Slotting Optimisation

The analytical exercise of assigning each SKU to its optimum pick location based on velocity, cube, weight, affinity and ergonomics — to cut travel time and damage. The discipline matters because picking is the most labour-intensive operation in the warehouse, and the largest single lever on pick productivity is where things sit. A warehouse that runs Slotting Optimisation well treats location assignment as a data-driven optimisation — velocity, cube, weight, affinity, ergonomics and replenishment cost all weighted in a documented model — refreshed on a defined cadence and validated against measured pick rate and travel time before and after every change.

• Velocity classes are recalculated against current — not historical — demand.
• Heavy and bulky items are slotted near the aisle to reduce manual handling injury.
• Affinity is measured from order data, not assumed from product hierarchy.
• Forward-pick locations are sized to one to two days of demand, not 'whatever fits'.
• Re-slot moves are scheduled into low-volume windows and tracked as a change record.

• Refresh velocity classification at least quarterly — and after every major demand shift.
• Weight ergonomics alongside velocity — a top-velocity heavy item still does not belong on the top shelf.
• Sequence move tasks to minimise disruption — empty the destination, move the source, update the WMS in one transaction.
• Measure pick rate and travel time before and after — never re-slot without a control comparison.
• Lock down forward-pick sizing rules — operators should never improvise location size.

• Re-slotting once at go-live and never again — demand drifts and gains evaporate.
• Optimising for velocity alone — operator injury rate rises silently.
• Manual move execution with no WMS update — operators end up picking from empty bins.
• Forward-pick locations sized 'whatever the rack allows' — replenishment storm follows.
• No before/after KPI comparison — claims of improvement remain anecdotal.

• E-commerce — high SKU count, long-tail demand, frequent re-slot cycles.
• Grocery — heavy items low, multi-temp zones complicate affinity.
• Pharma — controlled drug locations fixed, but velocity rules in OTC zones.
• Industrial parts — slow-mover-dominated, ABC class C is the main optimisation challenge.
• 3PL multi-client — slotting model rebuilt for every client onboarding.