Case Study: a Canadian clothing retailer achieves 92% demand forecasting accuracy with instinctools

Canadian Clothing Retailer boosts demand forecasting accuracy to 92% with instinctools

The Canadian clothing retailer, an ecommerce company, faced significant challenges with its traditional Excel-based demand forecasting model, which suffered from low long-term accuracy and led to frequent stockouts and overstock. To overcome these limitations, they partnered with *instinctools to develop and implement a machine learning-powered forecasting solution using Azure ML.

*instinctools developed a recurrent neural network (RNN) model on Azure ML, which was integrated with Power BI for visualization. This solution automated the forecasting process and incorporated a wide range of internal and external data sources. The project was a major success, increasing long-term forecasting accuracy from 23% to 92%, reducing stockouts to 10%, and keeping overstock below 5%.