Case Study: Seculus achieves more accurate inventory forecasting with Stradigi AI

Seculus uses Machine Learning in Kepler to discover new insights in their data

Seculus, a watch manufacturer, faced significant challenges in accurately predicting inventory needs for its extensive catalog of over 1,400 SKUs. Their traditional demand forecasting models were inadequate, and they lacked the internal AI or machine learning resources to process their large volume of data effectively. They partnered with Stradigi AI and its Kepler platform to develop a solution for this problem.

Stradigi AI implemented Kepler's AutoML technology to build and iterate on predictive models using the company's sales and feature data. This allowed Seculus to quickly identify the factors driving product demand and make operational changes. The results were substantial; Seculus produced its first AI model within a week and developed over 50 models in the first year. This new capability enabled them to reduce their total SKUs, fine-tune inventory requirements, and make more accurate, data-driven decisions, significantly improving forecasting accuracy.

Seculus

Oswaldo Moreira Neto

Director of Marketing & Innovation