When picking is slow and labour costs climb, the reflex is to blame the WMS and start scoping a replacement. That's an 18 to 36 month project with a seven-figure budget, months of integration, and a change-management load that pulls your best operators off the floor. For most operations, the constraint is how work gets sequenced inside the WMS.
A WMS tells the picker what to fetch and where to put it. It rarely calculates the shortest route through the aisles, the best way to cluster orders into a single trip, or where each SKU should sit based on how often it moves and what it moves with. That's the optimisation layer, and it can run on top of what you already have.
What the optimisation layer changes
It takes the same orders and the same racking and makes better decisions about execution:
- Picking routes and clustering. Calculate the shortest path and group orders so a picker covers less ground per line.
- Slotting. Place fast movers and correlated SKUs where they cut travel, using velocity and order-affinity data rather than a static ABC map from two years ago.
- Cartonization and palletisation. Choose the right box and build stable, fuller pallets, so you ship less air and fewer trucks.
Optioryx, the optimisation platform we implement, publishes ranges of 15 to 30% higher picking productivity, up to 50% less walking, and 10 to 30% lower transport costs. Your numbers depend on your layout, SKU profile, and order mix, which is why we model your data before anyone commits to a target.
Why this fits South African operations
Two local realities make the case stronger. Load shedding compresses the hours you have to pick and dispatch, so getting more out of each working hour matters more here than in markets with stable power. And capital is tight: a project that pays back in months, without ripping out a working system, clears an investment committee far faster than a full WMS replacement.
The fastest win in most DCs is better decisions inside the system you already trust.
How to test it without betting the operation
Start with a data-backed assessment. We extract your order and location data, model the current state, and show where time and cost are being lost, before you spend on software. If the numbers justify a pilot, we scope one zone or one product family, measure against a baseline, and only scale what the results support.
If picking travel time and labour cost are climbing in your DC, a short assessment will tell you whether the fix is optimisation or something deeper. Either way, you'll have the numbers to decide.