Workshops, parts distributors and dealers in Austria and Germany may soon get a practical AI tool to cut stock and speed repairs. Fraunhofer Austria announced a free workshop in Innsbruck on 5 November 2026 to show a demonstrator called “Shopping Trolley Eagle”, built to forecast short‑term spare‑parts demand and to clean workshop master data — a direct after‑sales use case for garages and parts logistics.
The demonstrator and the test case
Fraunhofer says the Shopping Trolley Eagle uses past order history and recent deliveries to calculate the expected spare‑parts need for the coming two months and produces an individual order suggestion for each workshop; the institute highlights a live test run carried out with Lindner Traktoren in Kundl, which it notes operates an inventory of about 39,000 different spare parts.
The Fraunhofer team also presents a second demonstrator that uses language models to standardise and complete parts master data — normalising names, units and missing attributes so that ordering, matching and automated replenishment work more reliably across dealer and wholesaler systems. Fraunhofer frames both tools as part of a “Beyond the Transaction – KI‑Potenziale im After‑Sales” workshop that it runs with DIH West; the institute lists the session at Wirtschaftskammer Tirol in Innsbruck on 5 November 2026 (09:00–12:00).
Operational benefits and constraints
According to Fraunhofer the approach targets two everyday aftermarket problems: too many slow‑moving SKUs that consume cash and space, and inconsistent or incomplete master‑data that causes mis‑picks and delivery delays. The demonstrator’s two‑month forecasting window is explicitly designed for the workshop level — where lead times, kit complexity and seasonal demand vary widely. Fraunhofer also stresses that human experts remain in the loop: suggested corrections and uncertain matches are marked for specialist review.
