Identifying train spare parts with Partium’s AI
04.04.2024
The importance of AI parts search in railway environments
AI technologies are playing an increasingly important role in the modern search for spare parts – especially in the railway sector.
Aging fleets, each with their unique array of parts, pose a complex puzzle for maintenance and supply chain teams. As trains age, the documentation surrounding their components becomes increasingly fragmented, with many critical parts reaching end-of-life or obsolescence.
Maintenance teams are dealing with onboarding of new trains, modernizing old trains across the entire fleet, and keeping duplicates and incorrect data out of your systems to prevent future complications. All while working on the shop floor trying to keep trains on track.


It appears as if there is no easy solution for these challenges, except there is: AI spare part search & AI-based data optimization.
How does AI spare parts search work
AI parts search is a bit like an extra pair of hands and eyes. Two key technologies that particularly stand out are image recognition and large language models (LLMs). These technologies offer innovative solutions to the challenges faced by traditional parts searches.
Image recognition technology allows a quick and accurate identification of the part by analyzing photos or images. This is particularly useful in situations where the exact name or part number of a spare part is unknown.
LLMs are particularly valuable in setting up and maintaining spare parts catalogs. You can process large amounts of text data, such as documents, Excel files, and online information, to extract and structure relevant information.
The combination of image recognition and large language models represents a powerful synergy.
AI spare parts search at German Railways (DB) and Austrian Railways (ÖBB)
Partium, leveraging its expertise in AI and railway technology, has developed a cutting-edge solution to address the specific needs of train maintenance. Partium helps maintenance teams to find and locate parts faster. Identifying spare parts and components correctly and searching for parts in the warehouse often costs valuable time, especially during unscheduled maintenance, and may even keep several technicians busy. With Partium they can find the right part in a snap, making a huge difference in asset availability & and reliability. Besides finding parts faster, Partium also offers a solution to enrich and optimize existing spare part master data.
Deutsche Bahn (German Railways) and ÖBB (Austrian Railways) both are valued Partium customers.
Deutsche Bahn & Partium
DB was looking for a digital and mobile solution to find and locate replacement parts and components faster. The main goal was to reduce the average search time per part from about 15-20 minutes.
It all started with the project for DB Fernverkehr (Long-Haul). Because of the huge success and user adoption in the field, DB Cargo, DB Regio (Commuter trains), DB Fahrzeuginstandhaltung (Fleet maintenance), and the DB Netz (Network) machine pool joined very fast. Partium is now the standard part search technology in basically all of Deutsche Bahn´s repair and maintenance environments.
ÖBB & Partium
To work more efficiently and expand the service area, ÖBB was investing more in future technologies such as digitalization and automation. They were looking for a solution that would allow all employees to identify and find spare parts, including the correct part number. ÖBB came across Partium upon the recommendation of Deutsche Bahn.
Besides finding parts faster and easier, ÖBB benefits from Partium in different ways, for instance:
- Faster onboarding of new employees
- Knowledge Management
- Overall accelerated processes related to internal part procurement/warehousing and maintenance.
The use of AI in train maintenance represents a pivotal step towards a more efficient, safe, and reliable railway system.
In this short video, you can see, how Partium changes the way how to search for train parts: https://www.youtube.com/watch?v=UL6U7wtufQ0