Warehouse metrics
Empty racking isn't one problem.It's a place.
Empty-and-partial locations is a count: how many locations across your footprint sit empty or below a fill level worth holding a location open for, grouped by where they actually sit rather than left as a flat total. WareBee flags every one continuously and clusters them by zone and aisle, so a scattered handful and a concentrated run read as the different problems they are.
See where the empties are clustering
What it is
How many, and exactly where they sit.
Empty-and-partial locations, on WareBee's digital twin, is a count and a map together: the number of locations sitting empty or holding less stock than makes the location worth keeping open, grouped by zone and aisle rather than reported as one flat total. It isn't a ratio like storage capacity utilisation, and it isn't a cube reading like storage density — it's a tally of specific locations, each one an address the twin can point a picker or a consolidation task at directly.
The number moves with how consolidation gets handled, not just how much stock is on hand. Fragmented quantities left scattered across many locations instead of pulled into one produce a higher count than the same stock consolidated would; putaway that defaults to whatever location is nearest rather than one already thin adds to a cluster instead of clearing it; and a promotion or seasonal drawdown that empties a run of locations all at once leaves a concentrated cluster very different from the same count spread thinly across the building.
A count of empty and partial locations means one thing on a site running a handful of SKUs through a handful of doors, and something else entirely on a site running a sprawling, fragmented catalogue across a much larger footprint — the same raw number could be unremarkable on one and a clear sign of drift on the other. Rather than publish a distribution meant to fit every warehouse, WareBee checks yours against a peer set matched on order profile, SKU count and footprint, and asks whether locations sitting empty or below a useful fill level are clustering somewhere a comparable operation wouldn't normally let them.
What moves this number
What leaves a location standing empty — or half so.
Each one is measured on your digital twin from data you already generate, so a count on its own turns into a specific run of locations worth walking.
Below a useful fill level
A location holding a token quantity is doing the job of an empty one while still reading as stocked on a simple occupancy check.
WareBee flags locations sitting below a fill level worth holding one open for, not just the ones showing zero, so a slowly draining slot gets caught before it's fully empty.
Where they cluster
A scattered empty here and there is a different problem from a whole aisle running thin at once.
Locations are grouped by zone and aisle rather than listed one by one, so a single walk clears a run of them instead of a picker crossing the building for each isolated case.
Fragmented quantities left unconsolidated
The same SKU sitting half a pallet deep across several locations instead of one keeps every one of those spots reading thin.
A move plan groups every scattered pocket of the same SKU into a single sweep, so consolidation clears a cluster in one pass rather than being chased location by location.
Putaway defaults to nearest
New stock keeps landing in whatever location is closest rather than the thinning one that actually needed it.
Directed slotting routes incoming stock toward locations already running low before it defaults to whatever's nearest, so a cluster of empties gets fed instead of ignored while receiving takes the shortest walk.
A walk-round finds a handful. The twin finds the pattern.
A supervisor walking the floor spots the empty locations directly in front of them — the twin sees every one at once, grouped by where they actually sit. WareBee flags locations empty or below a useful fill level continuously, then clusters the count by zone and aisle, so a run worth consolidating in one pass shows up as one job instead of a dozen separate stops on someone's list.
From there it's a consolidation and slotting question, tested first. Moves that pull fragmented stock into fewer locations, or redirect putaway toward the ones running thin, are simulated on the digital twin against your real stock and order data before anyone touches a pallet, so the locations reclaimed are known in advance, not discovered afterwards.
- Empty and partial locations flagged continuously and grouped by zone and aisle
- Fragmented stock consolidated in one pass instead of chased location by location
- Consolidation and re-slotting moves simulated before a pallet moves
Questions
Empty and partial locations, counted and placed.
What counts as a 'partial' location, not just an empty one?
A location holding stock, but less than makes it worth keeping open on its own — a quantity thin enough that it's doing the job of an empty location while still reading as stocked on a simple occupancy check. WareBee flags both empty and partial locations together, because a location slowly draining toward empty is the earlier version of the same problem, not a different one.
How is this different from storage capacity utilisation?
Storage capacity utilisation is a ratio — occupied capacity against true capacity, read across the whole footprint. Empty-and-partial locations is a count, and a map: specific locations, grouped by where they sit, that a picker or a consolidation task can be pointed at directly. A building can carry a comfortable utilisation ratio overall while still holding a cluster of empty and partial locations in one particular zone, and the count is what finds it.
Shouldn't a lower count of empty locations always be better?
Not once footprint enters the picture. A large facility built with headroom for peak will always carry more empty and partial locations in an ordinary week than a tightly sized one running near its ceiling, and that gap says more about how each building was sized than about how well either is run. WareBee reads your count against a peer set matched on order profile, SKU count and footprint before it calls anything high or low, so a bigger raw number on a bigger footprint doesn't get mistaken for a worse warehouse.
Does a scattered set of empties mean something different to a clustered one?
Yes. A handful of empty locations scattered across an otherwise busy footprint usually reflects normal turnover — stock moved out, nothing's moved in yet. A cluster concentrated in one zone or aisle more often points at something specific: a promotion that sold through, a consolidation that never happened, or putaway defaults sending new stock everywhere except the locations running thin. WareBee groups the count by zone and aisle precisely so the two patterns don't get read as the same finding.
Can consolidating locations create new empties on purpose?
Yes, and that's the point of the exercise rather than a side effect to worry about. Pulling fragmented quantities of the same SKU into fewer locations deliberately empties the ones no longer needed, freeing them for something else instead of leaving stock scattered thin across a dozen spots. WareBee simulates the move first, so the locations that come free are known before anyone starts moving pallets, not counted after the fact.
What data does WareBee need to flag empty and partial locations?
Current stock levels against each location's rated capacity are enough for a first read, the same location and inventory exports most WMS platforms already produce. Reconciling that against occupancy on an ongoing basis is what lets the count update continuously and group into zones and aisles worth walking, rather than waiting on the next physical walk-round to go stale again.