In a constantly expanding global market, where worldwide container traffic surged nearly fourfold between 2000 and 2019, predicting demand becomes crucial for a company’s optimal growth. Particularly, the fashion sector is highly unpredictable, characterized by: seasonality, cultural influences, and fashion trends. These factors make it challenging to forecast footwear demand between seasons. In recent years, various algorithms for demand prediction have been explored, generally classified into three main categories: statistical, artificial intelligence and hybrid algorithms, each with its unique characteristics. The goal of this work is to predict the sales of a specific shoe model for a company. To achieve this, some Key Performance Indicators (KPIs) have been established that provide a comprehensive description of the prediction made. Holt-Winters has been chosen as the prediction algorithm, which achieves an accuracy rate of 91.6% on the months that involve more than the 75% of the annual sales. Its proficiency in this regard is evident and provides a compelling glimpse into how the market will evolve in the future. In summary, the utilization of Holt-Winters holds significant relevance within a globalized setting, particularly when rapid and fluctuating predictions are necessary. This paper is intended to be the beginning of a more extensive article in which another prediction model is used, a relational database structure is proposed to store the predictions and where the business performance and the pattern of the time series related to the sales of the predicted shoe model are analyzed more extensively.