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CASE STUDY

Automation of budget allocations and projections to optimize vendor spend and budget accuracy for clinical supply chain teams

industry-iconCLIENT :Confidential
industry-iconINDUSTRY :Pharmaceutical
industry-iconDURATION :8 months
CLIENT :
Confidential
INDUSTRY :
Pharmaceutical
DURATION :
8 months

Business case

The operations team related to clinical supply chain managing procurement, packaging, labelling, transportation, and storage of clinical trial materials across multiple countries was facing challenges. They were having difficulties in manually monitoring and reconciling budgets versus actual expenses across multiple vendors. The absence of a standardized and automated financial workflows made it difficult to track spend accurately and forecast future costs with confidence.

Key business challenges

An internal assessment revealed multiple pain points affecting financial visibility and efficiency:

  • Disparate vendor formats: Vendors submitted invoices in varied formats and languages, requiring manual consolidation and validation for reporting.
  • Lack of standardization: There was no uniform method to capture and categorize purchase order data for cost types such as transportation, brokerage, and customs fees.
  • Regional complexity: Vendor reports from different countries required manual translation and conversion of currencies.
  • Manual effort: Finance teams relied heavily on Excel-based reporting, spending considerable time compiling and validating vendor data with invoice processing systems and other systems instead of focusing on analysis and strategic planning.

These challenges led to delayed financial reporting, inconsistent data, and limited visibility into true budget utilization. As the organization expanded its vendor network globally, the complexity of spend monitoring and vendor management increased substantially.

The client sought to streamline its vendor budget optimization and projecting process through automation, with the objective of:

  • Reducing manual workload involved in processing vendor invoices and purchase orders.
  • Standardizing vendor data submission to improve data consistency.
  • Enhancing visibility into budget versus actual expenditure at category and program levels.
  • Enabling proactive spend forecasting to support data-driven financial planning. 

Our solution

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.

Benefits

  • 90% reduction in manual effort: Automated data extraction, translation, and validation drastically reduced manual processing time.
  • Improved data quality: Centralized data validation and anomaly detection enhanced accuracy and consistency across vendor reports.
  • Enhanced operational efficiency: End-to-end automation reduced reporting turnaround time from days to hours.
  • Real-time spend visibility: Unified dashboards provided complete transparency into budget versus actual expenditure.
  • AI-driven forecasting: Predictive insights enabled proactive spend forecasting, empowering finance teams with strategic foresight.
  • Cross-functional collaboration: A single, standardized data foundation improved collaboration between Finance, Operations, and Procurement teams. 

Results

Pharma cost allocation
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