# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 3 Methodology
### Generalized Mathematical Model for Criticality Assessment
#### 3.3.4 Generalization Considerations  
* Adaptation of Weights: The model is easily adaptable to different railway companies by adjusting the weights (\(w_{j}\)) according to the company's priorities. A company that values service quality more highly may assign a higher weighting to (\(w_{\text{service}}\)), while one that is more concerned about the environment might give a higher weight to (\(w_{\text{environment}}\)). The assignment of weights and consequences is typically carried out through an expert consensus methodology, in which relevant company areas determine which dimension has the greatest impact on their business. It is worth noting that this distribution may vary in the medium term; however, in the short term, it remains stable, as is common in any risk analysis.
* Integration of Digital Technologies: The implementation of this model can be realized in a maintenance management system (EAM), where digital twins of each asset would allow for real-time monitoring of the values of (FF\({}_{i}\)) and (C\({}_{i}\)) values, recalculating criticality continuously to adapt to changing operational conditions. In the absence of integrated systems, the data on asset operating characteristics must be extracted from a data model that consistently relates the operational context to the specific asset. These relationships may not be direct, so it will be necessary to use relationship tables to interpret the source data about the asset. Each of these sub-models can then be attached to each asset to make the information available on demand for future queries. In this study, the developed criticality model does not consider failure interdependence, that is, the cascading effect that a failure in one component can have on others. Although a factorial analysis of failure interdependence would be valuable, it is considered beyond the scope of this work due to the added complexity and specific data requirements on asset dependency. However, the analysis based on the loss of functionality of the higher-level maintainable asset, UFS (unit functional of system, as per [PERSON] et al. [26]), partially compensates for this interdependence, as it is reflected in practice through the functional loss of the UFS, even if not explicitly modeled. In the applied case study, one of the considerations in the ETL process includes an inheritedfailure model, in which failures of secondary components are transferred to primary components when these failures cause a system-wide malfunction. Nevertheless, we recognize that the impact of partial failures, which do not entirely stop the system, is not captured in this model and could be explored further. We suggest that future research consider integrating interdependence models, such as those based on reliability block diagrams (RBD), Markov chains, and other systemic analysis models, to provide a more comprehensive risk assessment in complex railway systems. Scenario Simulations: The model can be used to simulate different maintenance scenarios, calculating how criticality would change if preventive interventions were or were not carried out on certain assets. This is especially useful for planning maintenance investments.