monitoring of assets, optimizing maintenance processes and ensuring that aftersales services are continuously adjusted to real conditions. This allows for efficient resource management and an adaptable warranty service, aligned with technological evolution and the principles of sustainability and ethics.

In general terms, the following table (

Table 1. Summary of regulations. (Source: own elaboration.)

Norm/ Standard	Main Features	Application in Warranty Management	Practical Example
ISO/IEC 42001	Ethics and transparency in AI	Failure prediction with AI, risk minimization	Ref. [26]: predictive maintenance in trains
	Security and reliability in automated decisions	Ensure responsible decisions in personalization of AI services	Refs. [3,4]: personalizing aftersales service in CRM
ISO 55000	Comprehensive asset life-cycle management	Reducing claims with predictive maintenance	Refs. [26,27]: maximizing turbine Performance
ISO 9001	Quality in aftersales service processes	Improved satisfaction in warranty resolution	Refs. [28,29,30]: quality aftersales service through IoT

) compares the ISO/IEC standards 42001, ISO 55000, and ISO 9001 in terms of their specific contributions to warranty management. While ISO/IEC 42001 focuses on the transparency and security of AI systems, ISO 55000 emphasizes maximizing the asset life cycle and ISO 9001 ensures quality in aftersales services.

The table above shows a summary of the different regulations described and their main characteristics. This synergy provides a robust framework for advanced warranty management, ensuring that aftersales processes maintain high standards of quality and efficiency, adapting to the demands of sustainability and ethics in today’s industry.

2.4. Industry 5.0 and the OECD Framework on AI in Warranty Management

Industry 5.0 represents a step forward from Industry 4.0, integrating a humanistic and sustainable approach in the use of advanced technologies, such as artificial intelligence (AI) and the Internet of Things (IoT) [6]. This emerging paradigm, promoted by the European Commission, emphasizes the need for people-centered and environmentally friendly industrial development, promoting resilience, sustainability, and social responsibility in industrial processes. In parallel, the OECD Framework on AI [34] sets out ethical principles to ensure that AI systems are safe, transparent, and non-discriminatory, which is critical in sensitive applications such as warranty management and aftersales services. By applying these principles to warranty management, companies can not only improve operational efficiency, but also ensure that automated decisions (e.g., failure prediction or service personalization) meet high ethical and transparency standards, increasing customer trust and optimizing the aftersales service experience.

Digital transformation initiatives in warranty management must align with the principles of transparency, explainability, risk management, safety, and fairness to ensure responsible and ethical AI applications. Transparency and explainability are critical in AI-driven warranty analytics, as automated decision-making must be auditable and interpretable to avoid biases in claim evaluations. ISO 42001, the international standard for AI governance, establishes guidelines for ensuring AI-driven warranty assessments adhere to ethical and regulatory frameworks, minimizing the risks associated with biased predictions, incorrect warranty claims, and opaque decision-making processes [35,36]. Additionally, ISO 55000 (Asset Management) and ISO 9001 (Quality Management) reinforce best practices by ensuring that AI-based predictive maintenance aligns with reliability and safety expectations, thus optimizing warranty coverage without compromising operational integrity. Industry leaders, such as Siemens’ Railigent and Rolls-Royce’s TotalCare, demonstrate how AI-driven warranty systems can enhance fairness and accountability by ensuring real-time monitoring, proactive maintenance, and ethical decision-making in asset life-cycle management. By integrating these standards and best practices, companies can mitigate AI-related risks, enhance customer trust, and ensure compliance with global regulatory requirements while optimizing warranty strategies [37,38].

The transition to Industry 5.0 in warranty management brings several benefits, as it introduces a more humanized and personalized approach to processes. This translates into a dynamic adaptation of warranty policies, based on individual customer needs and the current status of assets. Instead of a reactive approach, Industry 5.0 enables companies to anticipate problems through continuous monitoring and predictive analysis, minimizing operational disruptions and improving asset availability. For example, companies such as BMW have integrated IoT sensors into their vehicles to collect real-time data on component status [39]. This information, analyzed with AI, makes it possible to predict possible failures and personalize maintenance interventions, optimizing the life of assets and reducing maintenance costs. This approach not only makes it possible to offer maintenance solutions adapted to real use, but also promotes sustainability by reducing unnecessary repairs, extending the life of products.

The OECD Framework on AI sets out a number of key principles for AI systems to be used ethically and responsibly. These principles include transparency, safety, explainability, and non-discrimination in automated decision-making processes. In warranty management, these principles ensure that failure predictions and claim assessments are fair and auditable, avoiding bias and protecting the privacy of customer data. By applying these principles in warranty management, a system is promoted in which AI decisions are not only accurate and reliable, but also justifiable and reviewable [40]. This is crucial for customers to trust the use of AI in aftersales service, as it allows automated decisions in cases such as claims or maintenance to be made in an ethical and transparent manner. Some examples of principles applied to warranty management include the following:

• Transparency and explainability: AI systems must be able to justify their decisions. Companies such as Siemens apply AI to analyze large volumes of data and offer proactive solutions to maintenance. These systems comply with the OECD principle of transparency [35], allowing processes to be audited and ensuring the accuracy of the decisions taken.
• Safety and risk mitigation: In critical processes, such as predictive maintenance, AI must be reliable to avoid unexpected failures. Siemens’ Railigent platform [26], for example, follows these principles to monitor railway assets in real time, anticipating failures and ensuring safety in the use of AI.
• Non-discrimination and fairness: AI systems must avoid bias and ensure fair decisions. This principle is fundamental in automated aftersales services, as it allows AI to handle claims without prejudice or discrimination errors.

