# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 5 Discussion  
During the case study, several significant challenges were faced that impacted the implementation of criticality analysis on the rail network. One of the main challenges was data integration and data quality. The effectiveness of criticality analysis depends on accurate, complete, and up-to-date data. However, the available data were fragmented and came from different sources and systems that were not always integrated, making it difficult to use effectively. Another major challenge was the complexity and heterogeneity of the railway network. The network comprised various interdependent components, each with its technical characteristics, criticality levels, and maintenance requirements. This interdependence required a methodology that considered the criticality of each element and the system as a whole. In addition, the technological infrastructure needed to implement advanced criticality analysis presented significant challenges. Robust data management systems, specialized software, and advanced analysis tools such as digital twins and machine learning algorithms were required. Procurement, installation, and maintenance of this infrastructure proved costly and technically complex. Finally, training and change management were critical challenges. Adopting new methodologies and technologies involved a significant change in how asset managers and  
Figure 4: Example of the resulting criticality matrix. operational staff carried out their work. This adoption required a considerable commitment to training and organizational change to ensure that staff were technically trained and aligned with the strategic objectives of the data-driven approach. Digitalization significantly improved operational efficiency and decision-making in rail asset management through several key strategies. First, automating data collection and processing allowed for a smoother and more accurate information integration. Sensors installed in the rail infrastructure collected real-time data on asset status, eliminating the need for manual input and reducing the risk of errors. These sensors facilitated a faster and more accurate response to maintenance and operational needs. In addition, the implementation of digital twins provided virtual representations of the physical assets, continuously updated with real-time data. These digital twins allowed asset managers to simulate different operational scenarios and anticipate potential failures before they occurred, resulting in a more proactive approach to maintenance and asset management. The ability to anticipate possible problems improved planning and reduced unplanned downtime, optimizing operational efficiency. Digitalization also promoted interoperability and connectivity between asset management systems, databases, and analysis tools. This connectivity facilitated data integration and provided a more complete and coordinated view of the entire network, improving operational efficiency and decision-making. By sharing and using data effectively, organizations were able to improve cooperation and efficiency in a complex, multinational environment. Furthermore, implementing the data model transformed asset management by providing a powerful tool for continuous criticality assessment, optimizing and preventive maintenance, and improving the safety and reliability of railway operations. The results obtained by implementing data modeling and digitalization in railway asset management have several advantages over traditional methods. Firstly, data-driven methods offer a more accurate and reliable asset criticality assessment due to real-time and historical data, which allow for identifying patterns and trends that may not be evident with more conventional methods. In addition, traditional methods often rely on manual inspections and qualitative assessments, which can lead to decisions based on assumptions or experience. In contrast, the digitalized approach allows for quantitative and objective evaluation, facilitating prioritizing maintenance actions based on concrete data.