As part of Bombardier’s ambition to modernise its Maintenance & Diagnostic Centre (MDC) platform, a collaborative effort was launched to explore how AI and predictive technologies could reshape future maintenance operations across rail systems.
The engagement focused on defining a visionary concept for MDC - shifting from reactive servicing to data-driven, preventive maintenance powered by AI and deep learning. Working closely with domain experts and field technicians, the project broke down complex workflows into actionable use cases and scenario-based user journeys.
Through vision design sprints and stakeholder collaboration, multi-device prototypes were developed to visualise how maintenance routines could adapt to real-time data inputs and predictive models - streamlining diagnostics, reducing unplanned downtime and improving operational efficiency.
Scope included:
- Interviews with engineers and product owners
- Use case analysis across urban rail and metro systems
- Scenario development and workflow mapping
- Vision concepts for AI-supported diagnostics and alerts
- High-fidelity prototyping of a multi-platform interface
- Final concept presentation and strategic report
The work was selected by Bombardier for being representative of their innovation work and to validate product-market fit by showcasing it as part of their exhibition at InnoTrans Berlin — the world’s leading trade fair for transport technology — and received strong endorsement from Bombardier’s executive leadership as a forward-looking benchmark for maintenance innovation.
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