## 5 Discussion of Results, Practical Implications, and Future Research  
This section discusses the results of this paper, focusing on the contributions of the model-based approach to asset digitalization and management. It emphasizes the integration with digital maintenance decision-making processes, the application of IoT monitoring and model integration, and the broader implications for the advancement of digitization practices. * Introduction of Model-Based Approach for Asset Digitalization: Our research addresses the pressing need for a comprehensive model-based framework to facilitate the digitalization and management of assets, particularly those with low initial levels of digitization during their operational and maintenance (O&M) phase. A structured approach encompassing four tailored asset models is provided, offering practical guidance on how organizations can effectively digitize their assets to serve maintenance and asset management purposes. * Integration with Digital Maintenance Decision-Making Processes: One of the primary contributions of our work lies in the seamless integration of the proposed model-based approach with digital maintenance decision-making processes. The paper delineates how organizations can incorporate digital asset management practices into their existing workflows. This integration enhances the efficiency and effectiveness of maintenance operations, leading to improved asset performance and reduced operational costs. * Application of IoT Monitoring and Model Integration: Our research explores the combined use of an IoT monitoring platform with short-term monitoring models, such as Condition-Based Maintenance (CBM), and long-term models, such as the Asset Health Index (AHI), to enhance asset digitalization and management. Leveraging IoT technologies and integrating diverse data sources are important for organizations to gain real-time insights into asset health and performance. This integrated approach enables proactive maintenance strategies, optimizing resource allocation and minimizing downtime. * The incorporation of standards such as IEC 81346-1:2022 and ISO 14224:2016 ensures compatibility and interoperability with existing systems, setting our framework apart from previous studies. * Scalability and Adaptability: The scalability and adaptability of the framework enable its application across diverse industries and organizational settings. Future research could explore strategies for further enhancing the scalability and adaptability of the framework to accommodate evolving technological and operational requirements. * Bridging the Gap between Theory and Practice: We provide practical guidance for implementing digital asset management strategies. Continued collaboration with multidisciplinary groups and industry partners could further enhance the relevance and applicability of our findings in real-world settings. * Overcoming Implementation Challenges: The framework addresses data integration using a microservice-based design that efficiently handles large volumes of heterogeneous data from distributed sensors. However, its application in complex environments requires enhanced cybersecurity measures, such as end-to-end encryption and intrusion detection mechanisms. Additionally, processing algorithms can be further optimized to manage real-time constraints in critical systems. * Framework Generalization: The modular and scalable structure of the framework allows its adaptation to assets with diverse lifecycles and operational complexities. While the case study focuses on infrastructure such as bridges, the framework's design can be extended to industrial and energy assets by tailoring the models to the specific requirements of each sector. This adaptability ensures broad applicability and maximizes the framework's impact across varied contexts. * Algorithm and Model Generalization: In this example, the AHI model is very similar to the ones proposed in the CNAIM [42].