Predictive Analytics Platform for Global Pharma

Objective

Build a machine learning-powered analytics platform to improve supply chain demand forecasting accuracy and reduce pharmaceutical waste across global operations.

The Challenge

The client was relying on spreadsheet-based forecasting that resulted in frequent stockouts and overstock situations, costing millions annually. The existing data was fragmented across 12 regional systems with inconsistent formats.

Our Solution

We developed a custom predictive analytics platform using Python, TensorFlow, and PostgreSQL. The system ingests data from all regional sources via automated ETL pipelines, normalizes it, and feeds it into ensemble ML models that forecast demand at SKU level with 45% better accuracy than the previous approach.

Our Approach

Started with a 4-week discovery phase to map data sources and define KPIs. Then built a proof-of-concept model with 3 months of historical data. After validating accuracy improvements, we developed the full production system with a React dashboard for supply chain managers.

Technologies Used