Tools
WareBeeSimulation .
Test any change in the digital twin before it reaches the floor — layouts, slotting, policies, automation, demand spikes. Powered by SKU behaviour profiling that understands how each item actually behaves in your operation.
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Decisions proven in the twin first.
What-if scenarios
Demand spikes, new slotting strategies, shift patterns, peak season — modelled in hours.
Each scenario replays your real order history through the changed twin and reports the difference against today. Questions that used to be a consulting engagement — or a leap of faith — get an evidence-backed answer the same day they are asked.
Automation what-ifs
“What if we install AutoStore?” — throughput, cost and labour impact before capex.
The twin adds the proposed system — towers, ports, goods-to-person stations — and replays your own peak weeks through it. The business case that reaches the board is built on your order profile, not the vendor's reference site.
Policy testing
Storage, replenishment and batching policies trialled on real order history.
Pick a rule you are arguing about — storage, replenishment, batching — and the twin replays months of real orders under each variant. The winner is whichever policy the numbers back; the floor keeps running the old one until then.
Quantified deltas
Every scenario reports cost, travel, throughput and labour against the baseline.
Identical units, identical baseline, one results table — so “new layout versus more staff versus AutoStore” stops being three separate debates and becomes one arithmetic comparison an operations director can settle in a meeting.
SKU behaviour profiling
Six lenses on every SKU.
Simulation is only as good as its understanding of the items. WareBee profiles each SKU along six dimensions — keeping industry-standard classifications exactly as the industry defines them, and adding operational lenses where the standards stop.
A+ABC — work created
WareBee's operational flavour of ABC: based on pick and order lines rather than classic inventory value — how much warehouse work the SKU creates.
The count comes from a representative slice of your order history rather than a single peak week, so the score isn't skewed by one unusual month. That number feeds slotting and staffing plans directly, so an item finance barely notices can still earn a golden-zone location purely on how often someone has to fetch it.
XYZ — demand predictability
The industry-standard inventory-planning classification: demand variability and predictability. Kept exactly as the industry defines it.
Variability is measured period over period using the same statistical method planners already use for safety-stock calculations, so the letter a SKU gets here matches what an inventory system elsewhere would report for the same item. That consistency means the twin's slotting and replenishment logic can lean on XYZ without anyone reconciling two different demand pictures.
Regularity — steady or spiky
WareBee's warehouse-operations classification: whether the work a SKU creates arrives steadily or in spikes — frequency and regularity, separate from XYZ.
This lens looks at when the work lands within a period — every Friday, every shift-start, or spread evenly — rather than how much total demand varies month to month. Two SKUs can share the same XYZ letter and still need different treatment: one creates a spike a wave has to absorb, the other never taxes the roster.
Cube & replenishment effort
The physical cost of serving the SKU — space consumed, handling weight, replenishment cycles triggered.
Cube and weight come from catalogue data, but replenishment effort is derived — how often a location actually runs dry given current pick velocity, not a fixed reorder point. A small item topped up several times a shift can outweigh a bulky one restocked once a week when the two are ranked for slotting priority.
Affinity
Which SKUs are picked together and should live near each other.
Pairs and clusters are built from how often SKUs actually appear on the same order line, not from product category or supplier grouping. Two items with nothing in common on a catalogue sheet — different brands, different departments — end up next to each other because the order history says they travel together.
Lifecycle
Whether the behaviour is permanent, seasonal or temporary — so plans do not overfit to a promotion.
The classification tracks how long a velocity pattern has held and whether it repeats on a calendar, distinguishing a recurring peak season from a one-off promotion spike. A SKU slotted forward for a short campaign drops back out of the golden zone once the pattern ends, instead of squatting there because nobody remembered to revert it.
Frequently asked questions
Proving layout, slotting, policy and automation changes in the digital twin before the floor feels them.
What kinds of change can I test in simulation?
Layouts, slotting strategies, storage and replenishment policies, automation, shift patterns and demand spikes. Each scenario is modelled in the digital twin in hours, before anything reaches the floor.
Can I model an automation investment before I buy it?
Yes. Automation what-ifs let you ask questions like what happens if we install AutoStore, and see the throughput, cost and labour impact before you commit any capex.
What does each scenario report back?
Every scenario returns quantified deltas — cost, travel, throughput and labour — measured against your current baseline, so decisions rest on numbers rather than opinion.
What is SKU behaviour profiling and why does it matter?
A simulation is only as good as its understanding of the items. WareBee profiles each SKU along six lenses — A+ABC work created, XYZ demand predictability, regularity, cube and replenishment effort, affinity and lifecycle — so scenarios reflect how items really behave.
Are policies tested on real orders or on samples?
Storage, replenishment and batching policies are trialled on your real order history, so the results reflect your actual demand and not a synthetic dataset.
What is the digital twin that scenarios run on?
It is a digital copy of your warehouse that simulates the physical space, navigation and the processes inside it. That is what lets you test any change on screen before making it on the floor.