Articles | Open Access | https://doi.org/10.55640/ijdsml-05-01-17

GMP Compliance and MES: Strategies for Automated Compliance Assurance

Shriprakashan. L. Parapalli , Emerson Automation Solutions, Durham, NC- USA BioPhorum, The Gridiron Building, 1 Pancras Square, London, NIC 4AG UK International Society for Pharmaceutical Engineering (ISPE), 6110 Executive Blvd, North Bethesda, MD 20852, USA MESA International, 1800E.Ray Road, STE A106, Chandler, AZ 85225 USA

Abstract

Current Good Manufacturing Practice (cGMP) regulations emphasize stringent control over production processes, personnel, equipment, and documentation in pharmaceutical manufacturing. Meeting cGMP requirements involves meticulous recordkeeping, comprehensive quality control, and robust oversight—processes that are prone to human error when relying on traditional, paper-based approaches. Against this backdrop, Manufacturing Execution Systems (MES) offer a powerful solution for managing production workflows and ensuring regulatory adherence. This paper explores the integration of MES in a cGMP environment to automate compliance assurance and details key strategies including automated validation, data integrity assurance, QMS integration, regulatory reporting, training and competency tracking, risk-based automation, and AI-driven continuous improvement. Through literature reviews and case study analyses, we identify critical process elements where MES adds the most value, such as reducing human error, streamlining documentation, and facilitating digital audit trails. The findings suggest that adopting MES not only enhances operational efficiency but also enables a proactive approach to regulatory compliance, positioning organizations to adapt quickly to evolving industry standards.

Keywords

cGMP, GMP Compliance, Manufacturing Execution System, Quality Control, Pharmaceutical Manufacturing, Automated Compliance, Regulatory Adherence, Electronic Recordkeeping, Process Control, Data Integrity

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GMP Compliance and MES: Strategies for Automated Compliance Assurance. (2025). International Journal of Data Science and Machine Learning, 5(01), 170-181. https://doi.org/10.55640/ijdsml-05-01-17