The Challenge: A national railway operator was struggling with declining punctuality and rising maintenance costs across its aging network. Maintenance was performed on a rigid schedule regardless of asset condition, leading to unnecessary interventions and occasional unexpected failures. The operator possessed vast amounts of data from trackside sensors and rolling stock, but this information remained trapped in siloed legacy systems. They needed a strategy to integrate these disparate data sources into a single operational view to drive decision-making.

How Sartori Helped: Our digital transformation practice led the design and implementation of a new Digital Maintenance Hub. We mapped the critical data flows required to predict track circuit failures. We then oversaw the integration of a middleware layer that pulled real-time diagnostic data from both the signalling system and the train management systems. We worked alongside the client’s engineering teams to define new operational protocols, shifting them from a “fix-on-fail” mentality to a “predict-and-prevent” approach. This involved retraining operational staff to interpret predictive alerts and adjust maintenance schedules dynamically.

The Result: The implementation of the Digital Maintenance Hub resulted in a 16% reduction in service-affecting failures within the first year of operation. The operator was able to defer capital expenditure on track replacement by extending the asset life through targeted interventions. Furthermore, the availability of real-time asset data allowed the client to optimise their night-time maintenance windows, increasing the productive wrench time of their engineering crews by 23%.