The Role of AI-Driven Decision Support Systems in Optimizing U.S. Manufacturing Operations
Keywords:
Decision Support Systems, Manufacturing OperationsAbstract
The emergence of disruptive technologies has propelled several recently-industrialized regions towards a new era of smart manufacturing growth in order to collaboratively keep up with industry rivals. Despite U.S. dominance in advanced manufacturing, systems-centric approaches remain poorly examined given rapid changes in the manufacturing landscape; an issue dramatically magnified across localities lacking advanced human, capital, and fiscal systems. Manufacturing Decision Support Systems (MDSSs), building on factory data-integrating management practices widely implemented across discrete sectors, enhance transparency around capabilities used to generate product-level economic impact [1]. Such systems further model investment hypotheses surrounding enduring human capital, broader investments, and better technology. Preliminary implementation in small- and mid-sized discrete manufacturers across Indiana resulted in a framework of manufacturing economics surrounding productivity and unit cost [2] ; an initial step towards addressing urgent questions. Such modeling can also empower localities through identification of operations lacking basic practices to model their impact on economic growth. The research articulates pressing issues, data-centric methodologies able to confront them, and an optimistic perspective on the ability of MDSSs to permit U.S. producers to effectively navigate the wave of industrial challenges posed by global rivals equipped with newer, better systems. Enhanced understanding of manufacturing investment dynamics will further enable proactive, enabling approaches to building productive capacity amongst legacy systems on slower trajectories regarding technological adaptability/uptake.
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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, Praveen Sivathapandi, and Deepak Venkatachalam. "Artificial Intelligence-Enhanced Telematics Systems for Real-Time Driver Behaviour Analysis and Accident Prevention in Modern Vehicles." Journal of Artificial Intelligence Research 3.1 (2023): 198-239.
Paul, Debasish, Gowrisankar Krishnamoorthy, and Sharmila Ramasundaram Sudharsanam. "Platform Engineering for Continuous Integration in Enterprise Cloud Environments: A Case Study Approach." Journal of Science & Technology 2.3 (2021): 179-214.
Namperumal, Gunaseelan, Akila Selvaraj, and Priya Ranjan Parida. "Optimizing Talent Management in Cloud-Based HCM Systems: Leveraging Machine Learning for Personalized Employee Development Programs." Journal of Science & Technology 3.6 (2022): 1-42.
Soundarapandiyan, Rajalakshmi, Priya Ranjan Parida, and Yeswanth Surampudi. "Comprehensive Cybersecurity Framework for Connected Vehicles: Securing Vehicle-to-Everything (V2X) Communication Against Emerging Threats in the Automotive Industry." Cybersecurity and Network Defense Research 3.2 (2023): 1-41.
Sivathapandi, Praveen, Debasish Paul, and Akila Selvaraj. "AI-Generated Synthetic Data for Stress Testing Financial Systems: A Machine Learning Approach to Scenario Analysis and Risk Management." Journal of Artificial Intelligence Research 2.1 (2022): 246-287.
Sudharsanam, Sharmila Ramasundaram, Deepak Venkatachalam, and Debasish Paul. "Securing AI/ML Operations in Multi-Cloud Environments: Best Practices for Data Privacy, Model Integrity, and Regulatory Compliance." Journal of Science & Technology 3.4 (2022): 52-87.
