The project team implemented a Data-Driven Automation Framework leveraging AI, Data Engineering, and Visualization technologies to simplify, standardize, and automate the vendor budgeting process.
Key components of the solution included:
- Automated data integration via Dataiku A custom logic was developed in Dataiku to process vendor reports, extract invoice processing system data, and validate mandatory fields against invoice records. The processed data was automatically stored in data warehouses like Snowflake for centralized access.
- AI-enabled translation and standardization Artificial Intelligence models were used to translate invoices received in multiple languages and convert currencies into English and standard financial units. This eliminated manual translation previously handled by clinical supply team.
- Intelligent data quality and notification system Automated system checks identified missing or duplicate invoices and incomplete submissions. Notification emails were automatically sent to SMEs and vendors to resolve discrepancies promptly.
- Anomaly detection and ML insights Machine Learning models were implemented to detect anomalies such as duplicate, incorrect, or outlier invoices, ensuring accuracy and audit readiness.
- Integration across enterprise systems Data pipelines were integrated across order placing, processing, and tracking systems, and CTMS systems to ensure complete and accurate financial data flow.
- Dashboard and KPI enablement The analytics team provided KPI definitions and enabled interactive dashboards to visualize category-wise spend, monitor trends, and support data-driven decision-making at the program and protocol level.

