Next-Gen Life Sciences Manufacturing: A Scalable Framework for AI-Augmented MES and RPA-Driven Precision Healthcare Solutions

Abstract

ABSTRACT: The convergence of precision medicine and advanced manufacturing necessitates a paradigm shift in life sciences production. Traditional Manufacturing Execution Systems (MES) and manual processes struggle with the complexity, data volume, and personalization requirements of next-generation therapies like Cell and Gene Therapies (CGTs) and stratified biologics. This study proposes and validates a scalable framework integrating Artificial Intelligence (AI) and Robotic Process Automation (RPA) within a next-generation MES to enable precision healthcare manufacturing. Utilizing a mixed-methods approach, we developed a framework comprising an AI-augmented MES core for real-time process control and predictive analytics, an RPA layer for automating high-volume, rule-based tasks, and a digital thread for seamless data integration from patient to product. A quantitative simulation of a CGT manufacturing process demonstrated a 32% reduction in batch failure rates and a 25% decrease in release times. Concurrently, a qualitative case study with industry partners confirmed significant improvements in operational agility, regulatory compliance, and scalability. The findings indicate that the synergistic application of AI and RPA can overcome critical bottlenecks in precision medicine manufacturing, leading to more robust, efficient, and patient- centric production systems. This research provides a validated blueprint for the digital transformation of life sciences operations