SERVICE 02
Assortment and category management consulting
Every product on the shelf ties up capital. A good assortment is not the widest possible range; it carries what your local market actually demands, at the right depth and across the right price tiers.
Where does an assortment go wrong?
An assortment does not break in one move. It drifts, through the sum of individually reasonable decisions. A supplier brings a new range and a shelf is cleared for it. A customer asks for something and it joins the list. A discounted batch comes up and gets bought. Each one makes sense on its own; two years later they add up to a tail that holds half the inventory and produces a tenth of the revenue.
The cost is not limited to tied-up capital. Shelf space, counting time, ordering attention and stockroom capacity go to the same products. As the non-moving tail grows, availability on the products that do move falls — because the space, the money and the attention are all somewhere else.
The point of this work is not to cut the range for its own sake. It is to tie category width and product depth to sales data and local demand, and then to put that decision on a footing that lasts.
If any of these sound familiar, this page is for you:
- A meaningful share of inventory has not sold for months
- Three brands doing the same job sit side by side and two never move
- Best sellers run out regularly
- There are gaps between price tiers — the entry or premium tier is missing entirely
- New product decisions follow supplier visits rather than sales data
What we do
We begin by assessing your current product assortment, use the findings to guide decisions, and establish a practical routine for maintaining the range.
Current assortment analysis
Products are grouped by units sold, revenue, margin contribution and turn rate. What actually works and what merely occupies space becomes visible after the first session.
Category width and depth
For each category we set how many distinct products to carry and how many variants of each. Some categories reward depth, others width; the split depends on the area and the customer profile.
Price tiers
We check whether entry, mid and premium tiers are represented sensibly in every category. Gaps and needless overlaps surface here — customers need all three to exist meaningfully in order to compare at all.
Dead stock plan
For non-moving products we prepare a realistic exit: markdown, bundling, supplier return or phased liquidation. The aim is to move committed capital into products that work.
Supplier and buying rhythm
We review how many suppliers each category needs, how often orders go out, and what minimum order quantities do to your stock position. We also define which data should guide each buying decision.
New product rule
We write a short rule setting out which questions a new product has to answer before it earns a shelf, and what comes off in exchange. That is what stops the assortment from swelling again.
How we work
The tidier the data, the faster this goes — but messy data is workable too. The first step is often simply making the data usable.
Data collection
We pull sales, stock and purchasing data from your existing system and turn it into one table that can actually be analysed. Missing and incorrect records surface here as well.
Analysis and findings
We prepare turn rate, margin contribution and committed-capital tables by category, and present them showing plainly which number drives which conclusion.
Assortment decision
We decide together on category width, depth, price tiers and the dead stock plan. Decisions are written at product-list level, not left as general advice.
Rollout and tracking
Changes are made in stages and measured at set intervals. Where the expected effect does not appear, we look at why together.
What you receive
The reasoning behind each decision stays visible:
- Turn rate, revenue and margin contribution tables by category
- An assortment decision with width and depth targets
- A price tier map and the gaps found in it
- A dead stock clearance plan with dates and methods
- A recommended order frequency and supplier structure
- A one-page rule for admitting new products
Common questions
Our sales data is a mess — can we still do this?
Yes. In most businesses the data starts messy. The first step is making the existing records usable, and that step alone tends to make several decisions obvious. Where there is no data at all, we start with a short observation period.
Won't cutting the range cost us customers?
The aim is to retain the products customers value while reducing stock that attracts little demand. What gets cut is what was not selling; what is gained is that the most-wanted products are consistently in stock. Decisions draw on available sales data, on-site observation and customer needs.
When do results show?
The availability effect is usually felt within the first weeks. Improvement in stock turn and committed capital becomes clear over three to six months, depending on how fast the clearance plan runs.