Over 1,500 SKUs, and 80% of the profit coming from 20% of them — while the long tail quietly choked the factory. Nobody would cut a line, because nobody could prove what cutting it would do. We gave them the number.
Plenty of consumer-goods companies fall into the same trap: launch another flavour, scent, size and pack to hold shelf space, until the operation chokes. This one had, and 80% of its profit was coming from just 20% of its products.
The long tail was expensive. Hundreds of niche variations sold in small volumes, but each demanded frequent line changeovers — stopping to clean down and swap packaging — which raised production costs across the whole plant. Meanwhile, new launches weren't really bringing new customers; they were cannibalising the sales of the classic, more profitable lines.
And it was tying up cash. To support 1,500 SKUs, the warehouses held vast quantities of raw materials and dedicated packaging, some of it expiring or thrown away. But no one in the company dared pull a product, because no one could calculate what it would really do to customers and sales.
The true cost to serve each SKU, and what really happens if it leaves the shelf.
We connected each product's sales with its precise production cost, the machine-downtime it forces, and the cost of holding its inventory — so profitability was measured fully loaded, not on revenue alone.
An algorithm separates the “parasite” products — losing money and jamming the operation — from the “strategic anchors” that genuinely bring the crowd, so the catalog can be judged on truth instead of habit.
The engine simulates what happens to sales if a given product is dropped — whether the customer buys another of the company's products, or switches to a competitor — so the risk of a cut is known, not guessed.
The model shows which new products added real customers and which merely ate the classics — the difference between growth and expensive churn on the shelf.
Leadership — CEO, commercial and operations — get clear cut-and-consolidate recommendations, each with an accurate forecast of the production-cost saving and the warehouse space it frees.
Nothing is delisted automatically. The model hands the board the number it never had; the executives choose what goes and what stays, and own the decision.
Fewer products, more profit, more cash.
A quarter of the unprofitable catalog was cut or consolidated with no hit to the top line at all — the demand simply consolidated onto the company's own core products instead of leaking away.
Moving to longer, more profitable runs of core products cut production-line downtime by 35% — and freeing a substantial amount of working capital that had been locked in surplus packaging and raw materials markedly improved free cash flow.
The cut was only possible because, for the first time, every product carried its true cost to serve and the fallout of removing it could be modelled — the same discipline of joining commercial data to the real cost and supply picture that runs our demand-forecasting work. The board still made every call; the model just replaced fear with evidence.
Reef TRH