# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 4 Case Study  
### Scenario and Application  
The selected case study focuses on a commuter rail network covering approximately 700,000 assets. One of the busiest lines within this vast network was chosen for a detailed criticality analysis. Initially, over 12,000 elements were assessed on this line (source: combined SQL queries from master data of Asset Inventory Database), but after a thorough review, the analysis focused on 4018 key assessable assets (from the manual and semi-automatic filtered depurations process over the original query's results). The remaining assets were linked to these key assets through parent-child relationship structures, allowing the data collected from the secondary assets to contribute to the analysis of the performance of the primary assets. The criticality analysis, based on operational attributes such as location, network type, and traffic speed, as introduced in Section 3.3, was conducted in several stages, starting with extracting historical data on asset performance, covering six years and accumulating more than 200,000 failure events (source: Maintenance Corrective System dedicates Software). These events were subjected to a segmentation and filtering process to select 13,000 relevant records for the study. However, the descriptions of the failures were performed in text (no code), which made a more in-depth analysis of the failure modes challenging. Clear criteria allowed the derivation of meaningful values at the element level for each section of the assessed network. One of the most significant challenges was the lack of accurate data on maintenance costs in the enterprise resource planning) system, which limited the analysis in that specific dimension. A detailed process of creating and populating the data master (master table) was implemented to facilitate the criticality analysis, serving as the central asset assessment database. Specifically, the data model and the master table were applied to promote a comprehensive analysis of the criticality of railway assets:  
* The data model provided an organized structure for capturing and analyzing detailed information about each asset, including attributes such as type, location, current status, and maintenance history. This structure was essential to understanding the relationships between the different components of the network and how the failure of one component could affect others. * The master table was created as a centralized database that integrated data from various sources, such as inventories and maintenance records, through SQL queries. This process allowed the extraction and combination of crucial data, such as failure frequency and asset classes, resulting in a rich and detailed database for criticality assessment. Using the master table allowed for automating the calculation of asset criticality, assigning a specific value to each asset based on its characteristics and operational context. This automatization was achieved by integrating various relationship tables that identified the levels of intervention required for each type of asset. In addition, cross-referencing with other key tables was performed to obtain specific characteristics, such as network type and station category, which were essential to associate attributes with specific network assets correctly. In other words, the data model and the master table facilitated a more dynamic and effective criticality analysis, allowing the prioritization of interventions and asset management optimization in the rail network. Specific data covering various dimensions of the railway system were used in the practical application. These data included track maintenance history, asset classification, asset inventory, section criteria, and operational definitions. These data were collected on demand from different points, ensuring the information was up-to-date and relevant to the criticality analysis (Table 5).