01By warehouse type
WMS for Apparel: Sizes, Colours, Returns
How apparel stock differs from other goods, from size and colour matrices to returns and seasonal peaks, and what to ask a WMS vendor.
By warehouse type
A food and beverage store room, a parts depot and a pallet warehouse do not count, pick or store the same way. Start with your type, then the features.
A cold store worries about temperature and dates. A parts depot worries about thousands of small items. A food and beverage warehouse has lot numbers and recalls to think about. The right features follow from the goods.
We organise this site two ways: by type of warehouse, and by the operations features that cut across types, such as cycle counting, slotting and kitting. Pick whichever way you think.
Cycle counting, slotting and kitting all depend on knowing which item sits where and what is moving. These are screens from the Microsive demo platform, demo data only, showing shelving, a forklift task and an equipment list.



What changes for food and beverage, parts, apparel and others.
01By warehouse type
How apparel stock differs from other goods, from size and colour matrices to returns and seasonal peaks, and what to ask a WMS vendor.
02By warehouse type
Lot control, expiry and rotation, holds, traceability drills, temperature zones and returns: what a food and beverage warehouse should ask of a WMS.
Keeping counts right without shutting down the building.
03Cycle counting
How cycle counts are chosen, what the counter sees, how differences get a recount and approval, and nine questions to ask a vendor.
Putting fast movers where they are quick to reach.
04Slotting
Warehouse slotting in plain terms: the velocity, size and weight rules, the golden zone, a spreadsheet start, and what slotting software adds.
Bundling items into a single sellable unit.
05Kitting
What kitting is, how kits differ from bundles and assemblies, how a WMS tracks components, and what to check when stock runs short.
06Picking
Plain definitions of wave, batch and zone picking, when each one fits, a hypothetical floor example and the mistakes that slow pickers down.