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How Do I Decide How Many of Each Size to Order for a New Clothing Style?

Fashion brands order sizes on habit and run out of 10s while 6s pile up. We build size ratios from your kept sales, stockouts and returns for each range.

Updated 3 min readBy SpiderHunts Technologies

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Quick answer — TL;DR

Size ratios are often a habit copied from the first production run, so the middle sizes sell out while the ends are left over. We calculate size curves per range from what customers kept, correct for sizes that were sold out, and give your buyer a suggested split for each new order that they can adjust.

Sold out in a 10 and 12, drowning in 6s and 18s

At the end of the season, the leftover stock tells the story. Every style has a few size 6s and 18s left, and every bestseller sold out in 10 and 12 weeks before the end. The size split on your purchase orders is the same for every style: something like 1, 2, 3, 3, 2, 1. Someone set it up years ago, and it has been copied onto every order since.

Meanwhile your jeans sell to a different size curve from your dresses, and your knitwear in S, M, L sells differently again. The single ratio suits none of them.

Why the ratio never gets fixed

  • Sales reports show what sold, not what would have sold if the size had been in stock.
  • Returns are not taken off, so bracketed sizes look more popular than they are.
  • Size curves differ by range, but most brands use one ratio for all.
  • The buyer has no time to rebuild a curve before each factory order.

The first point matters most. If the 10 sold out on day twenty, the sales report only shows twenty days of 10s. The data understates exactly the sizes you need more of.

The cost of the wrong split

ResultEffect
Middle sizes sell out earlyLost sales on your best styles
End sizes left overMarkdowns and clearance
Broken size runsStyles that look available but are not
Reorders of one sizeFactory minimums make small top-ups expensive

How we build size curves you can use

  1. We pull sales by style, colour and size from your store and any other channels, net of returns, so the curve reflects what customers kept.
  2. For each size, we note the days it was out of stock, and estimate what it would have sold in that time from its own sales speed while it was available. The adjustment is shown separately so your buyer can see it.
  3. Curves are built per range, and per fit where it matters, because a slim tailored trouser sells differently from a relaxed one.
  4. For a new style, the buyer picks the range or a similar past style, and the tool suggests a split for the order quantity, rounded to the factory's pack or ratio rules.
  5. The buyer adjusts and approves the split, and it can flow into the purchase order.
  6. After the season, a report compares the ratio ordered with the ratio sold, so the curves improve each time.

When the buyer sits down to order

The buyer is placing an order for a new wide-leg trouser. They choose 'trousers, relaxed fit' as the reference, enter the total quantity and see a suggested split with the reasoning next to it: past sales, returns taken off, adjustment for sell-outs. They move a few units from the 18 to the 12 because they know the fabric runs small, then approve it. Nobody has to build pivot tables from scratch.

Honest caveats

The tool does not know about things that are not in the data, like a new customer group you are trying to reach or a sizing change to the pattern. That is why the buyer always has the final say. Small ranges with little history get a curve borrowed from a similar range until they build their own. Extended sizes launched recently need special care, because low early sales may reflect low stock rather than low demand.

Signs your size ratios need work

  • The same size split goes on every purchase order.
  • Middle sizes sell out while end sizes are left over.
  • Your size data does not account for sell-outs or returns.
  • Jeans, dresses and knitwear share one ratio.
  • Reorders are for single sizes that ran out.

FAQ

Frequently asked questions

The questions readers ask us after this guide.

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Is this a forecasting system?

It is narrower than that. It suggests how to split a quantity across sizes. How much to buy in total stays the buyer's decision.

Where does the data come from?

Your store and any other sales channels, plus returns data. Stock history is needed for the sell-out adjustment.

What if we have little history?

New ranges borrow a curve from a similar range until they have enough sales of their own.

Can it follow factory pack ratios?

Yes. If the factory packs in set ratios or minimums per size, the suggestion is rounded to them.

What drives the cost?

How many sales channels to combine and whether stock history is available or has to be rebuilt.

Keep reading

More on Problems We Solve

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Describe your ranges, where you sell (your own store, wholesale, marketplaces) and which spreadsheets and inboxes hold it together. We will tell you what we would build or connect, and if an app or a setting you already pay for covers it, we will say so.

  1. You tell us what you needTwo minutes on the form, or a message on WhatsApp.
  2. A senior engineer reviews itAnd comes back with questions, a realistic range and an honest view on fit.
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