The combination of Industry 5.0 principles and the OECD Framework on AI offers tangible benefits for warranty management, enabling a more ethical, safe, and sustainable use of advanced technologies. Some of the specific benefits include the following: (i) Maintenance optimization: AI and IoT allow warranty policies to be dynamically adjusted, reducing claims and improving customer satisfaction. (ii) Cost and downtime reduction: The ability to accurately predict failures facilitates preventative maintenance, reducing unforeseen repair costs and operational disruptions. (iii) Sustainability: Industry 5.0 promotes the efficient use of resources and waste reduction, which is positive for both warranty management and the environment. Extending the useful life of products reduces the environmental impact of operations. Adopting Industry 5.0 principles [6] and the OECD Framework on AI [34] in warranty management represents an innovative and ethical approach that enables companies to improve the quality of aftersales services and optimize their resources. These principles help companies use AI and other advanced technologies in an ethical manner, promoting a positive customer experience and encouraging sustainability and transparency in decision-making. With this approach, companies can offer an aftersales service that is not only efficient, but also aligned with the values of social responsibility and sustainability that today’s market demands.

3. Methodology

This section focuses on the selection and integration of digital enablers into a warranty management framework based on Adolfo Crespo’s eight-phase model [37,38] (Figure 1), adapted by [1] (Figure 2) for warranty management. This methodological framework, initially designed for asset management, is redefined to respond to the complexity and digitalization demands of the present day in the industrial field, where efficient aftersales service is essential for customer satisfaction and operational sustainability [41]. The adaptation of this model allows a transition from a reactive approach, aimed at resolving failures after they occur, to a proactive and predictive model, which allows anticipating possible problems and optimizing maintenance resources.

Modern warranty management heavily relies on advanced machine learning algorithms and optimization models to enhance predictive capabilities, classify failures, and optimize cost–benefit trade-offs:

• Artificial Neural Networks (ANNs) for Predictive Maintenance: ANNs are widely used to predict asset failures by analyzing historical performance data and identifying degradation patterns. These networks simulate the functioning of human neural structures to detect complex correlations in large datasets. In warranty management, ANNs enable early fault detection and maintenance scheduling, reducing unexpected failures and optimizing warranty claims [43,44].
• Support Vector Machines (SVM) for Failure Classification: SVM is a supervised learning algorithm that classifies data points by identifying hyperplanes that best separate failure modes from normal operational conditions. In warranty systems, SVM models are employed to categorize defects based on sensor data, maintenance logs, and operational parameters. This classification enhances root cause analysis, allowing manufacturers to refine warranty policies based on recurring failure types and severity [45].
• Bayesian Networks for Probabilistic Risk Assessment: Bayesian networks model dependencies between multiple variables to estimate the probability of failure under different operating conditions. This probabilistic approach is particularly useful in risk-based warranty decision-making, allowing companies to dynamically adjust their coverage and optimize maintenance intervals based on evolving asset conditions and environmental factors [46,47].
• Optimization Models for Cost–Benefit Trade-Offs in Warranty Policies: Various optimization models, such as stochastic programming, genetic algorithms, and mixed-integer linear programming (MILP), are utilized to determine the most cost-effective balance between warranty costs and service performance. These models assess variables such as failure rates, repair costs, downtime penalties, and warranty extension probabilities to establish optimal service contract structures that maximize customer satisfaction while minimizing operational expenses. For instance, MILP models help manufacturers dynamically adjust warranty coverage based on real-time asset performance, ensuring financial sustainability while maintaining reliability commitments [48,49].

These digital enablers offer real-time analysis and prediction capabilities that optimize maintenance, reduce operating costs, and maximize asset availability, generating benefits for both the organization and customers [1]. This proactive approach to warranty management directly responds to the findings of [1], who argue that implementing digital technologies in asset life-cycle management not only optimizes operational efficiency, but also improves customer experience by offering agile aftersales service that is necessities-oriented.

The incorporation of digital twins is another essential enabler within this framework. This technology allows the simulation of the asset’s life cycle under different scenarios, providing a predictive and detailed view of the equipment’s behavior in specific conditions [50]. The simulation of digital twins facilitates the design of maintenance strategies tailored to the characteristics of each asset, allowing precise interventions that extend its useful life and reduce operating costs. In an aftersales service context, digital twins also offer traceability and transparency, ensuring that maintenance and warranty management decisions are based on current data and a predictive and personalized view of the asset in its life cycle [51]. For all these reasons, the selection of these technologies responds to a comprehensive digitalization strategy that meets the current demands for precision, customization, and efficiency in warranty management, as highlighted by [1]. This methodology not only optimizes operational performance, but also positions the organization to face the challenges and expectations of customers in an increasingly competitive industrial environment focused on customer satisfaction.

3.1. Description of the Phases of the González-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 interventions that 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). DRAMS not only applies RAMS principles in a digitalized context, but also uses advanced tools such as artificial intelligence (AI), Internet of Things (IoT), and digital twins to optimize the monitoring and predictive analysis of critical assets. This digitalized approach enables the continuous, real-time assessment of asset reliability and availability, ensuring that warranty policies are aligned with current equipment performance and not just theoretical expectations. The components and benefits of DRAMS in warranty management can be summarized in the following points:

  ✓

  Real-Time Monitoring through IoT: DRAMS incorporates IoT sensors to capture real-time operational information from assets, such as temperature, vibration and wear levels. These data are continuously collected and analyzed to identify potential problems before they cause operational failure, allowing proactive intervention and reduced downtime [54]. By anticipating and planning necessary interventions, equipment reliability and availability are improved, contributing to a consistent service experience aligned with customer expectations.

  ✓

  Predictive Analytics with AI: AI and data analytics play a central role in DRAMS by analyzing historical and current patterns to anticipate failures and optimize maintenance plans. Machine learning algorithms process large volumes of operational data to identify wear patterns and specific risk factors. This predictive analysis helps companies schedule maintenance interventions at the most opportune time, maximizing availability and minimizing costs associated with emergency interventions [55].

  ✓

  Using Digital Twins for Simulation and Optimization: Digital twins allow the operating conditions of assets to be simulated and how they will respond to various operation and maintenance scenarios to be predicted. This simulation capability helps optimize warranty policies, allowing dynamic adjustments based on the asset’s current state and usage conditions. Rolls-Royce, for example, has employed digital twins to monitor and extend the life of its aircraft engines, reducing maintenance costs and improving operational safety [55].

