Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

The Role of AI-Based Decision Support Systems in Optimizing U.S. Manufacturing and Logistics Operations

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  • Prof. Rajesh Mehta
Prof. Rajesh Mehta
Senior Researcher, Microsoft Research, Bangalore, India

Published 01-09-2024

Keywords

  • Decision Support Systems,
  • Manufacturing,
  • Logistics
Creative Commons License

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

How to Cite

[1]
Prof. Rajesh Mehta, “The Role of AI-Based Decision Support Systems in Optimizing U.S. Manufacturing and Logistics Operations”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 252–266, Sep. 2024, Accessed: Nov. 22, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/163

Abstract

The introduction sets the stage for the essay by providing an overview of the topic and its relevance to U.S. manufacturing and logistics. It serves as an entry point to the discussion of AI-based decision support systems and their impact on the industry. AI-based decision support systems have become increasingly vital in optimizing manufacturing and logistics operations in the U.S. These systems not only enhance operational efficiency but also contribute to long-term economic viability, social cohesiveness, inclusion, and environmental sustainability [1].

Furthermore, the integration of intelligent systems in manufacturing has shown promising developments and future prospects. Specialized surveys have highlighted the application of fuzzy set theory, genetic algorithms, expert systems, and neural networks in various aspects of manufacturing, including production management, quality control, process planning, and flexible manufacturing systems control [2]. These advancements underscore the potential of AI-based decision support systems to revolutionize U.S. manufacturing and logistics operations.

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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, Deepak Venkatachalam, and Gunaseelan Namperumal. "Synthetic Data for Financial Anomaly Detection: AI-Driven Approaches to Simulate Rare Events and Improve Model Robustness." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 373-425.
  27. Paul, Debasish, Praveen Sivathapandi, and Rajalakshmi Soundarapandiyan. "Evaluating the Impact of Synthetic Data on Financial Machine Learning Models: A Comprehensive Study of AI Techniques for Data Augmentation and Model Training." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 303-341.
  28. Namperumal, Gunaseelan, Praveen Sivathapandi, and Deepak Venkatachalam. "The Role of Blockchain Technology in Enhancing Data Integrity and Transparency in Cloud-Based Human Capital Management Solutions." Journal of Artificial Intelligence Research and Applications 3.1 (2023): 546-582.
  29. Soundarapandiyan, Rajalakshmi, Praveen Sivathapandi, and Akila Selvaraj. "Quantum-Resistant Cryptography for Automotive Cybersecurity: Implementing Post-Quantum Algorithms to Secure Next-Generation Autonomous and Connected Vehicles." Cybersecurity and Network Defense Research 3.2 (2023): 177-218.
  30. Sudharsanam, Sharmila Ramasundaram, Akila Selvaraj, and Praveen Sivathapandi. "Enhancing Vehicle-to-Everything (V2X) Communication with Real-Time Telematics Data Analytics: A Study on Safety and Efficiency Improvements in Smart Cities." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 461-507.

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