Warehouse metrics
Look up the number, then findout if it's actually a problem.
Every measure here is defined from scratch, computed on your own data, and read against a peer set matched on order profile, SKU count and footprint — never a blanket industry figure. Find the metric you're chasing, see exactly where it comes from, and follow it to the challenge page that explains what a bad number usually means.
Start a pilotThe full library
All 26 measures.
Grouped by where each one sits in the operation — service, inbound, replenishment, space, labour, assets, cost, and compliance.
On-time shipment
Whether orders leave before the trailer does, timed against the schedule that actually matters.
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Lines picked per hour
The throughput a picker sustains once travel, waiting and handling are pulled apart.
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Travel distance per line
How far a picker walks to complete one line, mapped from the actual navigation graph rather than a straight-line guess.
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Wave completion rate
Whether a wave closes on time, and where in the shift it usually starts slipping.
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Putaway cycle time
The gap between a pallet's arrival and its confirmed stow, and what tends to stretch it.
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Trailer dwell time
How long a trailer sits at the door beyond the time its load actually takes to move.
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Dock-door utilisation
Splits a door's day into working, blocked and empty, so a busy-looking dock can be told apart from a jammed one.
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Pick-face fill
How close each pick face sits to its rated capacity when a wave begins.
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Pick-face stockouts
Counts the moments a picker reaches a location and finds nothing there to pick.
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Replenishment cycle time
The time from a low-stock trigger to stock that's actually pickable again.
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Storage capacity utilisation
Live occupancy set against true capacity, not the figure on the racking drawing.
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Storage density
How fully the locations already in use are filled, independent of how many locations exist.
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Empty and partial locations
Where slack capacity is actually sitting, location by location, rather than folded into one building-wide figure.
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Dead-stock share
The share of prime locations tied up by stock that hasn't moved in a meaningful stretch.
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Peak headroom
The gap between forecast peak volume and the ceiling each zone, dock and pick face can actually sustain.
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Labour cost per line
Isolates the labour portion of what one picked line costs, apart from equipment, space and everything else.
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Overtime share
Worked hours that fall outside what the shift plan allowed for.
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Plan adherence
Tracks planned against actual task completion, wave by wave and zone by zone, through the shift.
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Golden-zone share
The proportion of picks that land within reach, without stooping or stretching for them.
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MHE utilisation
Forklifts, reach trucks and pallet trucks, broken out by class and by hour rather than averaged into one number.
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Automation throughput
Station output measured against rated capacity, with idle time attributed to whatever actually caused it.
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Cost to serve
Builds true fulfilment cost from putaway, pick, pack and dock events instead of an allocated overhead line.
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Cost per order line
Cost to serve taken down to the line, for the orders where an average hides more than it shows.
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Compliance exceptions
Open dangerous-goods, racking-weight and fire-safety breaches, set against how long each has stood open.
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Safety incidents
Recorded incidents and near-misses, read alongside the golden-zone, weight and travel conditions the twin can already see.
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CO2 per order
Carbon attributed to fulfilling an order, drawn from the same activity data the cost model already uses.
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About this library
A number is only useful once you know where it came from.
Warehouse teams throw metric names around constantly — cost to serve, dwell time, pick-face fill — without a shared answer for what any of them actually measures or how the figure gets built. Each page here starts by naming the measure plainly and explaining, in plain terms, how WareBee's digital twin computes it from the activity your operation already generates.
From there it's cause: which parts of a shift, a slot or a route move the number, and which of them apply to you before you change anything on the floor.
The question every reader actually has — is my number bad? — never gets a borrowed answer. WareBee scores it against a peer set matched on order profile, SKU count and footprint, because a warehouse running dense e-commerce SKUs and one running pallet-in-pallet-out 3PL freight were never going to land on the same figure, and a single industry average was never telling either of them the truth.