Vol. 4 No. 2 (2024): Journal of AI-Assisted Scientific Discovery
Articles

The Role of AI-Driven Decision Support Systems in Optimizing U.S. Pharmaceutical Manufacturing Operations

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  • Dr. Omar El-Araby
Dr. Omar El-Araby
Professor of Electrical Engineering, Alexandria University, Egypt
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Published 10-08-2024

Keywords

  • Decision Support Systems,
  • Pharmaceutical Manufacturing
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Omar El-Araby, “The Role of AI-Driven Decision Support Systems in Optimizing U.S. Pharmaceutical Manufacturing Operations”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 229–245, Aug. 2024, Accessed: Nov. 22, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/159

Abstract

This essay examines the role of AI-driven decision support systems in optimizing U.S. pharmaceutical manufacturing operations. The pharmaceutical industry faces various challenges in maintaining operational efficiencies while meeting regulatory compliance and quality standards. Companies are seeking intelligent systems solutions to improve manufacturing processes, reduce costs, and achieve optimal decisions. Decision support systems (DSS) can analyze data, assist manufacturers, and provide optimal solutions in real-time. Advances in artificial intelligence (AI) enable automated systems to learn and create optimal outcomes for companies. Various types of AI-driven DSS, such as optimization models, expert systems, and simulation-based models, are being developed for pharmaceutical manufacturing operations. AI techniques like genetic algorithms and neural networks can improve production scheduling, inventory management, supply chain operations, facility layout design, and system control in the pharmaceutical industry. These systems can be employed to explore numerous variables and determine acceptable operating ranges for pharmaceutical manufacturing, leading to increased productivity, reduced operational costs, enhanced compliance, and improved product quality.

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