Analyses warehouse movements and makes better use of floor space.
Developed in Germany · Hosted in the EU or on-premises
Delivered together with
Sovereign hosting in Germany, single-tenant from the first user.
Legal, tax and compliance expertise for legally sound deployment.
Advisory and implementation for the public sector, starting with the AI readiness assessment.
The movement data is in the WMS, every pick with location, time and quantity. It is rarely analysed, because matching storage locations to pick frequency is done by hand. So stock is relocated on the strength of experience, usually during the stocktake.
15% higher warehouse productivity is nothing more than these 4.6 kilometres, handed back to a shift that walks them today.
The 15% increase in warehouse productivity is this agent's headline figure. The 620 picks per shift and the average walk of 27 metres reflect a typical two-aisle warehouse; they do not come from a time study.
The agent reads the last few weeks' picks from the WMS, with location, time and quantity. It works out the pick frequency of every storage location.
Zone B, 19,600 picks over 8 weeks, 412 storage locations assessedThe agent maps the walk per pick to each item's location. It flags every high-frequency item stored in a distant location.
12 A items above the 27 m average walk, item 40118 at 61 m with 271 picksThe agent compiles swap proposals with the expected walking distance saved. The head of warehouse decides what is moved and when.
umlagerung_zone_b.xlsx, 12 proposals, 4.6 km less walking per shiftIt presents swap proposals with location numbers and the expected walking distance saved. The instruction to the shift comes from the head of warehouse.
It works with walking distances and picks from the WMS. Load capacity, bay height and hazardous goods segregation are checked by the warehouse manager before every swap.
It analyses eight weeks of movements from the WMS. The head of warehouse has to add what sales expects for November and December.
Connected systems
Operations
A pilot typically runs on your own data within 4 to 6 weeks. The agent connects to your existing systems rather than running as a separate, parallel environment.
EU cloud or private cloud. Processing is GDPR-compliant and models are never trained on your data. Details in the Trust Center.
It is based on experience from comparable projects and process benchmarks, not a commitment. We work out the realistic effect for your organisation during the assessment before the pilot.