Strategy and Solutions
The client is a global supply chain and retail technology company managing complex operations across multiple regions. With data distributed across more than 200 sources—including ERP systems, third-party logistics providers, warehouse management systems, and supplier feeds—the organization struggled to achieve unified visibility and real-time decision-making. To address these challenges and modernize its data infrastructure, the company partnered with Zymr.
The organization operated with fragmented data systems, making it difficult to consolidate insights across supply chain operations. Data was spread across ERP platforms, logistics providers, warehouse systems, and supplier feeds, leading to delays in reporting and limited visibility into real-time inventory levels.
The existing architecture relied on a combination of traditional data warehouses and data lakes, which increased operational complexity and costs. Batch processing resulted in reporting delays of up to 24 hours, impacting decision-making for inventory management, demand forecasting, and logistics optimization.
Additionally, the lack of real-time data integration limited the company’s ability to implement advanced analytics and machine learning initiatives. The organization needed a scalable, cloud-native data platform capable of supporting real-time analytics, reducing infrastructure costs, and enabling future innovation.
Zymr designed and implemented a modern lakehouse architecture that unified data across all sources and enabled real-time analytics. This transformation significantly improved operational efficiency, reduced costs, and laid the foundation for advanced data-driven capabilities.
Zymr implemented a cloud-native lakehouse architecture tailored to the client’s supply chain and analytics needs, enabling real-time data processing and scalable insights.
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