Data-as-a-product for supply chain analytics reduces inventory costs by 15%
Sigmoid developed a data mesh architecture for modernizing enterprise data to enable next-gen analytics use cases that helped the client improve sales and inventory.
The client is a leading American food manufacturer of 65 brands, available across supermarkets, food service centers and restaurants. Inventory management was significantly impacted by the lack of quality data and information transfer between multiple user systems. The data from disparate sources needed to be consolidated to deliver near real-time insights for demand, supply and capacity optimization.
Sigmoid modernized the data architecture by enabling data-as-a-product for multiple business domains with greater ownership to the users. The decentralized architecture allowed interactions between multiple data hubs residing within Databricks, Spark, Snowflake etc. Data from 30+ source systems such as SAP, Blue Yonder, RightAngle, Oracle Transportation Management, sensors, and IoT devices etc. flowed between different data zones built across Snowflake and ADLS. Data from multiple Centers of Excellence (plan, procure, manufacture, deliver and sell) was centrally integrated for scalability and cross-domain consumption. The data mesh was driven by DataOps best practices to ensure scalable pipelines with code reusability, modularization, and maintenance in real-time.
Sigmoid’s data mesh architecture enabled data products and facilitated domain-driven ownership. Better visibility and real-time access to actionable insights improved forecasting. With a decentralized approach, the client was able to reduce their time-to-market.
in on-time product delivery
data usability across domains
advanced analytics use-cases identified