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Review Article Open Access December 27, 2020

Enhancing Pharmaceutical Supply Chain Efficiency with Deep Learning-Driven Insights

Venkata Obula Reddy Puli 1,* and Tulasi Naga Subhash Polineni 2
1
SAP Solution Architect, USA
2
Software Engineer, USA
Publihed in: Current Research in Public Health (Volume 1, Issue 1, 2021)
Page(s): 126-138
DOI: 10.31586/jaibd.2020.1186
Received
August 28, 2020
Revised
October 12, 2020
Accepted
December 20, 2020
Published
December 27, 2020
Keywords
Pharmaceutical Supply Chain; Deep Learning; Supply Chain Optimization; Predictive Analytics; AI in Pharmaceuticals; Inventory Management; Demand Forecasting; Drug Distribution; Supply Chain Automation; Machine Learning in Healthcare; Data-Driven Decision Making; Logistics Optimization; Pharmaceutical Logistics; Real-Time Tracking; Operational Efficiency
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2021. Published by Scientific Publications
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Cite This Article

APA Style
Puli, V. O. R. , & Polineni, T. N. S. (2021). Enhancing Pharmaceutical Supply Chain Efficiency with Deep Learning-Driven Insights. Current Research in Public Health, 1(1), 126-138. https://doi.org/10.31586/jaibd.2020.1186
ACS Style
Puli, V. O. R. ; Polineni, T. N. S. Enhancing Pharmaceutical Supply Chain Efficiency with Deep Learning-Driven Insights. Current Research in Public Health 2021 1(1), 126-138. https://doi.org/10.31586/jaibd.2020.1186
Chicago/Turabian Style
Puli, Venkata Obula Reddy, and Tulasi Naga Subhash Polineni. 2021. "Enhancing Pharmaceutical Supply Chain Efficiency with Deep Learning-Driven Insights". Current Research in Public Health 1, no. 1: 126-138. https://doi.org/10.31586/jaibd.2020.1186
AMA Style
Puli VOR, Polineni TNS. Enhancing Pharmaceutical Supply Chain Efficiency with Deep Learning-Driven Insights. Current Research in Public Health. 2021; 1(1):126-138. https://doi.org/10.31586/jaibd.2020.1186 
@Article{crph1186,
AUTHOR = {Puli, Venkata Obula Reddy and Polineni, Tulasi Naga Subhash},
TITLE = {Enhancing Pharmaceutical Supply Chain Efficiency with Deep Learning-Driven Insights},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {126-138},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1186},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2020.1186},
ABSTRACT = {The growing complexity of the operating environment urges pharmaceutical innovation. This essay addresses the need for the integration of advanced technologies in the pharmaceutical supply chain. It justifies the value proposition and presents a concrete use case for the integration of deep learning insights to make data-driven decisions. The supply chain has always been a priority for the pharmaceutical industry; research and development recognizes companies' increasing investment in big data strategies, with plans for a CAGR in big data tool adoption. The work presented herein has a preliminary explorative character to recuperate and integrate evidence from partly overlooked practical experience and know-how. The practical relevance of the essay is directed toward practitioners in pharmaceutical production, supply chain management, logistics, and regulatory agencies. The literature has shown a long-term concern for enhanced performance in the pharmaceutical supply chain network. This essay demonstrates the application of deep learning-driven insights to reveal non-evident flow dependencies. The main aim is to present a comprehensive insight into deep learning-driven decision support. The supply chain is portrayed in a holistic manner, seeking end-to-end visibility. Implications for public policy are discussed, such as data equity: many countries are protecting their populations and economic growth by building resilience and efficiency to ensure the capacity to move goods across supply chains. The implementation strategy is covered. The combined reduction of variability, efficiency as matured richness, reliability (on stochastic flows and their understanding through deep learning and data), and system noise (increased dampening through the inclusiveness of all stakeholders) results in increased responsiveness of supply chains for pharmaceutical products. Future work involves the integration of external data, closing the loop between planning and its application in reality.},
}
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