DRAMS’ advanced capabilities not only allow assets to remain operational at optimal levels, but also extend their useful life and optimize their performance. This improvement translates into tangible benefits, such as reduced maintenance costs and improved operational efficiency. In addition, the early identification of potential problems allows unforeseen incidents to be minimized, which is essential to increase customers’ satisfaction and strengthen their confidence in aftersales service. In an environment where customers are increasingly demanding reliable and personalized services, DRAMS represents an essential component of the transition to a proactive warranty management model [56]. This proactive approach is based on real operational data, ensuring more accurate warranty management tailored to each customer’s specific needs. By reducing the incidence of failures and unplanned downtime, companies can offer an aftersales service that exceeds expectations, reinforcing customer loyalty and differentiating themselves in the market.

• Phase 7: Life-Cycle Cost Analysis. Life-cycle cost analysis provides an understanding of the total costs associated with operating, maintaining, and guaranteeing an asset over its useful life. This phase is supported by financial modelling tools that integrate historical and projected data, providing a comprehensive view of costs throughout the life cycle [44]. This approach allows warranty policies to be adjusted to reflect actual maintenance and repair costs, optimizing resource allocation.
• Phase 8: Electronic Warranty and Customer Relationship Management (e-Warranty and CRM). This final phase combines e-Warranty and CRM into one comprehensive system, where the digitalization of warranties facilitates the automated tracking of asset status and warranty claims. By using advanced technologies, such as blockchain and artificial intelligence (AI), the organization can offer a more agile, secure, and transparent warranty service, eliminating inefficiencies and preventing fraud [57]. At the same time, the CRM system allows data to be collected and analyzed on product usage and customer preferences, generating insights that enable a personalized offer of aftersales services [46].

3.2. Implementation and Development of the Presented Methodology

The implementation of this methodology involves the deployment of digital enablers corresponding to each phase. These enablers are specifically selected to align warranty management practices with the organization’s strategic objectives. The project will require a collaborative approach between IT, operations, and customer service departments, as well as a robust infrastructure that allows the integration of the aforementioned technologies. Depending on the specific objectives of each phase, AI technologies are organized into various categories, ranging from machine learning models to continuous improvement tools. Choosing these categories represents an essential step for improving system functionality and efficiency specifically within warranty management. Every category of the model provides unique operational and management capabilities that optimize different model stages.

(a)

Machine learning models that contain artificial neural networks (ANNs), SVMs, and Decision Trees help systems extract information from past data and current data for classification and prediction tasks.

(b)

Data Mining and Association Rules, together with other data analysis techniques, provide capabilities to identify concealed patterns within extensive datasets, thus aiding root failure cause identification and warranty policy optimization.

(c)

The visualization tools alongside analytical tools assist organizations in tracking KPIs to support better decision-making that avoids strategic objective misalignment.

(d)

NLP (natural language processing) analyses customer feedback to help businesses enhance satisfaction levels, as well as enhance process operations.

(e)

The categories within this section ensure systems run efficiently to maintain the quality standards needed for optimal performance.

(f)

System quality and performance standards need maintenance to deliver high satisfaction levels to customers.

(g)

The life-cycle management approach optimizes performance alongside cost reduction for all product stages, especially in the Warranty Policy Risk–Cost–Benefit Analysis phase.

(h)

The system receives constant upgrades through collaborative learning, which keeps it active and oriented toward customer needs.

(i)

Utilizing optimization models allows companies to enhance asset performance alongside warranty policy execution, through which they lower their costs while maximizing their efficiency.

(j)

This category involves ongoing process adjustment according to altering circumstances, which enables businesses to preserve their market competitiveness in changing conditions.

Table 2. Distribution of each technology in the phases of the warranty management model.

Category	Phase 1: BSC	Phase 2: CA	Phase 3: FRCA	Phase 4: RCM	Phase 5: RCBA	Phase 6: RAMS	Phase 7: LCCA	Phase 8: eW and CRM
(a) Machine Learning Models	X	X	X	X	X	X	X	X
(b) Data Analysis Techniques	X	X	X		X		X
(c) Predictive and Analytical Tools	X	X	X		X	X	X
(d) LLM/NLP			X	X				X
(e) Monitoring and Control	X			X			X
(f) Performance and Quality					X	X	X
(g) Life-Cycle Management							X
(h) Collaborative Learning								X
(i) Optimization Models			X	X	X
(j) Continuous Improvement							X

proposes a distribution of each technology in the phases of the warranty management model.

Each category of digital enablers has been selected based on its ability to meet the specific operational and management needs of each phase of the model, thereby optimizing the functionality of warranty management. The objectives of each phase and the technologies selected to achieve these objectives are detailed below. In Phase 1, focused on the Balanced Scorecard (BSC), the objective is to monitor and improve the performance of warranty management KPIs, aligning them with the organization’s strategic objectives. Enablers such as machine learning models, data analysis techniques, predictive and analytical tools, natural language processing (NLP), and monitoring and control are used. Tools such as Power BI and Tableau facilitate the visualization of KPIs, and predictive models anticipate performance variations. In addition, NLP allows the analysis of customer feedback, which helps improve satisfaction and adjust processes. Phase 2, called Criticality Analysis, aims to identify critical assets by analyzing their operational relevance and impact on the system. Machine learning models and data analysis techniques are used to do this. Machine learning algorithms assess the status of assets based on historical data and real-time conditions, while tools such as RapidMiner and KNIME allow large volumes of data to be analyzed, generating a criticality matrix that prioritizes maintenance interventions. In Phase 3, known as Failure Root Cause Analysis, the goal is to determine the causes of recurring failures and adjust maintenance policies. Here, the use of LLM/NLP and machine learning models is key. Tools such as spaCy and BERT analyze textual descriptions of failures, identifying common patterns in maintenance reports, which increases the accuracy of corrective maintenance and reduces the recurrence of incidents. Phase 4 focuses on the use of Warranty-Adapted Maintenance Design Tools. Its goal is to adapt maintenance strategies to the current conditions of assets, thus optimizing resources. Enablers such as predictive models and real-time monitoring allow for continuous adjustments to maintenance tasks. Machine learning tools such as Keras and PyTorch allow for real-time adaptation, reducing unnecessary interventions and extending asset life.

