"27% increase in picking productivity with no change management"
Picking productivity runs 27% above where it was before Pulse, reducing up to 9 FTE in manual picking.
20 to 50% less walking distance.
Two decisions, solved together
1. Order clustering
Groups orders to get maximum pick density and minimum travel. 1 walk to the far aisle, instead of 2.
Applied for multi-order picking and waving, while respecting operation constraints: trolley capacity, weight limits, cut-off times, order priorities, fill rates and more.
2. Pick path optimization
Calculate the shortest travel path for every pick tour, cutting walking time. Solve jointly with clustering for even more gain.
Routes follow your movement rules: one-way aisles, cross-aisles and blocked zones. Combine with pick-to-box or pick-to-pallet operations.
Simulate the gains
Apply clustering or pick path optimization on the digital twin of your warehouse. See what each optimization does to picking productivity.
See it on your own warehouse
Questions?
No, it does not. Typically, a WMS sequences picks by location code. It has no model of how far apart two locations are, or which aisles run one way, so it falls back on a fixed snake path.
The ones that claim to apply picking optimization apply basic business rules such as go to the nearest location next, so it never solves the route and the order grouping together, which is where the 20 to 50% less walking comes from.
Almost nothing. The optimization runs in the background and the result goes back into your WMS, so operators pick from the same terminals / interface they use today.
We build a digital twin of your warehouse and show you the business case of applying the optimizations through simulation.
There are 3. Manual file import and export, flat files over CSV or SFTP, or a direct API connection to your WMS, TMS or ERP.
Any of them. Pulse exposes its optimization through an API, so it connects to whatever you already run.