ABSTRACT:
The pharmaceutical manufacturing industry is rapidly moving toward digital transformation and advanced quality management systems to improve product quality, manufacturing efficiency, and regulatory compliance. Data-driven quality improvement has become an important approach for identifying process variability, reducing human errors, minimizing deviations, and improving overall manufacturing performance. Modern pharmaceutical industries generate large amounts of production and quality-related data through manufacturing execution systems (MES), laboratory information management systems (LIMS), enterprise resource planning (ERP), process analytical technology (PAT), and automated manufacturing equipment. The proper analysis and utilization of this data help industries in making better decisions, improving process control, and maintaining product consistency. Advanced technologies such as artificial intelligence (AI), machine learning (ML), big data analytics, cloud computing, and Industry 4.0 tools are increasingly being implemented in pharmaceutical manufacturing environments. The regulatory authorities it is including the USFDA, WHO, and ICH emphasize data integrity, quality risk management (QRM) and continuous improvement as essential elements of the modern pharmaceutical quality systems. This review article discusses the concept of data-driven quality improvement in pharmaceutical manufacturing environments, major data sources, digital technologies, statistical tools, regulatory perspectives, benefits, challenges, and future opportunities.