Driving Operational Excellence Using Machine Learning

By Tobias Kutscher, Senior Data Scientist Biopharma, HP BioP Operations Network Management; and Dr. Alexander Krauland, Head of Digital Transformation Unit, HP BioP Operations Network Management

This content was created by Boehringer Ingelheim BioXcellence™

Boehringer Ingelheim’s “In-House Data Science Team” has reaffirmed its long-standing commitment to Operational Excellence by developing an innovative, automated variable observation tool. This cutting-edge tool exemplifies the organization’s dedication to leveraging advanced technology for optimizing biopharmaceutical manufacturing processes.

A recent milestone in this endeavor involved the application of machine learning to analyze and uncover correlations between higher afucosylated glycan levels and critical process parameters, including osmolality, ammonia, and glucose. This sophisticated machine learning application has proven invaluable in rapidly identifying potential influencing factors within the highly complex environment of biopharmaceutical production.

The ability to pinpoint these relationships with speed and precision not only enhances process understanding but also supports more effective decision-making, enabling proactive adjustments to manufacturing operations. This approach underscores Boehringer Ingelheim’s strategy of harnessing the power of smart data utilization to drive innovation and efficiency.

The following whitepaper explores this achievement in detail, demonstrating how the integration of machine learning and advanced analytics in biopharmaceutical manufacturing exemplifies Operational Excellence. Using the case study of afucosylated glycan levels, the whitepaper highlights the transformative impact of data-driven methodologies on improving process outcomes and ensuring the highest standards of quality and reliability.