In Phase 5, Warranty Policy Risk–Cost–Benefit Analysis, the aim is to evaluate warranty policies through risk and cost simulations. Digital twins and optimization models play a crucial role. These digital twins allow simulating the impact of different warranty policies in various scenarios, while tools such as AnyLogic and IBM CPLEX facilitate these simulations, optimizing the return on investment and minimizing risks. Phase 6, focused on Reliability, Availability, Maintainability, and Safety (RAMS), aims to ensure the availability and safety of assets throughout their life cycle. Machine learning and predictive models are used to achieve this. Tools such as IBM Maximo and Infor EAM enable the continuous monitoring of critical variables, optimizing operational availability and reliability and ensuring that the service meets high safety standards. Phase 7 focuses on life-cycle cost analysis, which aims to assess cumulative costs over the asset’s life cycle. Data analytics techniques and life-cycle cost models assist in this phase. Tools such as IBM SPSS Modeler and Tableau facilitate the detailed analysis of operation and maintenance costs, allowing for adjustments to warranty policies and optimizing financial sustainability. Finally, Phase 8, e-Warranty and Customer Relationship Management (CRM), seeks to improve the customer experience through a digital warranty platform and proactive relationship management. Enablers include continuous improvement tools, user feedback, NLP, and collaborative learning. Tools such as Salesforce Einstein Analytics and Zoho CRM collect real-time data on the customer experience, personalizing care and tailoring warranty policies to their needs, maximizing customer satisfaction and loyalty. To illustrate how each technology can be implemented in the warranty management model,

Table 3. AI tools and application examples.

Category	AI Methods	Examples of Specific Tools
Machine Learning Models	ANNs, SVMs, Decision Trees, Predictive Models, Reinforcement Learning, Deep Learning, Association Rule Mining, Bayesian Networks	TensorFlow, Scikit-learn, PyTorch, Keras, XGBoost, AlphaGo
Data Analysis Techniques	Data Mining Techniques, Association Rules	RapidMiner, KNIME Analytics Platform, Orange Data Mining
Predictive and Analytical Tools	Predictive Analysis, Optimization Algorithms, Simulation, Digital Twins	IBM SPSS Modeler, MATLAB, AnyLogic Simulation Software
LLM/NLP	Large Language Models (LLMs)/Natural Language Processing (NLP)	Natural Language Toolkit (NLTK), spaCy, BERT Model
Monitoring and Control	Real-Time Monitoring and Control	Apache Kafka, Prometheus Monitoring System
Performance and Quality Analysis	Performance Analysis, Quality Control	JProfiler, Apache JMeter
Life-Cycle Management	Asset Health Indexing, Life-Cycle Cost Analysis	IBM Maximo Asset Management, Infor EAM
Collaborative Learning	Collaborative Learning	Google Colab, Microsoft Azure Notebooks
Optimization Models	Optimization Models	Gurobi Optimization, IBM CPLEX
Continuous Improvement	Feedback Loop for Continuous Improvement	Salesforce Einstein Analytics

presents a list of specific AI methods and tools applicable in each category.

To understand how digital enablers help optimize a warranty management model based on AI and other advanced technologies, Table 3 organizes the tools into key categories. Each category groups specific methods and examples that enhance a phase of the model, enabling a transition to a more predictive, proactive, and customer-centric system. The relevance of each category and its application within the warranty management framework is explained below:

• Machine Learning Models. Machine learning models encompass a variety of techniques that enable systems to learn from historical and real-time data. Artificial neural networks (ANNs), SVMs (Support Vector Machines), and Decision Trees are used in classification and prediction tasks. Reinforcement Learning and Deep Learning provide advanced capabilities for optimizing asset performance in complex environments. Tools such as TensorFlow, Scikit-learn, and Keras facilitate the development of these models in predictive maintenance and failure pattern analysis applications. For example, in the Criticality Analysis phase, these models help prioritize maintenance of the most critical assets.
• Data Analysis Techniques. These techniques include Data Mining and Association Rules, which enable hidden relationships and patterns to be discovered in large volumes of historical data. Tools such as RapidMiner and KNIME Analytics Platform enable warranty managers to analyze complex datasets, helping to identify root causes of failures and optimize warranty policies. This is crucial in phases such as Failure Root Cause Analysis, where specific factors leading to recurring failures need to be identified.
• Predictive and Analytical Tools. In warranty management, the use of Predictive Analytics and digital twins is essential to anticipate possible failures and simulate scenarios. Tools such as IBM SPSS Modeler and AnyLogic allow the simulation of warranty policies, evaluating their impacts on profitability and cost. Digital twins, which digitally replicate the status of assets, are useful in the Warranty Policy Risk–Cost–Benefit Analysis phase, as they facilitate the simulation of cost–risk scenarios in warranty policies.
• Natural Language Processing (NLP) is essential for interpreting and analyzing textual customer feedback and crash reports. Tools such as spaCy and the BERT Model allow for extracting useful information from large volumes of unstructured data. In the context of customer relationship management (CRM), NLP helps manage customer relationships by personalizing the response according to their experiences and reported problems.
• Real-Time Monitoring and Control. These tools enable the real-time monitoring of asset performance and condition, which is crucial for immediate response to failures. Apache Kafka and Prometheus are used to collect and analyze sensor data, supporting the Reliability, Availability, Maintainability, and Safety (RAMS) phase by ensuring assets maintain an optimal level of operation.
• Performance and Quality Analysis. Systems such as JProfiler and Apache JMeter are used to assess the performance of the warranty management model and monitor quality at each phase of the asset life cycle. These enablers are essential to track KPIs and ensure the quality of assets in service.
• Life-Cycle Management. In the Life-Cycle Cost Analysis phase, asset life-cycle management systems, such as IBM Maximo and Infor EAM, are used to plan and manage the costs associated with component maintenance and replacement. These tools provide a comprehensive view of cumulative costs, which is critical to optimizing asset profitability and financial sustainability.
• Collaborative Learning. Google Colab and Microsoft Azure Notebooks are platforms that facilitate collaborative learning, allowing teams from different disciplines to work together on prediction and optimization models. This collaboration is essential in the context of continuous improvement, where it is necessary to integrate new approaches to warranty management.
• Optimization Models. Optimization models, implemented using tools such as Gurobi Optimization and IBM CPLEX, allow for maximizing efficiency and minimizing costs in the warranty model. These models are especially useful in the Warranty Policy Risk–Cost–Benefit Analysis phase, where warranty policies are evaluated based on return on investment and operating cost.
• Continuous Improvement. Continuous analysis tools such as Salesforce Einstein Analytics facilitate constant feedback on warranty policies, ensuring that they adapt to changes in the market and customer expectations. These tools are integrated into the e-Warranty phase, where a constant update of warranty policies is sought to maximize customer satisfaction and loyalty.

