# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 5 Discussion
This prioritization optimizes resource allocation and improves operational efficiency by reducing unplanned downtime by 25%. Digitalization enables greater adaptability and agile response to changes in the network and operating environment, something that traditional methods cannot match due to their rigidity. Simulating failure scenarios and planning proactive interventions are other significant advantages, as they reduce service interruptions and improve availability. Finally, interoperability and data integration in a unified framework facilitates a more complete and coordinated view of the network, something that traditional methods, with their partitioned systems, cannot provide. In short, using data models outperforms conventional methods in accuracy, efficiency, and responsiveness, significantly improving asset management. The study has several limitations that could be addressed in future research to improve the implementation and effectiveness of the data model in railway asset management. For example:  
* Complexity and heterogeneity of railway networks complicate data integration and application of the criticality model. * Risk of information overload due to the large amount of data available. * Advanced data filtering and analysis techniques are needed to prioritize critical information. * High initial implementation costs for advanced technology and staff training. * Scalability and flexibility of digital solutions require more attention. * Develop scalable models that are adaptable to different operational contexts and infrastructures. * Creation of standardized frameworks to facilitate the integration of digital technologies in various rail networks. In summary, addressing these limitations through future research could significantly improve the effectiveness and efficiency of the data model in rail asset management. For the practical implementation of the digitalization-based approach and the data model for railway asset management, several key recommendations can be made:  
* Developing a Standardized Framework: It is crucial to develop standardized frameworks and protocols that facilitate the integration of various digital technologies in criticality assessment. This development will help overcome data integration challenges and ensure that systems are interoperable. * Training and Change Management: Adopting new technologies requires significant organizational change. Implementing training programs is essential to ensure that staff are technically trained and aligned with the strategic objectives of the data-driven approach. * Cost-Benefit Analysis: Conducting a detailed cost-benefit analysis is essential to justify investments in digitalization. This analysis will help organizations understand the long-term benefits and cost savings of implementing these technologies. * Optimizing Data Quality: Ensuring data accuracy, completeness, and timeliness is essential for practical criticality analysis. Robust data collection and management processes must be established to minimize discrepancies that can lead to erroneous assessments. * Implementing advanced technologies: Advanced tools such as digital twins and machine learning algorithms can significantly improve criticality analysis. These technologies enable dynamic, real-time analysis, optimizing fault identification and prioritization. * Cybersecurity and Data Privacy: It is critical to implement cybersecurity measures to protect the integrity of information used in criticality analysis, ensuring that the data are secure and used ethically. * Application in Real-Time Environments: We expanded the discussion to address the potential for integrating real-time data sources into our model. Specifically, real-time traffic conditions and geometric measurements from inspection trains could be incorporated to enhance the accuracy and immediacy of the criticality assessment process. By including these real-time data sources, we aim to minimize delays in data-driven decision-making, which, in the current system, can sometimes exceed 12 months.