Demand Planning for New Products in Retail

Retailers grappling with supply chain optimisation encounter a myriad of challenges, ranging from inventory management to demand forecasting to logistics and distribution. For D2C brands and retailers, particularly those dealing with a vast array of SKUs (like fashion industry), forecasting demand for new products remains a persistent challenge.

“As a fashion brand, we continually introduce new SKUs in various styles to cater to our customer base. While we can manage demand planning for our established items using techniques such as moving averages on Excel, we struggle when it comes to estimating demand for newly launched products,” expressed the Founder of a fashion brand.

Accurately estimating the demand for new products is a pivotal aspect of efficient inventory planning and overall business success. TrueGradient offers a solution that leverages machine learning algorithms to estimate the demand for new products.

The forecasting method for new products harnesses the sales volumes of existing products to predict demand for the new ones. This approach is particularly effective since new products often share common attributes with existing ones, such as colour, size, or flavour. This holds especially true when there are no major alterations in marketing or distribution strategies.

The model extracts product attributes from existing products and cross-learns them with all products in the same category, as well as other categories, possessing matching attributes to predict the demand for the new product. Let’s illustrate this with an example from a fashion apparel brand. Suppose they launch a new shirt. The SKUs for this new shirt, based on size and colour, may look something like this:

Now, the demand planning of new shirt will happen at the most granular level i.e. SKU ID. For SKU1, the model takes into account the existing assortment of shirts and algorithmically cross-learns the demand for red shirts, shirts with size XS, collared shirts, plain shirts, and shirts sold on the website to predict the demand for SKU1, which in this case would be a red, solid, collared shirt in XS size to be sold on the website. Naturally, these algorithms are more intricate than they might appear, as numerous factors are considered on the backend to ensure accuracy.

A very common misconception is to leverage competitor data for a similar product. However, what people often overlook in this approach is that every business is unique in terms of its growth stage, distribution channels, and strategies. What works for one business may not necessarily apply to others. Therefore, if access to competitor data is available, planners can use it as a reference but should not exclusively rely on that data for their own planning.

In conclusion, new product forecasting stands as a critical driver in actively shaping effective inventory management and enhancing overall business performance. It is essential for businesses to proactively recognise their unique attributes when devising forecasting strategies.

If you’re looking to refine your inventory planning and drive business success through accurate forecasting, feel free to contact us at info@truegradient.ai for a personalised consultation, and together, we can optimise your supply chain.