A warehouse that thinks
Pickers were walking too far and fast movers were in the wrong places. They wanted slotting and pick paths that adapted.
Pickers were walking too far and fast movers were in the wrong places. They wanted slotting and pick paths that adapted.
Product placement was historical and manual. High-velocity SKUs sometimes sat in the back; pick paths were built by the WMS in a fixed order. As volume and SKU mix changed, travel time per order crept up and throughput hit a ceiling. The team wanted recommendations that used real order and velocity data and that could be phased in without a full re-slot every month.
We built a slotting and path engine that consumes order history, current locations, and warehouse geometry. It recommends which SKUs should move where to minimize total travel and suggests batch and path ordering for each wave. The output is a set of move tasks and pick sequences that their WMS and labor can execute. We refreshed recommendations weekly at first so the team could validate; once they trusted it, we added the option to push high-impact moves immediately. The same engine supports what-if scenarios for new SKUs or layout changes.
Average travel per order dropped; picks per hour increased. The warehouse could handle peak without adding as much temporary labor. They’ve since used the tool to design a new zone and are exploring integration with put-to-light for even faster cycles.