Samsung Biologics Taps AI for CDMO

Samsung Biologics is operationalizing its AI strategy through a partnership with Samsung SDS, implementing a proof-of-concept that leverages the Brity Automation platform and a proprietary large language model (SR-LLM). This system uses Retrieval-Augmented Generation (RAG) technology to search and retrieve information from complex Standard Operating Procedure (SOP) documents, including tables and images, to speed up responses to regulatory agency and client audits. The company's new Plant 5, which began full operation in April 2025, serves as a testbed for these digital initiatives, employing AI and digital twin technologies to automate manufacturing processes and enhance quality management. This "smart factory" approach also utilizes autonomous mobile robots and electronic manufacturing records to improve data integrity and reduce process errors. Underpinning this is a centralized data management system designed to create a single source of truth by integrating historical and real-time data from disparate sources. Machine learning models are applied to this data to predict production yield and quality, while real-time monitoring dashboards and multivariate data analysis provide process scientists with immediate insights. The business case for such AI integration in the CDMO space is compelling; one real-world case study demonstrated a 1.5% yield increase and a 2% reduction in the Cost of Goods Sold (COGS) within three months of implementation. Broader analyses suggest AI can reduce cumulative manufacturing cycle times by over 20% and cut overall manufacturing costs for customers by as much as 50%. Beyond the factory floor, AI algorithms are reshaping the biopharma supply chain by analyzing historical data to predict demand, optimize inventory levels, and streamline logistics. This can lead to a 15% increase in forecast accuracy and a 2-3% decline in total supply chain costs. Key challenges to implementation across the industry include the need for large, high-quality datasets to train and validate AI algorithms effectively. Navigating the stringent regulatory requirements for pharmaceutical manufacturing and the "black box" nature of some complex AI models also remain significant hurdles for CDMOs to overcome.