Build the assortment matrix around what each location actually sells
An assortment usually accumulates by history: whatever was brought in once stays. Different neighbourhoods end up with the same list, a dead tail occupies shelf space, and missing items are invisible entirely.
What is wrong today
- The dead tail holds both shelf and cash, yet shows up in no report as a problem.
- Locations with different demand get an identical matrix.
- Revenue lost to an item you never stocked is nowhere counted.
How it works
Every item gets a role: core, local, test, delist, or missed opportunity. The role is argued from sales data, not from opinion.
Remove an item and you see the capital and shelf space it frees. Add one and you see expected revenue, the confidence behind it, and the cannibalisation of neighbours.
Delisting is not a checkbox but a sequence: stop purchasing, sell down the remainder, return to the supplier where the contract allows.
What it looks like in use
Every item is named by its role in the category — and by the number that justifies that role.
| Item | Locations selling | Share of category | Substitute | Decision |
|---|---|---|---|---|
| Nourishing cream 75 ml | 12 of 14 | 9.4% | no | keep |
| Nourishing cream 30 ml | 4 of 14 | 0.8% | yes, 75 ml | delist |
| Wet wipes, 60 pcs | 14 of 14 | 6.1% | no | keep |
| Shampoo 400 ml, type 2 | 3 of 14 | 0.4% | yes, type 1 | delist |
An anonymised example: locations are lettered and the dataset is shared across every screen on this site.
What it is built on
- Dead stock and the cash frozen in it are computed per location today — that is half of the future matrix.
- Sales history at item × location is already collected, slow movers included.
Who stays in control
A matrix version is approved by a person and stored with a date and an author. Supplier and shelf constraints are set by the network and never overridden by a model.
How it is measured
Against locations whose matrix did not change: revenue per item, share of dead tail, and capital released.
Show us one critical process. We will show how it runs here.
We look at your cycle: how an order is assembled today, who decides, where time leaks and what the system takes over.