To evaluate the impact of these technologies, the following key performance indicators (KPIs) are defined, as presented in

Table 4. KPI table.

Key Indicator	Description	Expected Impact
Reduction in downtime	Downtime avoided thanks to predictive maintenance.	Greater asset availability and operational continuity.
Optimization of operational costs	Savings in maintenance costs through automation and failure prediction.	Cost reduction and increased profitability in asset management.
Customer satisfaction	Aftersales satisfaction level measured through customer surveys.	Improved perception of warranty service and increased customer loyalty.
Compliance with standards	Alignment with quality standards such as ISO 55000, 42001, and 9001.	Guaranteeing safety, quality, and efficiency throughout the asset life cycle.
DRAMS	Reliability, availability, maintainability, and digital security.	Reducing downtime, optimizing operating costs, and complying with safety standards.

These indicators quantify the results of the integration of AI, IoT, and digital twins in each phase of the model, guiding decisions towards efficiency and customer satisfaction.

.

3.3. Industry Best Practices for Digitalized Warranty Management

In this section, we explore the best practices applied by large companies in each phase of the digitalized warranty management model according to the framework proposed by [1], which adapts the originally developed eight-phase model [41]. In other words, leading companies’ practices will be utilized to establish best practices for the improvement of successful warranty management strategies, operational enhancement, and customer satisfaction. The approach keeps the established framework in harmony with industry guidelines and emerging technological advancements. Particularly, this analysis focuses on BMW, Rolls-Royce, and General Electric practices, and how the Internet of Things (IoT), artificial intelligence (AI), Computer Aided Maintenance Management (CMMS) systems, and digital twins have transformed their warranty management practices towards a more predictive and proactive approach.

The implementation of the Balanced Scorecard (BSC) strategy at BMW seeks to evaluate warranty performance in real time and optimize aftersales management by aligning customer satisfaction metrics with the company’s strategic objectives. To undertake this, BMW uses technologies such as machine learning and BI tools, including Tableau and Power BI, integrated into its AI-based CRM system, which allows for real-time data analysis and personalized service based on customer history [58]. This strategy has allowed them to improve efficiency in resolving claims by 15% and increase customer satisfaction thanks to a fast and personalized service [59]. However, the high cost of infrastructure represents a barrier to its implementation in emerging markets. In the Criticality Analysis phase implemented by Caterpillar, the objective is to prioritize critical assets to optimize resources and reduce the risk of operational failures. To undertake this, Caterpillar uses IoT sensors and machine learning algorithms in its Cat^® Connect Technology platform, which allows real-time monitoring of the status of assets [33]. This has achieved a 20% reduction in maintenance costs and extended the useful life of critical assets. However, the integration of multiple sensors and lack of connectivity in remote areas complicate predictive monitoring in certain regions [60]. For Failure Root Cause Analysis, Siemens employs its Railigent platform, which uses AI and natural language processing (NLP) to analyze large volumes of data from IoT sensors, identifying failure patterns that enable preventative interventions [26]. This approach has succeeded in reducing maintenance time by 50% and increasing train availability by 10% [61]. However, complying with international regulations and training staff to handle complex data poses additional challenges. In the aeronautical field, Rolls-Royce adjusts its maintenance policies in the Maintenance Design Tools Adapted to Warranty phase, using digital twins and AI algorithms in its TotalCare program. This technology allows real-time performance monitoring and predicting the future state of engines, which improves service reliability by reducing claim resolution times by 30% and increasing engine availability [62]. However, the high cost of digital twins limits their use to high-value assets, such as aircraft engines. General Electric implements the Warranty Policy Risk–Cost–Benefit Analysis phase, using digital twins to monitor gas turbines in real time and evaluate warranty policies through risk and cost simulations [27]. This has allowed the company to reduce maintenance costs by 25% and optimize asset performance [28]. However, digital twin models require constant investments to adapt to different assets and operating conditions. In the Reliability, Availability, Maintainability, and Safety (RAMS) phase, Siemens ensures the reliability and safety of its railway assets with its Railigent platform, which combines AI, computer-aided maintenance management (CMMS), and IoT sensors [26]. This platform facilitates predictive maintenance, which increases train availability and reduces unforeseen maintenance risks [26]. Training staff in interpreting the data generated is essential to maximize the effectiveness of this solution. Caterpillar, in the Life-Cycle Cost Analysis phase, optimizes the life-cycle costs of its assets through the use of a CMMS system and real-time data analysis. This approach allows maintenance policies to be adjusted according to the specific needs of the equipment, extending the useful life of the assets and reducing cumulative costs throughout the life cycle [28]. Limited connectivity in remote areas poses a challenge for continuous monitoring. Finally, in the e-Warranty and Customer Relationship Management (CRM) phase, Rolls-Royce employs digital twins in its e-Warranty system to monitor engines in real time and adjust warranty policies, thus personalizing aftersales service [62]. BMW also uses AI in its CRM to analyze customer behavior, allowing for personalized aftersales service and improved loyalty. This strategy has improved efficiency and transparency in warranty management, increasing customer satisfaction [39]. However, the high costs of digital twins and AI limit their adoption in lower-value assets and in emerging markets, where infrastructure and financial resources are more limited [59]. This analysis shows how the use of AI, IoT, digital twins, and CMMS systems optimize both operational efficiency and customer satisfaction in critical sectors and high-value assets. The cases of Rolls-Royce and General Electric stand out for their ability to reduce costs and improve operational availability through the use of digital twins, while Siemens and BMW underline the importance of AI and IoT in personalizing services and reducing failures in customer-experience-oriented sectors [3,25]. However, the success of these implementations depends on factors such as technological infrastructure, budget, and specialized training [58], as demonstrated by Rolls-Royce (2023) [62].

