AI USE CASE
Warehouse Slotting Optimisation for SMEs
Automatically relocate fast-moving SKUs to cut picker walk distance and speed dispatch.
What it is
This use case analyses historical pick frequency and co-pick patterns to recommend optimal SKU placement within a small warehouse. For a small 3PL handling hundreds of daily picks, that translates to meaningful labour savings and faster throughput without adding headcount.
Data you need
At least 3–6 months of order pick history with SKU identifiers, pick locations, and order timestamps.
Required systems
- erp
Why it works
- Export at least 3 months of clean pick-line data before starting, even a simple spreadsheet export is enough.
- Appoint one warehouse manager as owner of the slotting recommendations with a recurring monthly review slot.
- Run a before/after walk-distance measurement over one week to validate the model and build internal buy-in.
How this goes wrong
- Pick data is stored only on paper or in non-exportable WMS logs, making historical analysis impossible.
- Warehouse layout changes frequently (seasonal goods, spot clients) so slotting recommendations become stale within weeks.
- Staff ignore relocation recommendations because the physical reorganisation effort is underestimated and never scheduled.
- Co-pick patterns are too sparse (too few daily picks) to yield statistically reliable clustering, producing noisy suggestions.
When NOT to do this
Don't invest in slotting optimisation if your warehouse holds fewer than 200 active SKUs and processes under 50 picks per day, the marginal gain won't justify the reorganisation effort for a team that size.
Example vendors
This page describes an idea. It gives no budget or timeline: those depend on your company. The free diagnostic tells you which ideas fit your situation.