The Role of AI-Driven Decision Support Systems in Enhancing U.S. Manufacturing Logistics

Authors

  • Dr. Olga Petrova Professor of Applied Mathematics, National Research University Higher School of Economics (HSE), Russia Author

Keywords:

Decision Support Systems, Manufacturing Logistics

Abstract

The introduction sets the stage for exploring the role of AI-driven decision support systems in U.S. manufacturing logistics. It highlights the significance of AI technology in optimizing production processes and improving supply chain management, ultimately enhancing the efficiency and competitiveness of the manufacturing industry [1]. As AI continues to evolve, its potential economic impact is significant, with predictions indicating a potential increase in global GDP by 14% by 2030. Moreover, AI has the potential to transform industrial processes, including automation of manufacturing tasks, resource allocation, and even customer service and hiring processes [2].

The introduction provides a foundation for understanding the applications and benefits of AI-driven decision support systems in manufacturing logistics, emphasizing the potential for AI to enhance various aspects of the manufacturing industry, from production to customer service and business decisions.

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References

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.

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.

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.

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

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.

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.

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.

Putha, Sudharshan. "AI-Driven Decision Support Systems for Insurance Policy Management." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 326-359.

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.

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.

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.

Kasaraneni, Ramana Kumar. "AI-Enhanced Claims Processing in Insurance: Automation and Efficiency." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 669-705.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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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Published

24-09-2024

How to Cite

[1]
Dr. Olga Petrova, “The Role of AI-Driven Decision Support Systems in Enhancing U.S. Manufacturing Logistics”, J. of Artificial Int. Research and App., vol. 4, no. 2, pp. 275–294, Sep. 2024, Accessed: Nov. 24, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/243

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