Below is a matrix table (

Table 5. Matrix of digital enablers in the aftersales and warranty management framework.

Warranty Management Framework Phase	Specific Objective of the Phase	Digital Enablers	Specific Technologies and Tools	Best Industrial Practice	Main Benefit
Phase 1: Balanced Scorecard	Monitor and improve the performance of KPIs in warranty management, aligning them with the strategic objectives of the organization	Machine learning models (classification and regression); data analysis techniques	Power BI, Tableau, machine learning algorithms	BMW: Uses machine learning and BI tools like Tableau and Power BI in its AI-powered CRM system	Improved decision-making through real-time visualization of KPIs, allowing for an agile and proactive response.
Phase 2: Criticality Analysis	Identify and prioritize critical assets to optimize resource allocation	Machine learning (ML), IoT	IoT sensors, Decision Trees, Support Vector Machines (SVM), KNIME, RapidMiner	Caterpillar: Using IoT and ML to monitor critical assets and optimize interventions in heavy machinery	Improves resource allocation, reducing interventions in non-critical assets and decreasing maintenance costs by 20%.
Phase 3: Failure Root Cause Analysis	Determine patterns and root causes of critical failures	AI, NLP	Bayesian networks, supervised learning, spaCy, NLTK, BERT	Siemens: Railigent platform with AI and NLP for pattern analysis and root cause of failure analysis in rail transport	Reduction in recurring failures and maintenance times by 50%, improving the availability and reliability of the railway system.
Phase 4: Maintenance Design Tools Adapted to Warranty	Dynamically adjust maintenance design based on operating conditions	Predictive models, IoT	ML models for prediction, Keras, PyTorch, real-time sensors	Rolls-Royce: TotalCare with digital twins to monitor and optimize aircraft engine maintenance	Increased engine availability and reduced downtime by adjusting maintenance interventions and reducing costs.
Phase 5: Warranty Policy Risk–Cost–Benefit Analysis	Evaluate the profitability and risk of guarantee policies through simulations	Digital twins, simulation, AI	IBM SPSS, AnyLogic, MATLAB, Gurobi Optimization	General Electric: Using digital twins to evaluate warranty policies in gas turbines	Alignment of warranty policies with actual asset performance, reducing maintenance costs by 25% and increasing profitability.
Phase 6: RAMS (Reliability, Availability, Maintainability, and Safety)	Ensuring the reliability and availability of assets throughout their life cycle	Digital twins, IoT, ML	IBM Maximo, MATLAB, Apache Kafka, Scikit-learn	Siemens: Railigent to maximize train availability through monitoring and predictive maintenance	Significant increase in train availability and reduction in operational risks, optimizing the safety and efficiency of rail transport.
Phase 7: Life-Cycle Cost Analysis (LCCA)	Optimizing asset life-cycle costs	LCCA, data analytics	IBM SPSS Modeler, Tableau, Infor EAM, SAP PM	Caterpillar: Life-cycle management with CMMS for heavy machinery	Extending equipment life, reducing cumulative costs, and optimizing resources through data-driven maintenance decisions.
Phase 8: e-Warranty and Customer Relationship Management (CRM)	Implement a continuous improvement system in warranty policies, adapting them to the actual behavior of assets and improving customer relations through a proactive and personalized aftersales service	Six Sigma, continuous improvement, AI, CRM, NLP	Salesforce Einstein Analytics, Minitab, Dynamics 365, ICT for data privacy and transparency, Zoho CRM, BERT Model, spaCy	Rolls-Royce: e-Warranty with digital twins to adapt in real time to operating conditions; BMW: Advanced CRM with AI to personalize and improve aftersales service	Dynamic adjustment of warranty policies, improving transparency and customer satisfaction through faster and more personalized responses. Increased customer satisfaction through a personalized aftersales experience and reduced response times, resulting in greater loyalty.
General Enablers for All Phases	Flexibility and adaptability in each phase of the model, promoting the digitalization of warranty management	AI, IoT, machine learning, data analytics, NLP	TensorFlow, Scikit-learn, Tableau, Apache Kafka, Prometheus Monitoring System		Digital transformation of warranty management, increasing operational efficiency and optimizing resources and operating costs.
Key Benefits of the Management Framework	Reduction in operating costs, improvement in customer satisfaction, optimization of asset life cycle, anticipation of failures

) containing the phases of the warranty management framework, digital enablers, tools and technologies, and industry best practices. Each phase is illustrated with an example of a relevant company that has implemented these technologies to improve its warranty management. This provides a comprehensive view of the model applied in the industry.

Each column refers to the following aspects:

• Phase and Objective: Each phase of the model focuses on a specific objective, aligned with the challenges and needs of digitalized and optimized warranty management.
• Digital Enablers: Digital enablers applied at each stage, such as machine learning, IoT, NLP, and digital twins, enable a predictive and personalized approach, improving the efficiency of warranty management.
• Specific Technologies and Tools: At each stage, specialized tools and technologies such as IBM Maximo, AnyLogic, and Salesforce have been selected that integrate AI and data analysis to facilitate the monitoring, simulation, and personalization of aftersales services.
• Best Industrial Practice: Leading companies at every stage have implemented these enablers and technologies to improve their warranty management processes, such as Caterpillar in critical asset identification [28], Siemens in root cause analysis, and Rolls-Royce in adaptive maintenance [26]. Each example illustrates how these advancements can reduce costs, increase asset availability, or improve customer satisfaction.
• Main Benefit: The benefit of each phase is specific, but focuses on the optimization of resources, the reduction in unplanned interventions, the extension of the life cycle of assets, and improving the relationship with the client, achieving a more proactive and efficient management of guarantees.

