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AI- AND ML-DRIVEN INSIGHTS ON AWS CLOUD DELIVERS 15% REDUCTION IN FORECASTING ERROR FOR THE WORLD’S LARGEST PHARMACEUTICAL COMPANY TO ACHIEVE USD 600 MN INCREMENTAL SALES

CLIENT

 

Our client is one of the world’s largest pharmaceutical company producing medicines and vaccines for a wide range of medical disciplines, including immunology, oncology, cardiology, endocrinology, and neurology.

BUSINESS CHALLENGE

The client was facing the challenge of different demand patterns of drug combinations for each country. This inconsistent demand prediction system that was modified by local sales teams based on their experience and market knowledge provided below par results.

The client was looking for a solution that would enable them to achieve seamless and faster deployment of new changes and models into production.

SOLUTION

 

We developed an MLOps-based solution for automated ML lifecycle through an automatic model selection framework that would identify the best model combination for each demand category. The automatic model selection framework would

  • Identify the best model combination for each demand category
  • Ensure seamless deployment of new changes and models arising due to regional and market factors
  • Allow for faster deployment of changes into production

 

ARCHITECTURE

 

BUSINESS BENEFITS

 

Our ML pipeline enabled rapid pushing of new approaches into production, and achieved the following winning outcomes for the pharmaceutical giant:

15% reduction in forecast error (root mean square error)

Reduced costs by ~2.5%

Boosted sales revenue by 1.5% (~$600 million)