Overview

A common trend that happens as a company grows is shifting size scales. I have not seen an item run out of inventory across all sizes at the same time. Usually one or two sizes will break earlier than the rest and that can behave differently by the time of year. Forecasting at the sku level can sometimes be too granular based on these ebbs and flows and can put your item trend at risk. It is good practice to review your size scale spreads by different levels to ensure you are using the best data to inform your buys.

Examples:

  • Dark colors vs bright colors on the same item can have very different size spreads.

  • Selling in Holiday can have very different size spreads than in Spring.

  • Marketing events or promos can cause variances in size scales vs the norm.

This is why we have added the ability to choose a size spread that you have already validated to generate your forecasts.

Workflow

In the Forecast function menu you can locate the Size Spread field near the bottom after the Volume field. This is by design, as the size spread only gets applied after the forecast is generated in the backend. So no matter what Seasonality, Seasonality Groups or Volume method you choose the size spread will work based on reflowing the forecast to the chosen spread.


Use Cases

In the above menu selections, I based a new color of my Blake Jacket on my repeat Blake Jacket in Black's Sales History. I have no history on my new color, Clear, so I chose the Volume Method of Spread Current. This will take the 1000 units I want to buy in Clear (this was brought over from Assortment Plan) and spread it over the selected forecast Time Frame based on Black's seasonality. However, I cannot use the size scale that Blake Jacket in Black sells at because I have additional sizes in my new color. This is why I selected "Tops" as my Size Spread.

This is a good example using many of the functions we have in Toolio. However, you can use this for many scenarios such as:

  • Current volume trend is selling heavier in one more than normal.

  • You are forecasting for a season that has a different size selling trend than the one you are in.

  • You could have a large size scale and not every size sells every week so you need to use a more generic curve.

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