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 aftersales 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 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. 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 aftersales 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.
• Aeronautics Industry: Servitization and Performance-Based Warranty Models: In the aeronautics sector, warranty management is increasingly driven by servitization and performance-based contracts. Companies like Rolls-Royce utilize “Power-by-the-Hour” service agreements, wherein airlines pay based on engine performance rather than ownership. This model aligns with digital twin applications, enabling the real-time tracking of aircraft engines and facilitating predictive maintenance to prevent failures. Such warranty frameworks not only reduce operational costs but also ensure asset availability, a critical factor in aviation. Additionally, the stringent regulatory environment in aeronautics necessitates the integration of ISO 55000-based asset management principles within warranty policies, ensuring compliance with industry standards.

While all three industries employ predictive analytics and digital transformation to enhance warranty management, their strategies differ based on asset criticality, operational complexity, and customer engagement models. The automotive sector prioritizes customer-centric warranty solutions through predictive diagnostics and extended service contracts. The railway industry focuses on life-cycle cost optimization using digital twins, while aeronautics emphasizes servitization to maximize asset availability and regulatory compliance. These distinct approaches underscore the industry-specific adaptations of advanced technologies in warranty management, demonstrating a shift from reactive to predictive and proactive frameworks.

While not designed as a critical comparative analysis of field-tested case studies, the value of this paper lies in providing a unified taxonomy and implementation framework, which can support future empirical studies and benchmarking efforts. As a critical discussion, it is worth mentioning that the implementation of AI, IoT, and digital twins in warranty management presents both benefits and challenges. The main limitations identified are discussed below and solutions are suggested to mitigate these problems. In other words, the implementation of advanced technologies in asset management involves a significant initial investment in infrastructure, sensors, and the development of custom algorithms. According to [64], those companies that manage to overcome this barrier gain a competitive advantage by reducing their costs in the medium and long term. As a solution, the gradual implementation of these technologies is recommended, starting with areas of high initial impact and expanding to other areas as measurable benefits are obtained, thus optimizing the return on investment. Furthermore, the adoption of these technologies requires staff training and adaptation, as they require skills in data analysis and predictive maintenance techniques. Ref. [65] emphasize that investment in staff training is key to maximizing the potential of artificial intelligence and preventing internal resistance. Therefore, it is recommended to establish continuous training programs and collaborate with technology providers to develop and strengthen the digital skills of teams. Integration with legacy systems is another critical challenge, as interoperability between traditional systems and new technologies can be complex due to compatibility issues and data migration. Ref. [66] suggests a gradual transition through the use of compatible platforms that facilitate smooth integration. To this end, an effective solution is the adoption of hybrid solutions, which allow a gradual integration of new systems with traditional ones, thus minimizing operational risks during the process.

Another important challenge is data security and privacy, as real-time data collection and analysis increase cybersecurity risks, meaning that compliance with privacy regulations is paramount. While digital transformation enhances efficiency, predictive accuracy, and cost-effectiveness in warranty management, it also introduces significant risks. Cybersecurity vulnerabilities pose a major concern, as warranty management systems increasingly rely on IoT-connected assets and AI-driven analytics, making them potential targets for data breaches and cyberattacks. High initial costs associated with implementing AI models, IoT infrastructure, and digital twin platforms can be a barrier, especially for small- and mid-sized enterprises. Additionally, workforce adaptation challenges arise as organizations must train employees in AI-based diagnostics, predictive analytics, and automated warranty processing, requiring a cultural and technical shift. Addressing these risks necessitates robust cybersecurity frameworks, phased implementation strategies to optimize return on investment, and ongoing workforce upskilling programs to ensure successful digital adoption in warranty management. Ref. [67] warns about the risks inherent in digital transformation and highlights the importance of implementing robust cybersecurity measures. In this context, it is essential to adopt advanced security protocols, comply with regulations of standards such as ISO 42001, and ensure ethical data management to protect both the company and customers. The comparison between traditional warranty management and management based on artificial intelligence and digital twins reveals clear advantages of the digital approach. Firstly, in terms of downtime, traditional maintenance is reactive, while predictive maintenance allows interventions to be scheduled before failures occur, improving availability and operational continuity. Secondly, resource optimization is significantly more effective with artificial intelligence, as traditional methods require more corrective interventions, while AI allows for precise interventions, thus reducing maintenance costs.

The digital approach improves customer satisfaction. While traditional warranties are often standardized, the use of AI allows for a personalized service according to the individual needs of each customer, increasing the positive perception of aftersales service and strengthening customer loyalty. In terms of future opportunities and innovative potential, the integration of AI and digital twins in warranty management represents an opportunity to innovate and add value in the industry. The structured methodology for digital warranty management incorporates predictive analytics models to enhance decision-making. Bayesian networks are employed to model probabilistic dependencies between failure events, enabling risk assessment under uncertain conditions. Machine learning classification models, such as Support Vector Machines (SVM) and Random Forests, are used to categorize defect patterns and optimize warranty claim processing [45]. Additionally, cost–risk optimization functions, including stochastic programming and mixed-integer linear programming (MILP), facilitate warranty policy adjustments by balancing service costs against asset reliability. While this framework provides a structured approach, future research should focus on empirical validation through field studies and pilot implementations in real-world warranty operations. Industry collaborations could test the effectiveness of AI-driven warranty models across sectors such as the automotive, railway, and aeronautics industries, refining model accuracy and optimizing life-cycle cost predictions [59]. Such studies would provide quantifiable performance metrics, reinforcing the practical applicability and scalability of this digital transformation framework.

