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April 30 - May 1, 2025
North Javits Center | New York City

Case Study Roundup: Optimizing Supply Chain Efficiency with Data and AI

In March 2020, the general public got a crash course in how important supply chains were to peoples’ daily lives. While businesses understood what unexpected disruption could cause, the immediate scarcity of products that had never been in short supply (famously including toilet paper, computer chips used in the auto industry, ketchup and lumber for housing, among many others) created a vivid and unforgettable example. If they hadn’t already, organizations immediately looked for data and AI-driven technology solutions that would optimize supply chain operation and minimize future disruption.

The following are real-world examples of the effect these solutions have had on how businesses source their materials and get their products to their customers.

Increasing supply chain efficiency through reliable, automated forecasting. Services provider LatentView Analytics built an automated forecasting system for a major PC, printer and imaging products maker, leveraging Azure Databricks and Apache Spark. The company increased computation by 8x and spent five time less on infrastructure cost.

Updated infrastructure enables international expansion. Rangespan, a U.K.-based supply chain services provider worked with AWS to increase its capacity to serve suppliers and retailers in Europe and the U.S. The company uses a range of AWS capabilities to provide customers with order management, customer protection and product data catalogs as an end-to-end supply chain service.

Fresh food supplier requires optimal logistics. Swiss food company Coop Group has 14 distribution centers serving 2,500 retail stores. The company worked with Hitachi Vantara to simplify its supply chain infrastructure and improve storage performance of the massively growing data volumes it uses to ensure logistics processes.

New analytics architecture increases data access performance by a factor of 10. Amazon’s Supply Chain Finance Analytics Team was charged with improving the business intelligence they provided to the company. The unit worked with Dremio to accelerate query time and provide a unified view of their data.

In a separate case study, Dremio worked with German chemical and consumer goods company Henkel to eliminate siloes separating data sources integral to supply chain management and optimize access to unstructured databases. The company migrated to a platform based on Microsoft Azure Data Lake Storage (ADLS), Dremio, Databricks and Tableau. The changes led to a 10% increase in efficiency, cost savings and a reduction in query time from 3-4 minutes to eight seconds.