EUCO Rail - Data-driven spare parts planning for maximum availability in rail transport

EUCO Rail is a specialized provider of maintenance and service solutions for the rail sector. The company operates its own workshops and ensures that customer fleets remain fully operational at all times, with the clear mandate to have the right parts available at the right time and at the right location.

That mandate was precisely the structural challenge.

Rail maintenance places exceptional demands on spare parts planning: bills of materials are complex and vehicle-specific, supply chains are multi-tiered with sometimes long lead times, and maintenance needs arise both predictably (cyclic maintenance intervals) and unexpectedly (fault resolution under time pressure). At the same time, fleet and model diversity continues to grow and with it, planning complexity.

Key challenges before PartsOS:

• Reactive, largely manual procurement with no data-driven foundation
• No cross-location visibility into inventory levels and demand
• Difficulty synchronizing planned maintenance cycles with unplanned requirements
• Growing complexity driven by increasing vehicle types and workshop locations
• Capital tied up in excess stock while critical components were simultaneously unavailable
• High manual effort that could no longer scale with increasing model diversity

Maintenance cycles and consumption data, unified in a single forecast model

Jörg Ernst, CEO and owner of the EUCO Rail Group, and Claudia Kratz, Managing Director (Commercial) of EUCO Rail Services GmbH, first encountered PartsCloud at the Service Roundtable hosted by H&Z, one of the leading management consultancies specializing in after-sales in the mechanical engineering sector. They immediately recognized the potential of an AI-powered solution for the specific operational requirements of rail maintenance.

What followed was not a conventional IT project, but a pragmatic, partnership-driven implementation approach.

The four core pillars of the project

1. Full item master analysis at part-number level
   Every meaningful planning process starts with a clean data foundation. PartsCloud conducted a comprehensive analysis of EUCO Rail's entire item master at the individual part-number level: Which parts are required, and at what frequency? Which are critical to vehicle availability? Which carry long replenishment lead times?

2. Combined forecast model: consumption data + maintenance schedule
   The key differentiator in the rail segment: demand does not arise solely from historical consumption, it is also driven by plannable maintenance intervals. PartsOS combines both signals in a single forecast model, predicting which parts will be required for upcoming maintenance events, and in what quantities.

3. Automated order recommendations with an active reminder system
   Based on the combined forecast, PartsOS automatically generates purchase order recommendations, prioritizes open procurement decisions, and notifies the team via email and app ensuring no critical order is triggered too late.

4. Cross-site inventory transparency
   Across all relevant depot locations, PartsOS provides a consolidated inventory overview: which stock is held where, and where are bottlenecks forming? This reduces both duplicate orders and local part shortages.

The operational rollout and calibration of the forecast model, based on historical consumption data and maintenance schedules is currently underway.

Through the combination of data-driven demand planning and proactive procurement, EUCO Rail expects the following measurable improvements:

• Significantly reduced manual planning effort through automated order recommendations
• Higher availability of critical components driven by maintenance-based forecasting
• Fewer excess stocks through data-driven reorder point calculation
• Cross-location transparency, a unified view of inventory and demand across all workshops

This page will be updated as soon as first measurable results are available.