A full warehouse and an empty shelf
The warehouse is full. There are pallets of products that have barely moved in months, cash tied up in them, and a stock take that keeps flagging the same slow lines. Yet customers are being told the product they want is out of stock, and the sales team is chasing suppliers for express deliveries on the lines that actually sell.
Both problems land on the same people. Finance wants stock reduced. Sales wants more availability. The buying team feels they cannot win, because every attempt to fix one side makes the other worse.
Why both happen together
Most stock systems reorder with simple rules: a minimum level, a fixed reorder quantity, or "order the same as last month". These rules are usually set once, often the same way across whole categories, and rarely revisited. A product that sells steadily and one that sells in bursts get the same treatment. So do a product with a reliable supplier and one with erratic lead times.
The result is predictable. Steady, slow lines accumulate because the reorder quantity is too high for them. Volatile, fast lines run out because the buffer is too small for their swings. Averages hide both problems, because on average stock looks about right.
What the imbalance costs
| Side | What it costs |
|---|---|
| Excess stock | Cash tied up, storage space used, markdowns and write-offs |
| Stockouts | Lost sales, customers trying a competitor, emergency orders |
| Express restocking | Higher freight costs and supplier goodwill used up |
| Staff time | Buyers firefighting instead of planning |
| Customer trust | Repeat customers learn not to rely on you for key lines |
The overstock is visible on the balance sheet. The lost sales are not, which is why businesses often cut stock across the board and make the availability problem worse.
How we set stock by how each product actually sells
- We pull sales, stock and purchase order history from your stock system, ERP, Shopify or WooCommerce, and mark stockout periods so lost sales are not mistaken for low demand.
- We group products by how they sell (steady, seasonal, intermittent, new) because each pattern needs a different forecasting approach.
- We build demand forecasts per product with a range, for example a likely level and a high level, rather than one number.
- We model supplier lead times from your purchase history, including how much they vary, since an unreliable supplier needs more buffer than a reliable one.
- We combine the two into suggested reorder points and quantities per product, based on the availability you want for each group, so key lines get more cover and slow lines get less.
- We present this as a weekly reorder list or feed it back into your stock system, with overstocked lines flagged for action such as pausing reorders or promotions.
We backtest the approach on your past data before anyone relies on it, so you can see how it would have handled the periods that went wrong.
What the buying team sees
A reorder list where each suggestion reflects that product's own pattern. Slow lines stop being topped up by default. Fast, volatile lines carry enough buffer to ride out a busy week or a late delivery. Overstock is flagged early, while there is still time to act.
The conversation with finance changes too. Instead of a blanket instruction to reduce stock, there is a list of which lines are over-held and why, and which need more.
Buyers still decide. The suggestions come with the reason behind them, such as a seasonal rise or a supplier who has been running late, and any override is recorded. Over time the record shows where the model needs adjusting and where the team's knowledge adds something the data does not have.
Is this your situation?
- You have both slow-moving excess stock and regular stockouts.
- Reorder levels were set once and are rarely reviewed.
- The same rule is used for very different products.
- Supplier lead times vary and are not tracked properly.
- Cutting stock to free cash has made availability worse.