DOI: https://doi.org/10.63345/ijrmp.org.v10.i4.2
Harshit Bedi
Panchkula, Haryana, India
Abstract
Digital twin technology—virtual replicas of physical processes—has rapidly emerged as a transformative innovation in many industrial sectors, including pharmaceutical manufacturing. This paper investigates the impact of digital twin technology on manufacturing efficiency within the pharmaceutical industry. It examines how digital twins facilitate real-time monitoring, process optimization, and predictive maintenance, thereby reducing downtime and enhancing product quality. A detailed review of literature up to 2020 is provided, outlining the evolution of digital twin applications, integration challenges, and the potential for cost reduction. A mixed-methods approach was employed, combining case study analysis, simulation models, and expert interviews to evaluate the performance gains and efficiency improvements in pilot projects. Results indicate that pharmaceutical plants employing digital twin frameworks observed improvements in process reliability and a significant reduction in production errors, which translates to lower operational costs and higher compliance with regulatory standards. Moreover, the real-time data feedback loop inherent to digital twin systems supports agile decision-making and fosters a culture of continuous improvement. The study concludes that while the initial investment in digital twin infrastructure can be high, the long-term benefits in terms of enhanced efficiency and quality assurance are considerable. Future research should focus on scaling these models across various pharmaceutical production settings and on integrating advanced AI analytics to further refine process simulations.
Keywords
Digital Twin, Pharmaceutical Manufacturing, Efficiency, Process Optimization, Predictive Maintenance, Simulation
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