A complete example of the report you receive from a scan. The operation analysed here, Acme Distribution Ltd, is fictional and its dataset is fully synthetic, generated to behave like a real mid-size trade distribution warehouse. Every number in this document was computed by the same analysis pipeline we run on real client data.
01 Fast movers are slotted like slow movers. 80% of A-class items sit outside the golden zone. Re-slotting by measured velocity is worth about £12,800 a year. Page 4.
02 The golden zone is wasted. Of the 311 pick faces nearest despatch, only 82 hold A-class items, 134 sit empty, and 19 are held only by items that did not move all month. Page 5.
03 Two zones carry half the walking. The P tote wall and aisle E took 52% of all pick lines, a congestion and resilience risk that re-slotting can rebalance for free. Page 6.
Every figure is computed directly from three standard WMS exports (page 7), routed through the same travel model for every scenario, with assumptions stated on page 7. Figures are indicative until calibrated on a site walk.
Your exports passed the integrity checks: 100% of pick lines matched to a known location · 0 primary-location mismatches between item master and observed picks · 0 orphan SKUs. Clean data in, numbers you can act on out. Where a client export has gaps, the scan reports them first, because fixing the data is often finding number one.
The 20% of items doing 80% of the picking should live on the pick faces closest to despatch. Here, 80% of A-class items sit outside that golden zone, so pickers walk deep into the racking for items they touch many times a day. Each dot below is one item: the further right it sits, the further a picker walks for it.
Labelled items are the three highest-velocity A-class SKUs slotted furthest from despatch.
Move A-class items into the golden zone and demote the slow movers occupying it, using the ranked move list the scan produces. Average travel falls from 26.6 to 16.5 metres per pick, a 38% reduction worth about £12,800 a year at current volumes.
The 311 pick faces nearest despatch are the most valuable real estate in the building. What actually occupies them:
Two things stand out. First, 134 golden faces, 43% of the zone, hold no assigned item at all: free capacity in the best real estate of the building, and ready landing space for the Finding 01 re-slot. Second, dead stock: 221 SKUs, 16% of the catalogue, were not picked once in the period yet still hold forward pick slots. 35 faces are occupied by nothing but dead items and are releasable outright, 19 of them inside the golden zone itself.
This is capacity you already own. Releasing it is the cheapest expansion a warehouse can buy, and it is what makes the re-slot compound: near faces are what the fast movers move into.
Relocate the 221 zero-movement SKUs to reserve or the disposal decision list. That releases 35 faces outright, clears 19 golden-zone faces for items that earn them, and consolidates the remaining dead slots out of the pick path.
The P tote wall and aisle E took 52% of all pick lines in the period. Concentration like this means pickers queueing behind each other at peak, uneven congestion, and a layout that is fragile to growth in exactly the SKUs that are already busiest.
The re-slot in Finding 01 is the fix here too: spreading the A-band deliberately across aisles while keeping each item in its correct storage type levels the load without touching a single beam.
When the velocity re-slot is sequenced, cap the share of A-class faces any single aisle carries. The scan’s move list can carry that constraint directly, so balance costs nothing extra.
For this example site our modelling engine also evaluated a re-rack option: 71.7% less travel than today (about £24,200 a year) and 29% more pallet positions within the same walls. Racking decisions are five-to-six-figure commitments; modelling them against your real pick history before any steel is ordered is exactly what the full digital-twin engagement is for. A scan tells you whether that conversation is worth having.
Any WMS or ERP that can produce a CSV or Excel export can feed a scan: MySYS GLOBAL, Mintsoft, Linnworks, Peoplevox, SAP, or a bespoke system. Column names do not need to match ours, we map them on our side. A representative month is enough to start; three months is ideal.
| File | Columns needed | Notes |
|---|---|---|
| pick_lines | despatch/order ref · date · SKU · location · qty · timestamps if available | One row per pick line. Timestamps sharpen the analysis but are optional. |
| sku_master | SKU · description · storage type · primary location | Unit price optional, it lets the report talk in £ of throughput. |
| location_master | location code · aisle/zone · bay · level · type | Coordinates optional. We derive positions from your codes, or from a layout drawing (PDF or CAD) if you have one. |
No customer or personal data is needed. Picker IDs can be pseudonymised before export, or by us on receipt. Data is used only for this analysis, shared with no one, and deleted on request. An NDA is available before anything is sent.
Travel modelled on lane-snapped serpentine routing from despatch, applied identically to every scenario, so savings isolate slotting value rather than routing discipline. Walk speed 1.4 m/s, picker cost £18/h loaded. Figures are indicative until dimensions are calibrated on a site walk or drawing.