A drug manufacturer achieved 67% Reduction in Setup Time and $900K costs Savings

Challenge

A generic drug manufacturer wanted to predict the quality of the tablet based on the compressor machine parameters and input raw material quality attributes.

Solution

Gramener used one year of historical IOT data from the compressor, input material quality attributes, and tablet quality attributes used for classification models (SVM, Random Forest, Decision Tree, Ada Boost, XG Boost)

Impact

Gramener optimized a drug manufacturer's machine setup, reducing manual interventions and achieving a 67% reduction in setup time.
We provided this solution by analyzing historical compressor data and material and tablet quality attributes with advanced machine learning models.
The digital twin enhanced tablet quality and operational efficiency, saving $900k annually and 67% reduction in setup time.