Liebherr AI parts app: maintenance planning and safety notes for engineers
Reviewed by Tom Sullivan

First reported on The Construction Index
30 Second Briefing
Liebherr Nenzing has launched an AI-based app to identify parts and schedule maintenance for its deep foundation equipment and crawler cranes up to 400 t capacity. Components can be located via photo recognition, QR code scan, multilingual text search in over 100 languages, or item number, with results cross-checked against the official Liebherr parts catalogue to avoid mis-orders when nameplates are damaged. The app also uses machine operating hours to propose upcoming service intervals, listing required parts, consumables and fluids in optimised quantities for each maintenance kit.
Technical Brief
- Nenzing plant coverage means AI support spans deep foundation rigs, duty cycle cranes and crawler cranes.
- Damaged or corroded nameplates no longer block part identification, reducing unsafe improvisation or component mismatch.
- Cross-checking against the official Liebherr parts catalogue adds a second barrier against installing incorrect critical components.
- QR-based identification ties parts to specific machines, helping maintain traceability for lifting, slew and hoist components.
- Operating-hour driven service kits encourage adherence to OEM maintenance intervals, limiting overrun on wire ropes, hydraulics and brakes.
Our Take
Liebherr’s use of AI for parts search at the Nenzing plant aligns with its broader digital push, such as the recent rollout of MyLiebherr Performance and MyLiebherr Maintenance services in the UK and other markets, signalling that support and maintenance workflows are being systematically digitised rather than treated as standalone tools.
With crawler cranes up to 400 t capacity supported and text search in more than 100 languages, this AI layer is likely aimed at global mixed fleets where incorrect or delayed parts identification can have disproportionate safety and downtime impacts on heavy lifts.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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