Digital Twin based Data Pattern Analysis for IoT-Based Pharmaceutical Production Monitoring
Narne Hemanth Chowdary , Prasad Reddy P.V.G.D., Suresh Chittineni
Open Access
Volume - 8 • Issue - 1 • march 2026
Abstract
A major step forward in pharmaceutical production has been the merging of Artificial Intelligence (AI) with the Internet of Things (IoTs), which successfully connects the digital and physical realms. Improving the production process and quality control through the integration of AI algorithms into IoT sensors leads to higher overall efficiency. Digital Twins (DT) are made possible by the creation and implementation of new Industry 4.0 technologies, which help to make manufacturing smarter and more agile. DTs are digital representations of real-world systems that mimic their operation and dynamics. Complete DT implementation in pharmaceutical production has not yet occurred, despite the pharmaceutical sector is currently undergoing a digital transformation to incorporate industry 4.0 and has adopted Quality-by-Design (QbD) programs. Therefore, it is crucial to assess the pharmaceutical industry's progress in implementing DT solutions. Data obtained from digital twins can also provide a full image of a product or process's lifetime, which can help with supply chain management, product quality control, and workflow optimization for manufacturing individual parts. This research proposes a Digital Twin enabled Data Pattern Analysis with Diminutive Difference Trigger using Probabilistic Regression (DT-DPA-DDT-PR) model for accurate monitoring of pharmaceutical production operations. The proposed model when contrasted with traditional methods performs better in pharmaceutical production monitoring.
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Chowdary, Narne Hemanth, Prasad Reddy P.V.G.D., and Suresh Chittineni. "Digital Twin based Data Pattern Analysis for IoT-Based Pharmaceutical Production Monitoring." Journal of Trends in Computer Science and Smart Technology 8, no. 1 (2026): 1-27. doi: 10.36548/jtcsst.2026.1.001
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Chowdary, N. H., P.V.G.D., P. R., & Chittineni, S. (2026). Digital Twin based Data Pattern Analysis for IoT-Based Pharmaceutical Production Monitoring. Journal of Trends in Computer Science and Smart Technology, 8(1), 1-27. https://doi.org/10.36548/jtcsst.2026.1.001
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Chowdary, Narne Hemanth, et al. "Digital Twin based Data Pattern Analysis for IoT-Based Pharmaceutical Production Monitoring." Journal of Trends in Computer Science and Smart Technology, vol. 8, no. 1, 2026, pp. 1-27. DOI: 10.36548/jtcsst.2026.1.001.
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Chowdary NH, P.V.G.D. PR, Chittineni S. Digital Twin based Data Pattern Analysis for IoT-Based Pharmaceutical Production Monitoring. Journal of Trends in Computer Science and Smart Technology. 2026;8(1):1-27. doi: 10.36548/jtcsst.2026.1.001
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N. H. Chowdary, P. R. P.V.G.D., and S. Chittineni, "Digital Twin based Data Pattern Analysis for IoT-Based Pharmaceutical Production Monitoring," Journal of Trends in Computer Science and Smart Technology, vol. 8, no. 1, pp. 1-27, Mar. 2026, doi: 10.36548/jtcsst.2026.1.001.
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Chowdary, N.H., P.V.G.D., P.R. and Chittineni, S. (2026) 'Digital Twin based Data Pattern Analysis for IoT-Based Pharmaceutical Production Monitoring', Journal of Trends in Computer Science and Smart Technology, vol. 8, no. 1, pp. 1-27. Available at: https://doi.org/10.36548/jtcsst.2026.1.001.
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@article{chowdary2026,
author = {Narne Hemanth Chowdary and Prasad Reddy P.V.G.D. and Suresh Chittineni},
title = {{Digital Twin based Data Pattern Analysis for IoT-Based Pharmaceutical Production Monitoring}},
journal = {Journal of Trends in Computer Science and Smart Technology},
volume = {8},
number = {1},
pages = {1-27},
year = {2026},
publisher = {IRO Journals},
doi = {10.36548/jtcsst.2026.1.001},
url = {https://doi.org/10.36548/jtcsst.2026.1.001}
}
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Keywords
Digital Twins
Pharmaceutical Production
Quality-by-Design
Data Pattern Analysis
Diminutive Difference Trigger
Probabilistic Regression
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