About HAVI
HAVI is a global supply chain management company that supports some of the world's largest food service organizations with end-to-end supply chain solutions.
Operating across multiple regions and managing thousands of suppliers, products, and inventory items, HAVI depends heavily on accurate and consistent operational data to manage sourcing, logistics, supplier relationships, and performance reporting at scale.
As the organization continued expanding globally, the complexity of managing supplier and product data across different systems and regions increased significantly.
The Business Challenge
HAVI's biggest challenge was not simply data volume; it was data standardization and integration across regions. Supplier, product, and item data existed across multiple systems, each using different structures, formats, and business definitions.
Creating a reliable global supplier performance report required significant manual effort. Teams had to reconcile regional data sources, apply business rules manually, and rebuild reporting logic repeatedly for different operational needs.
The organization technically had access to the data, but producing consistent and trusted reporting outputs required too much engineering and analyst effort to be sustainable. Legacy ETL pipelines also lacked the flexibility needed to support evolving supplier KPIs and global reporting requirements. As supply chain operations expanded, the manual approach became increasingly difficult to maintain.
Legacy Environment
Before modernization, HAVI's environment included:
- Multiple regional systems containing supplier, product, and inventory data
- Legacy ETL pipelines requiring extensive maintenance and customization
- Spreadsheet-driven reconciliation workflows across regions
- No unified enterprise supplier data model
- High engineering effort required for integrated analytics and reporting
- Inconsistent data definitions across supplier and product domains
The fragmented environment made global visibility and scalable reporting difficult to achieve efficiently.
Why Change Was Needed
Leadership teams needed faster and more reliable visibility into supplier performance across regions. Supply chain and procurement teams also needed consistent reporting to support sourcing decisions, supplier management, and operational planning.
The existing manual reconciliation process created delays, inconsistencies, and operational bottlenecks. The organization needed a governed and automated data foundation capable of applying business rules consistently across all supplier and product data without relying on constant engineering intervention.
The OpenOntos Approach
OpenOntos modernized HAVI's supply chain data environment by building a governed Azure Databricks data lake designed specifically for global supplier and product analytics. The implementation replaced legacy ETL pipelines and manual reconciliation processes with automated and scalable integration workflows.
The goal was to create a centralized analytics foundation where supplier performance reporting could run automatically and consistently across all regions. A major focus of the engagement was building integrated enterprise data models that connected supplier data, product information, and operational KPIs into a single trusted framework.
Migration & Modernization Strategy
The modernization strategy covered the complete data lifecycle from ingestion through enterprise analytics delivery. Key implementation activities included:
- Ingesting supplier, product, and item data from multiple regional systems into Azure Databricks
- Standardizing supplier and product definitions across all operational regions
- Building automated pipelines to apply HAVI's business rules and KPI logic during ingestion
- Creating Gold Layer models for enterprise supplier performance reporting
- Replacing legacy ETL processes with governed and maintainable automated pipelines
- Establishing a scalable analytics foundation for future supply chain intelligence initiatives
The overall approach focused on reducing operational complexity while improving reporting consistency across regions.
AI & Automation Role
AI-assisted profiling was used to analyze source system structures and identify the mapping relationships required to create standardized enterprise data models. Automation also played a major role in applying supplier performance KPIs directly within the pipeline layer.
Instead of manually reconciling supplier data for every reporting cycle, the platform automatically applied business logic and generated reporting outputs consistently across regions. This shifted the organization from manual integration workflows to automated and repeatable data operations.
Technical Transformation Highlights
The implementation delivered several major improvements across HAVI's supply chain analytics environment:
- Multi-region supplier, product, and inventory data standardized into a unified enterprise model
- Automated KPI and business rule application embedded directly into data pipelines
- Azure Databricks established as the centralized platform for supply chain analytics
- Legacy ETL workflows replaced with scalable and governed alternatives
- End-to-end automation of supplier performance reporting across global operations
The final platform created a much more maintainable and scalable reporting environment for the organization.
Business Outcomes
The modernization delivered measurable operational improvements across supply chain reporting and analytics:
- Supplier performance reporting automated across global regions
- Significant reduction in engineering effort required for integration and reporting workflows
- Faster access to trusted supplier performance insights
- Standardized supplier and product definitions implemented enterprise-wide
- Accelerated delivery timelines compared to traditional engineering approaches
- Improved reliability and consistency across operational reporting
Operational Impact
Supply chain and procurement teams gained access to automated and consistent supplier reporting that previously required weeks of manual reconciliation work. Leadership teams could now identify supplier performance trends, operational risks, and regional performance gaps much faster.
The organization also improved its ability to make sourcing decisions, manage supplier relationships, and monitor operational performance using trusted enterprise-wide data. By removing repetitive reconciliation work, engineering teams could shift their focus toward building new analytics capabilities instead of maintaining legacy integration processes.
Key Benefits Achieved
- Unified and standardized supplier and product data across all operational regions
- Automated supplier KPI reporting replacing manual reconciliation processes
- Governed Azure Databricks data lake serving as the single source of truth
- Reduced engineering overhead and ETL maintenance effort
- Faster and more reliable supply chain reporting
- Scalable foundation supporting future analytics and operational intelligence initiatives
Conclusion
HAVI's modernization initiative demonstrates the value of standardizing and automating supply chain data operations in a large and globally distributed enterprise environment. The organization already had the operational data it needed; the challenge was accessing and integrating it efficiently across regions.
OpenOntos helped simplify that complexity by creating a governed and automated analytics foundation capable of supporting supplier performance reporting at scale. The result was faster access to trusted operational intelligence, reduced manual effort, and a more scalable platform capable of supporting the evolving needs of a global supply chain business.