When a customer buys a train, they’re not simply purchasing a vehicle. They’re investing in three decades of reliability, maintenance, spare parts availability, and operating costs.

A failure that occurs years in the future often traces back to decisions made during engineering. A maintenance strategy defined during bidding may impact fleet availability decades later. Every choice creates a ripple effect throughout the lifecycle of the train.

At Stadler Rail, managing those decisions requires information to flow seamlessly between engineering, service, and commercial teams.

With more than 12,800 vehicles operating soon across 50 countries and a diverse portfolio of highly configurable rail vehicles, Stadler’s RAM and LCC team found itself at the center of that challenge.

“We depend on consistent, reliable data across multiple systems,” said Bennet Weller, PLM/ERP Project Manager at Stadler Rail.

As customer requirements around reliability, maintainability, and lifecycle costs increased, the company recognized that spreadsheet-driven processes and disconnected tools could no longer support the business.

When Critical Decisions Depend on Fragmented Data

RAM/LCC is not a single business process. It spans:

• Reliability engineering
• Maintenance planning
• Availability modeling
• Spare parts management
• Lifecycle cost calculations
• Commercial bidding activities

Each area depends on information generated elsewhere.

“The topic overall of RAM and LCC has gained a lot of traction in recent years,” Weller explained. “All these processes are highly interconnected with each other.”

Yet the supporting data remained scattered.

Supplier information arrived through spreadsheets. Teams maintained different versions of the same records. Information was transferred manually between systems. Engineers spent valuable time searching for, validating, and reconciling data.

“The whole data structure in RAM/LCC was a little bit disorganized,” Weller said.

As projects became more complex, these inefficiencies began to limit visibility and slow execution.

Stadler didn’t have a data shortage. It had a coordination problem.

The Intelligence Layer Between Engineering and Operations

Rather than replacing existing systems, Stadler focused on making them work together.

The company adopted a simple principle: every system should do what it does best.

• Teamcenter manages engineering structures and product data.
• SAP manages maintenance information, pricing, spare parts, and lifecycle cost calculations.

The missing piece was a layer that could coordinate information between the two while keeping core systems clean.

That is where Mendix came in.

Instead of becoming another repository, Mendix serves as the operational layer that guides workflows, validates incoming data, and orchestrates information between systems.

For Stadler, Mendix provided:

• Rapid development under tight timelines
• Seamless integration with Teamcenter and SAP
• Flexibility to extend processes without modifying core systems
• Workflow orchestration across platforms
• Support for a clean-core architecture

This role is particularly important because supplier data arrives in different formats and varying levels of quality. Before information reaches Teamcenter or SAP, Mendix validates it, identifies duplicates, structures records, and routes them through guided approval processes.

“We are validating the data before the data comes into the core systems,” Weller emphasized.

The result is a scalable architecture that links product, maintenance, and cost data without compromising the integrity of core platforms.

Turning Information into Reusable Knowledge

The objective was never simply to improve data quality. It was to make information usable across the organization.

“We planned to build a Mendix RAM/LCC app where the data is structured,” Weller shared.

Once information is standardized and validated, it can be reused throughout the lifecycle:

• Engineering teams can leverage knowledge from previous projects.
• Maintenance planners can work from approved product information.
• Commercial teams can build lifecycle cost estimates with greater confidence.

Instead of recreating information project by project, teams now build on a common foundation.

Creating the Foundation for Smarter Decisions

The most visible result at Stadler is speed.

But the larger impact reaches beyond process efficiency.

• Engineers spend less time managing spreadsheets.
• Lifecycle cost calculations are based on consistent information.
• Maintenance planning is more closely aligned with design decisions.
• Teams across the organization work from the same data foundation.

Stadler plans to extend the solution to additional locations, increase automation, and explore AI-driven processing of supplier information. Those initiatives build on a framework that already connects engineering, maintenance, and commercial operations.

For Stadler, the real transformation was not reducing a process from 30 hours to 10 minutes. It was creating a continuous flow of information that supports decisions from the first design review through decades of operation.