ejedsml Open Access Journal

European Journal of Emerging Data Science and Machine Learning

eISSN: Applied
Publication Frequency : 2 Issues per year.

  • Peer Reviewed & International Journal
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Optimizing Digital Integration of Manufacturing Execution Systems to Enhance Quality and Compliance in the Pharmaceutical Sector

1 University of Manchester, United Kingdom
2 University of Tunis, Tunisia

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Abstract

The pharmaceutical industry has undergone profound shifts propelled by digital transformation, yet the integration of Manufacturing Execution Systems (MES) remains a cornerstone for enhancing operational quality, compliance, and process performance. This research article elucidates the theoretical foundations, operational mechanisms, and strategic implications of MES adoption within pharmaceutical manufacturing environments. Placing an emphasis on business payback beyond return on investment (ROI), this analysis synthesizes multidisciplinary perspectives on MES architectures, regulatory challenges, quality management integration, and digital transformation frameworks. Drawing upon advancements in Industry 4.0, information systems theory, and organizational change paradigms, the work investigates how MES facilitates adaptive quality systems, strengthens compliance with stringent regulatory standards, and enables real-time decision-making. The inquiry foregrounds the strategic deployment of MES as an enabler for pharmaceutical firms to reconcile complex manufacturing requirements with digital operational capabilities. By critically reviewing extant literature, this article exposes a significant gap in empirical analyses linking MES implementation outcomes with measurable performance improvements, suggesting key pathways for future inquiry and practical adoption strategies.


Keywords

Manufacturing Execution System, Digital Transformation, Pharmaceutical Quality Management, Regulatory Compliance

References

1. Shweta Bhanda, “Challenges in Digital Transformation in the Manufacturing Industry and How to Address Them,” Uneecops, 2024.

2. Lalitkumar K. Vora et al., “Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design,” Pharmaceutics, 15(7):1916, 2023.

3. Byabazaire, J., O’Hare, G., & Delaney, D. (2020). Using trust as a measure to derive data quality in data shared IoT deployments. In 2020 29th International Conference on Computer Communications and Networks (ICCCN) (pp. 1–9). IEEE.

4. Amicis, F. D., Barone, D., & Batini, C. (2006). An analytical framework to analyze dependencies among data quality dimensions. In Proceedings of the International Conference on Information Quality (ICIQ 2006), Cambridge, MA, USA, 10–12 November 2006.

5. Olivia McDermott et al., “Pharma Industry 4.0 Deployment and Readiness: A Case Study within a Manufacturer,” The TQM Journal, 36(9), 2024.


How to Cite

Optimizing Digital Integration of Manufacturing Execution Systems to Enhance Quality and Compliance in the Pharmaceutical Sector. (2025). European Journal of Emerging Data Science and Machine Learning, 2(01), 1-6. https://parthenonfrontiers.com/index.php/ejedsml/article/view/400

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