Case Study
A $10B food distributor with 50+ warehouses faced $150M annual losses due to fragmented data across 16 systems, causing spoilage, stockouts, and FDA violations.
DataCatalyst’s lean team implemented a unified MDM platform with real-time monitoring, connecting 300+ suppliers and 100+ retail partners through our Accelerate methodology.
The MDM solution supported $30M annual savings, 20% improved FDA compliance, higher order accuracy, and 25% faster cross-docking, transforming data management from a liability into a strategic asset.
A $10 billion food distributor with 50+ warehouses, operating in an omnichannel environment (warehouse/store/eCommerce), was experiencing significant business challenges.
They managed relationships with over 300 suppliers and had more than 100 retail partners connected through API gateway integration. Their business relied on 16 different source systems, including SAP S/4 ERP, BY Warehouse Management System, time-series IoT databases, supplier portals, spreadsheets, and bespoke databases built on IBMi and SQL Server.
Fragmented product data was creating massive operational problems. Inconsistent Global Trade Item Numbers (GTINs) and inadequate shelf-life tracking for perishable goods were resulting in FDA violations.
These data quality issues were causing nearly $150 million in annual losses from expired and discarded food through spoilage and stockouts of seasonal items.
Before engaging with DataCatalyst, the distributor struggled with manual processes and spreadsheets to manage their complex inventory across multiple warehouses. Their existing systems were siloed and disconnected, making it impossible to gain a unified view of product data.
DataCatalyst deployed a lean team of experts who worked with over 120 stakeholders from various departments, including Operations, Procurement, Compliance, Sales/Merchandising, and Supply Chain.
Our team conducted a comprehensive assessment of the distributor’s data landscape, identifying the critical pain points and opportunities for improvement.
We leveraged our proprietary Accelerate methodology to deliver incremental value through MVPs (Minimum Viable Products), achieving results in weeks rather than months.
DataCatalyst developed a comprehensive MDM (Master Data Management) platform with four key components:
Our small but highly experienced team was uniquely qualified to deliver this solution due to our deep expertise in food distribution data challenges and experience implementing MDM solutions to address similar problems for other clients.
Implementing the solution required significant changes to the distributor’s data management practices. Staff needed to adopt new workflows and processes resulting in significant change for teams accustomed to working with their own siloed systems.
Our focus on speed and business value accelerated adoption as employees quickly saw the benefits of the new centralized system. Despite the complexity of integrating 16 source systems and aligning data standards across 300+ suppliers, our efficient approach enabled rapid time-to-value, with initial benefits realized within the first implementation phase.
The MDM implementation supported impressive, measurable business results:
These outcomes were made possible by a data transformation that was achieved in a fraction of the time typically required for projects of this scale.
With their new unified data platform, the food distributor is now better positioned to handle seasonal demand fluctuations, maintain regulatory compliance, and optimize inventory across their network. The improved data quality enables more accurate forecasting and planning. In particular, it enables predictive analytics to forecast demand spikes for high-turnover and seasonal items, drastically reducing stockouts
The business now has a foundation for future innovation, including AI-driven inventory optimization and enhanced supplier collaboration. Ultimately, the transformation from fragmented to unified data management has positioned the company for sustainable growth and competitive advantage.