In the first use case it is necessary to increase the efficiency of intra logistics. The logistics within the store is, with 48 percent of the total logistics cost, the most expensive part of the supply chain. The core processes include filling the shelves and managing the store warehouse. These processes are characterized by long running distances and high search costs and are usually executed manually and without digital support. They bind employees who are not available to customers. This calls for a transformation towards omni-channel trading, i.e. the intelligent connection of the offline and online world. Delivered pallets are usually not adjusted to the store but to optimal transport costs and must therefore be laboriously unloaded, since the goods of a pallet are distributed over the sales area.
The basis for the solution is the semantic digital twin (semdZ) of a store through the availability of exact location and inventory data of all items. By using AI incoming goods from different sources can be sorted individually according to placement and demand. Thus, goods of a common retail space section are pre-placed on trolleys and can then be put away efficiently and in a space-saving manner. In addition, the processing of empty shelf space is prioritized, while non-refillable items remain directly in the warehouse. The expected high time savings create space for new services for customers in the omnichannel area, such as the comissioning of click-and-collect orders in the store, where products are ordered online and picked up at the store.
Funded by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.