MaintenanceNewVerified Real deployment at a named or precisely described company, with a measured result from a primary source.

Technicians identify a spare part from a photo or a few words and see its stock and location (Deutsche Bahn, ENGEL)

Added 2026-10-16 · Last updated 2026-10-16

Role

Maintenance (spare-parts identification) · Typical users: maintenance technician, MRO storeroom clerk, maintenance planner, OEM parts and service team

The problem

A technician standing at a broken machine often holds a worn part with no readable label and has to find its part number in catalogues, parts lists or a messy material master, or ask the one colleague who knows. On older equipment the documentation is incomplete or missing. The search takes time, the wrong part gets ordered, and OEMs field the same identification calls on their hotline. manroland Goss says its printing systems can have up to 40,000 parts in up to 500 assemblies, with 5,000 to 10,000 consumable parts depending on the press type; at DB Cargo, fleets span decades and documentation is often incomplete.

How AI helps

The technician photographs the removed part, types a few words or reads the label, QR or barcode, and an AI search (image recognition plus semantic text search over the material master, parts lists and drawings) returns the matching part with its SAP or part number, manufacturer, storage location and stock. When there is no confident match, the request goes to an expert with the photos and what the technician already tried.

Step by step

1. Measure the baseline: how long technicians spend identifying a part, how often the wrong part is ordered, and how many calls go to the expert or the OEM.
2. Clean the spare-parts records that matter first (critical spares on your bottleneck machines): manufacturer, manufacturer part number, a standard description, dimensions and location.
3. Add photos of the critical spares to their records, both new and as removed from the machine, plus the labels.
4. Pilot the search on one area: the technician photographs or describes the part, picks from the suggested matches, confirms against the label or dimensions, and the storeroom checks every pick in the first weeks.
5. Route no-match cases to the expert with the photos, and add each answer back to the record so the next search finds it.
6. Track time to identify, wrong parts ordered and expert calls against the baseline, and extend to other areas once the error rate is acceptable.

Tools

• Partium Find (image and semantic multi-modal part search): used at Deutsche Bahn (DB Mat.ID), ENGEL and manroland Goss
• Other tools to evaluate: visual part search in your CMMS or EAM if it has one, OEM parts portals with image search, shape-search tools for 3D parts, MRO master-data tools (Sparetech, Verusen)
• Any multimodal LLM (ChatGPT, Claude, Copilot, Gemini) with the prompt below, on a photo and an extract of your spare-parts list, to test identification on a few real parts

As reported by the source; most figures are vendor or self-reported and not independently verified.

Human review: cautions

• The technician confirms every match against the label, the dimensions or the drawing before installing; a look-alike part with the wrong rating or material can fail in service.
• Results depend on the material master: duplicates and vague descriptions give wrong matches, so clean the critical spares first.
• The figures come from the vendor's customer pages; Deutsche Bahn is a rail operator, and ENGEL and manroland Goss report no figures.
• Do not paste supplier prices or confidential drawings into a public chatbot; use an approved enterprise tool.
• General chatbots can misread labels and invent part numbers: use the prompt only to shortlist from your own list, and always check.

You are an experienced maintenance storeroom specialist helping a technician identify a spare part.

I will attach photos of the removed part and any label, and paste: (1) the machine and assembly it came from [machine, make, model, assembly]; (2) what I can measure [dimensions, thread, number of holes, material if known]; (3) an extract of our spare-parts list for that machine [part number, description, manufacturer, manufacturer part number, dimensions, storage location, stock].

Do this:
1. Describe what you see on the part and read any text, number or code on the label exactly as written; if you cannot read a character, write '?' instead of guessing.
2. Propose up to three matching records from my list, ranked, with the reasons for each match and what does not fit.
3. For each candidate, list what I should check on the part to confirm it (a dimension, a marking, a feature).
4. If nothing fits, say so and draft a short request to the manufacturer or our expert with the details they will need.
5. Flag records in my list that look like duplicates of the same part.

Do not invent part numbers or suppliers that are not in my list or on the label. Do not order or reserve anything.
Use only the data I paste in; if something is missing, write 'not found' or list it as a question instead of guessing. Do not send, order, post or change anything in any system: produce drafts for my review and approval.

Related cases

• Clean up duplicate and incomplete spare-parts records in the material master
• AI finds duplicate and excess MRO spare parts across plants
• Troubleshooting copilot over manuals and work-order history
• Condition-monitoring alerts ranked by an expert so teams act on the right ones

All 7 Maintenance use cases →

Sources

• https://www.partium.io/customers/deutsche-bahn
• https://www.partium.io/customers/engel
• https://www.manrolandgoss.com/en/read-press-release/items/manroland-goss-makes-part-identification-easier-via-partium-the-web-app-for-spare-parts.html