Case Study: a Fortune 500 global groceries and merchandise retailer achieves 2x better resource utilization with Impetus demand-supply chain forecasting

a Fortune 500 global groceries and merchandise retailer improves warehousing utilization by 50% with Impetus

The customer, a Fortune 500 global groceries and merchandise retailer, faced a challenge in its demand-supply chain forecasting. It sought to migrate from a legacy system to a modern, Spark-based architecture to improve the accuracy of its short and long-range product forecasts, enhance overall resource utilization, and better understand customer buying behavior. To address this, they partnered with vendor Impetus.

Impetus implemented an ML-powered forecasting solution built on a big data platform using Hadoop and Spark. This solution provided accurate forecasts for all store products, incorporated various interventions like weather and promotions, and optimized processing jobs. The results for the retailer were significant, including a 50% improvement in warehousing utilization and management and a 2x reduction in resource utilization, all while ensuring jobs were completed within their SLAs.