Case Study: a leading alcoholic beverages company achieves faster, more accurate demand forecasting with Sigmoid

Leading Alcoholic Beverages Company cuts data run-time 20x with Sigmoid

A leading alcoholic beverages company faced the challenge of estimating its market share to predict demand and optimize its global supply chain. With limited internal datasets and a need to model around 200,000 time series, the company required a solution to perform this complex analysis quickly. Sigmoid was engaged to build an ML-based demand forecasting solution to address this.

Sigmoid developed an agile forecasting solution by pulling data from Azure Data Lake, preprocessing it, and training several ML and deep learning models on Azure Kubernetes Service. The entire framework was built on Kedro for scalability. This solution from Sigmoid resulted in a 20x faster data run time, a 5x reduction in forecasting costs, and a 3x reduction in compliance and overhead costs, enabling more accurate demand prediction and optimized inventory management.

Leading Alcoholic Beverages Company