In the future, developments in these technologies will enable adaptive business models that incorporate real-time life-cycle data, allowing adjustable warranties to be offered based on the actual use of the asset. Future research should focus on the implementation of regulations and best practices that mitigate risks and promote transparency in the handling of sensitive data [35]. Another possible line of research is to develop continuous improvement models for AI and servitization. The advancement of AI models that reduce bias and improve accuracy represents an opportunity to implement continuous improvement processes in servitization, dynamically adjusting warranty policies based on changing operating conditions [55]. Ultimately, digital transformation applied to warranty management, and servitization in particular, offers a promising path towards resource optimization, improved customer experience, and reduced operating costs [68]. The progressive and ethical implementation of these technologies is essential, as is the adoption of measures that mitigate risks [51]. Companies that adopt these technologies will be better positioned to compete in a dynamic market, aligning themselves with the principles of Industry 4.0 and preparing for Industry 5.0, which will focus on personalization and collaborative interaction between humans and technology, offering increasingly adaptive and personalized aftersales services [67]. Below is a SWOT analysis (

Table 6. SWOT.

SWOT Analysis	Description
Strengths	Reduction in operating costs, personalization of aftersales service, and optimization of the life cycle.
Weaknesses	High initial costs and need for specialized training.
Opportunities	Creation of new business models, such as adaptive and adjustable guarantees based on use.
Threats	Real-time data security challenges and interoperability with legacy systems.

) that examines the strengths, weaknesses, opportunities, and threats of the implementation.

From here it can be seen that companies that implement AI and digital twins in warranty management will be able to significantly differentiate themselves in the market, offering highly personalized aftersales services. This digital transformation not only improves operational efficiency, but positions companies to compete in Industry 4.0 and evolve towards Industry 5.0, where the interaction between humans and advanced technologies will be key to innovative and adaptive customer service. This innovative framework enables companies in the industrial sector to respond to the growing demands for efficiency and adaptability in a digitalized environment.

• Reduction in Failures and Operating Costs: The application of AI in predictive maintenance, combined with data analysis provided by IoT, makes it possible to anticipate failures and adjust interventions based on patterns identified in real time. This reduces corrective interventions, minimizes downtime, and optimizes resources, resulting in substantial savings for companies [55]. For example, General Electric has managed to reduce maintenance costs by 25% by adopting digital twins, extending the life of critical assets and reducing downtime.
• Optimizing Warranty Policies Warranty through Servitization: The adoption of e-Warranty and CMMS platforms facilitates more agile and transparent warranty management. Servitization allows warranty policies to be dynamically adapted according to the operational status and actual use of the asset, generating a more satisfactory aftersales experience aligned with customer needs. This approach responds to the trend of offering personalized services, which contribute to customer loyalty [59].
• Improving Customer Satisfaction through Proactive Servitization: Personalized warranty management increases customer satisfaction by providing a proactive, tailored service experience. Companies like BMW and Rolls-Royce have shown that transparency and the ability to anticipate problems through AI and digital twins create a positive experience, fostering loyalty and satisfaction [52].

To maximize the benefits of this digital transformation in warranty management, recommendations are proposed such as extending the implementation to new sectors. That is, critical sectors such as energy and advanced manufacturing can benefit from the servitization and progressive digitalization of their warranty models. AI and CMMS allow for the adoption of more efficient predictive and operational models, maximizing the return on investment in warranty management [67]. Furthermore, continuous training and education in digital enablers is crucial. Since these technologies require personnel trained in data analysis and digital management, it is essential to invest in continuous training to optimize the use of digital enablers and ensure the adaptation of personnel to these new service models [55]. Another recommendation that is proposed aims to ensure compliance with data security and ethics: The collection of large volumes of data poses challenges in terms of security and privacy. It is essential to adopt robust cybersecurity and data ethics policies to maintain customer trust and comply with regulations such as ISO/IEC 42001, ensuring the integrity and proper use of personal and operational data [31,64]. Although this work provides a robust framework for understanding the benefits of digitalization in warranty management, there are still areas of research that can expand these findings. For example, in terms of exploring new applications in emerging sectors, the proposed framework could be applied in emerging sectors such as healthcare and public transport, where servitization and critical asset management can significantly improve operational efficiency and customer satisfaction [66]. Similarly, it would be beneficial to delve deeper into the ethical and privacy aspects of AI. That is, the intensive use of data makes ethical and transparent management essential.

5. Conclusions

This paper reviews how the implementation of advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and Computer Aided Maintenance Management (CMMS) systems can transform warranty management, migrating from a reactive model to a predictive and proactive one. The integration of these digital enablers allows for the optimization of maintenance policies, anticipating failures and reducing operating costs in industries with highly critical assets [61,63]. The introduction of servitization in warranty management also contributes to extending the relationship with the customer, offering a personalized and adapted aftersales service that not only optimizes the life cycle of assets, but also strengthens customer loyalty. The analysis shows that integrating advanced technologies such as AI and IoT into warranty management offers concrete benefits, including cost reduction, increased customer satisfaction, and asset life-cycle optimization.

The goals presented in the introduction have been fulfilled through systematic analysis. Objective 1 (O1) was achieved by a thorough examination of the literature, identifying and assessing pertinent technologies, including AI, IoT, and CMMS, while demonstrating their use in predictive warranty management within different manufacturing settings. The analysis of case studies fulfilled Objective 2 (O2) by exhibiting how advanced technologies created operational cost reductions and process acceleration alongside increased customer satisfaction. Multiple case studies confirmed how businesses use AI and IoT technologies to drive better warranty management operations and enhance their customer experience. For Objective 3 (O3), the assessment focused on digital enabler integration within warranty management systems that follow ISO55000 and the standard practices of ISO 9001 and ISO 42001. The assessment evaluated real-world implementations across major companies in the aviation, automotive, and railway industries to demonstrate how these technologies result in sector-focused advantages, along with improved operational results. Future work will aim to validate the full framework through longitudinal studies or pilot programs within companies.