The Role of AI-Driven Decision Support Systems in Optimizing U.S. Semiconductor Manufacturing Operations
Keywords:
Decision Support Systems, Semiconductor ManufacturingAbstract
The semiconductor manufacturing industry has been facing increasing complexity and cost pressures due to the demands of Moore's Law and supply chain disruptions, leading to record waiting times for orders. This has underscored the critical need for optimizing production processes to mitigate market challenges and reduce environmental impact. The application of machine learning techniques, particularly in combinatorial optimization problems such as planning and scheduling, has emerged as a promising area of research in semiconductor fab efficiency improvement [1].
The work by Tassel et al. highlights the use of self-supervised and reinforcement learning in semiconductor fab scheduling, addressing a wide range of challenges encountered in large-scale production environments. Their proposed adaptive scheduling method aims to improve yield and reduce customer order delays, demonstrating the potential of AI-driven decision support systems in enhancing operational efficiency and addressing critical market situations in semiconductor manufacturing.
Downloads
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 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.
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.
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.
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.
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.
