Abstract: The pharmaceutical manufacturing sector is going through a significant shift as it adopts sophisticated analytics and predictive maintenance as drivers of efficiency and innovation. This research investigates how regulatory changes, technical developments, and sustainability requirements are converging to shape the future of pharmaceutical production. Manufacturers are empowered by predictive maintenance, a transition from reactive to proactive methods, to reduce downtime, maximize resources, and uphold regulatory compliance. Regulatory organizations are becoming more helpful, offering precise rules for smooth implementation.
With Industry 4.0 technologies providing real-time insights for operational efficiency and quality control, data-driven excellence emerges as a persistent quest. Digital twins revolutionize simulation and monitoring, reducing physical testing and hastening product development. Collaborations with IT companies have hastened the uptake of innovations. Comprehending futuristic breakdown and anticipating the health line of manufacturing, a model called PharMTrans has been built in this study, employing a bi-linear outcome technique with four analysis phases.
A data-rich, proactive, and sustainable approach will guide pharmaceutical manufacturing in the future. The industry is well-positioned to design a future where efficiency, quality, and sustainability intersect, ensuring pharmaceutical production remains a foundation of global health and well-being. This is accomplished through sophisticated analytics and predictive maintenance.

Keywords: Pharmaceutical Manufacturing, Data-driven, Advanced Analytics, Machine Learning, PharMTrans.

Works Cited:

Comfort Iyanda, Kai Yang " Advanced Analytics and Predictive Maintenance in Pharmaceutical Manufacturing ", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 10, no. 11, pp. 9-18, 2023. Crossref https://doi.org/10.17148/IARJSET.2023.101102

| DOI: 10.17148/IARJSET.2023.101102