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. 21, 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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References

  1. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence and Blockchain Integration for Enhanced Security in Insurance: Techniques, Models, and Real-World Applications." African Journal of Artificial Intelligence and Sustainable Development 1.2 (2021): 187-224.
  2. Singh, Puneet. "AI-Driven Personalization in Telecom Customer Support: Enhancing User Experience and Loyalty." Distributed Learning and Broad Applications in Scientific Research 9 (2023): 325-363.
  3. Rambabu, Venkatesha Prabhu, Selvakumar Venkatasubbu, and Jegatheeswari Perumalsamy. "AI-Enhanced Workflow Optimization in Retail and Insurance: A Comparative Study." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 163-204.
  4. Pradeep Manivannan, Rajalakshmi Soundarapandiyan, and Amsa Selvaraj, “Navigating Challenges and Solutions in Leading Cross-Functional MarTech Projects”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, pp. 282–317, Feb. 2022
  5. Jasrotia, Manojdeep Singh. "Unlocking Efficiency: A Comprehensive Approach to Lean In-Plant Logistics." International Journal of Science and Research (IJSR) 13.3 (2024): 1579-1587.
  6. Gayam, Swaroop Reddy. "AI for Supply Chain Visibility in E-Commerce: Techniques for Real-Time Tracking, Inventory Management, and Demand Forecasting." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 218-251.
  7. Nimmagadda, Venkata Siva Prakash. "AI-Powered Predictive Analytics for Credit Risk Assessment in Finance: Advanced Techniques, Models, and Real-World Applications." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 251-286.
  8. Putha, Sudharshan. "AI-Driven Decision Support Systems for Insurance Policy Management." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 326-359.
  9. Sahu, Mohit Kumar. "Machine Learning Algorithms for Automated Underwriting in Insurance: Techniques, Tools, and Real-World Applications." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 286-326.
  10. Kasaraneni, Bhavani Prasad. "Advanced AI Techniques for Fraud Detection in Travel Insurance: Models, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 455-513.
  11. Kondapaka, Krishna Kanth. "Advanced AI Models for Portfolio Management and Optimization in Finance: Techniques, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 560-597.
  12. Kasaraneni, Ramana Kumar. "AI-Enhanced Claims Processing in Insurance: Automation and Efficiency." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 669-705.
  13. Pattyam, Sandeep Pushyamitra. "Advanced AI Algorithms for Predictive Analytics: Techniques and Applications in Real-Time Data Processing and Decision Making." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 359-384.
  14. Kuna, Siva Sarana. "AI-Powered Customer Service Solutions in Insurance: Techniques, Tools, and Best Practices." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 588-629.
  15. Gayam, Swaroop Reddy. "Artificial Intelligence for Financial Fraud Detection: Advanced Techniques for Anomaly Detection, Pattern Recognition, and Risk Mitigation." African Journal of Artificial Intelligence and Sustainable Development 1.2 (2021): 377-412.
  16. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Automated Loan Underwriting in Banking: Advanced Models, Techniques, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 174-218.
  17. Putha, Sudharshan. "AI-Driven Molecular Docking Simulations: Enhancing the Precision of Drug-Target Interactions in Computational Chemistry." African Journal of Artificial Intelligence and Sustainable Development 1.2 (2021): 260-300.
  18. Sahu, Mohit Kumar. "Machine Learning Algorithms for Enhancing Supplier Relationship Management in Retail: Techniques, Tools, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 227-271.
  19. Kasaraneni, Bhavani Prasad. "Advanced AI Techniques for Predictive Maintenance in Health Insurance: Models, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 513-546.
  20. Kondapaka, Krishna Kanth. "Advanced AI Models for Retail Supply Chain Network Design and Optimization: Techniques, Applications, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 598-636.
  21. Kasaraneni, Ramana Kumar. "AI-Enhanced Clinical Trial Design: Streamlining Patient Recruitment, Monitoring, and Outcome Prediction." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 706-746.
  22. Pattyam, Sandeep Pushyamitra. "AI in Data Science for Financial Services: Techniques for Fraud Detection, Risk Management, and Investment Strategies." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 385-416.
  23. Kuna, Siva Sarana. "AI-Powered Techniques for Claims Triage in Property Insurance: Models, Tools, and Real-World Applications." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 208-245.
  24. Pradeep Manivannan, Priya Ranjan Parida, and Chandan Jnana Murthy. “The Influence of Integrated Multi-Channel Marketing Campaigns on Consumer Behavior and Engagement”. Journal of Science & Technology, vol. 3, no. 5, Oct. 2022, pp. 48-87
  25. Rambabu, Venkatesha Prabhu, Jeevan Sreerama, and Jim Todd Sunder Singh. "AI-Driven Data Integration: Enhancing Risk Assessment in the Insurance Industry." Australian Journal of Machine Learning Research & Applications 2.2 (2022): 130-179.
  26. Selvaraj, Akila, Praveen Sivathapandi, and Rajalakshmi Soundarapandiyan. "Blockchain-Based Cybersecurity Solutions for Automotive Industry: Protecting Over-the-Air (OTA) Software Updates in Autonomous and Connected Vehicles." Cybersecurity and Network Defense Research 3.2 (2023): 86-134.
  27. Paul, Debasish, Gunaseelan Namperumal, and Akila Selvaraj. "Cloud-Native AI/ML Pipelines: Best Practices for Continuous Integration, Deployment, and Monitoring in Enterprise Applications." Journal of Artificial Intelligence Research 2.1 (2022): 176-231.
  28. Namperumal, Gunaseelan, Sharmila Ramasundaram Sudharsanam, and Rajalakshmi Soundarapandiyan. "Data-Driven Workforce Management in Cloud HCM Solutions: Utilizing Big Data and Analytics for Strategic Human Resources Planning." Australian Journal of Machine Learning Research & Applications 2.2 (2022): 549-591.
  29. Soundarapandiyan, Rajalakshmi, Yeswanth Surampudi, and Akila Selvaraj. "Intrusion Detection Systems for Automotive Networks: Implementing AI-Powered Solutions to Enhance Cybersecurity in In-Vehicle Communication Protocols." Cybersecurity and Network Defense Research 3.2 (2023): 41-86.
  30. Sudharsanam, Sharmila Ramasundaram, Praveen Sivathapandi, and Yeswanth Surampudi. "Cloud-Based Telematics and Real-Time Data Integration for Fleet Management: A Comprehensive Analysis of IoT-Driven Predictive Analytics Models." Journal of Artificial Intelligence Research and Applications 3.1 (2023): 622-657.

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