What is Broken Sizes?
Broken sizes is a term used in fashion business to describe an assortment that does not have full size range anymore, due to the sell out of some sizes.
When a retailer launches a line of clothing it initially launches in full size range. After a while of displaying it on the floor and selling to customers, the most popular sizes will start to sell out. By the end of the season, only a few pieces are remaining and only the less popular sizes are in display.
This affects the visual display on the floor, because the remaining assortment looks very light, and also when customers approach it they will not always find their sizes.
What retailers do in this case is to consolidate all the broken range of merchandise into one or two store locations by doing inter-branch transfers and display them there, or remove the stock from the floor until end of season sale time.
How To Avoid Having Broken Sizes?
It is almost impossible to have all the sizes in a clothing collection sell at the same speed. However, with proper planning and analysis the buying process can be optimized to minimize broken sizes at the end of the season.
It should be noted, however, that retailers have to carry the full size range from the beginning. Even when they know that some sizes will not sell that much, they still have to carry them in order to launch the collection properly. Also some suppliers dictate the size allocation and send their own size range in their allocated quantities to avoid having broken sizes at their end.
To optimize this process the retailer needs to analyze the sales of previous seasons and create a size curve that matches the customer profile of their store.
We have seen different stores having different size curves, and that’s perfectly normal, because it depends on the customer demographics of those stores.
having a robust analysis mechanism and actually acting on it, will allow you to predict the size curve for your stores that are closer to reality and hence minimize broken sizes at the end of the season.
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How Does A Size Curve Look Like?
A size curve is usually closer to a bell curve, where the most in demand sizes are in the middle and the outliers are in less demand.
For some places and customers this size curve could be a little bit skewed towards a smaller or larger range.
When you plan your size curve it could look like this:
S:M:L:XL
1:2:2:1
This simply means that for every piece of small size you will buy 2 pieces of medium, two pieces of large and one piece of x large.
Let’s say you have decided to buy 120 pieces.
Your ordered quantity will look like this
| S | M | L | XL |
| 20 | 40 | 40 | 20 |
The relationship between those numbers will depend on your analysis of sales data from previous seasons.
Read More: Size Curve
Considerations
A final consideration to note when analyzing your sales data by size is that typically the more popular sizes will sell out quicker, and so the data you have will not report the missing sales opportunities, which tell you the actual demand for the size.
For example if you ordered 20 pieces of a certain size and they sold out in the first week, and then at the end of the season you analyze and find that this size sold 20 and the other size also sold 20, and so you assume they are equal. However, if you had more quantity of that size in the first place, it would have sold more. It is just recording 20, because this is all what you had.
To rectify this and have a more realistic data for analysis we usually ask the store team to note down the missed sales opportunities due to out-of-stock situations for a particular size. This way we know that the demand for that size is far more than the quantity that was present, and in the next order we increase this quantity to match the actual demand.
Another way to do it also is to monitor the sell-through report and see when that size had a 100% sell thru. The earlier it is the more quantity needs to be added to next order.
As mentioned, we will never have the perfect prediction of what will sell next year, but having more data points for analysis will bring us close enough.
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