Leveraging AI and Machine Learning for Advanced Product Configuration and Optimization

Main Article Content

Balachandar Ramalingam
Rajas Paresh Kshirsagar
Hemant Singh Sengar
Dr. Lalit Kumar
Dr Satendra Pal Singh
Prof.(Dr) Punit Goel

Abstract

In the contemporary landscape of product development, the integration of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized product configuration and optimization processes. This study explores the potential of AI and ML technologies to enhance the customization of products, allowing manufacturers to respond dynamically to customer preferences and market trends. By employing advanced algorithms and data analytics, businesses can analyze vast datasets to identify patterns and insights that inform decision-making in product design and configuration.


The application of AI-driven tools facilitates the automation of complex configuration tasks, reducing lead times and minimizing errors associated with manual processes. Moreover, the adaptability of ML models allows for continuous improvement in configuration strategies, optimizing resource allocation and production efficiency. This research highlights case studies from various industries that have successfully implemented AI and ML solutions, demonstrating tangible benefits such as increased customer satisfaction, reduced costs, and enhanced competitive advantage.


Furthermore, the paper discusses the challenges and considerations associated with the integration of these technologies, including data security, the need for skilled personnel, and the importance of ethical considerations in AI applications. By leveraging AI and ML for advanced product configuration and optimization, organizations can not only meet the growing demands of customization but also pave the way for innovative product offerings in an increasingly complex marketplace. This study serves as a comprehensive guide for practitioners seeking to harness the power of AI and ML in their product development initiatives.

Article Details

How to Cite
Ramalingam, B., Kshirsagar, R. P., Sengar, H. S., Kumar, D. L., Singh, D. S. P., & Goel, P. P. (2024). Leveraging AI and Machine Learning for Advanced Product Configuration and Optimization. Journal of Quantum Science and Technology (JQST), 1(2), May(1–17). Retrieved from https://jqst.org/index.php/j/article/view/6
Section
Original Research Articles

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• Ravi Kiran Pagidi, Raja Kumar Kolli, Chandrasekhara Mokkapati, Om Goel, Dr. Shakeb Khan, & Prof.(Dr.) Arpit Jain. 2022. "Enhancing ETL Performance Using Delta Lake in Data Analytics Solutions." Universal Research Reports 9(4):473–495. https://doi.org/10.36676/urr.v9.i4.1381.

• Ravi Kiran Pagidi, Nishit Agarwal, Venkata Ramanaiah Chintha, Er. Aman Shrivastav, Shalu Jain, Om Goel. 2022. "Data Migration Strategies from On-Prem to Cloud with Azure Synapse." IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.9, Issue 3, Page No pp.308-323, August 2022. Available at: http://www.ijrar.org/IJRAR22C3165.pdf.

• Kshirsagar, Rajas Paresh, Nishit Agarwal, Venkata Ramanaiah Chintha, Er. Aman Shrivastav, Shalu Jain, & Om Goel. (2022). Real Time Auction Models for Programmatic Advertising Efficiency. Universal Research Reports, 9(4), 451–472. https://doi.org/10.36676/urr.v9.i4.1380

• Kshirsagar, Rajas Paresh, Shashwat Agrawal, Swetha Singiri, Akshun Chhapola, Om Goel, and Shalu Jain. (2022). "Revenue Growth Strategies through Auction Based Display Advertising." International Journal of Research in Modern Engineering and Emerging Technology, 10(8):30. Retrieved October 3, 2024 (http://www.ijrmeet.org).

• Phanindra Kumar, Venudhar Rao Hajari, Abhishek Tangudu, Raghav Agarwal, Shalu Jain, & Aayush Jain. (2022). Streamlining Procurement Processes with SAP Ariba: A Case Study. Universal Research Reports, 9(4), 603–620. https://doi.org/10.36676/urr.v9.i4.1395

• Kankanampati, Phanindra Kumar, Pramod Kumar Voola, Amit Mangal, Prof. (Dr) Punit Goel, Aayush Jain, and Dr. S.P. Singh. (2022). "Customizing Procurement Solutions for Complex Supply Chains: Challenges and Solutions." International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET), 10(8):50. Retrieved (https://www.ijrmeet.org).

• Ravi Kiran Pagidi, Rajas Paresh Kshir-sagar, Phanindra Kumar Kankanampati, Er. Aman Shrivastav, Prof. (Dr) Punit Goel, & Om Goel. (2022). Leveraging Data Engineering Techniques for Enhanced Business Intelligence. Universal Research Reports, 9(4), 561–581. https://doi.org/10.36676/urr.v9.i4.1392

• Rajas Paresh Kshirsagar, Santhosh Vijayabaskar, Bipin Gajbhiye, Om Goel, Prof.(Dr.) Arpit Jain, & Prof.(Dr) Punit Goel. (2022). Optimizing Auction Based Programmatic Media Buying for Retail Media Networks. Universal Research Reports, 9(4), 675–716. https://doi.org/10.36676/urr.v9.i4.1398

• Phanindra Kumar, Shashwat Agrawal, Swetha Singiri, Akshun Chhapola, Om Goel, Shalu Jain. "The Role of APIs and Web Services in Modern Procurement Systems," IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume 9, Issue 3, Page No pp.292-307, August 2022, Available at: http://www.ijrar.org/IJRAR22C3164.pdf

• Rajas Paresh Kshirsagar, Rahul Arulkumaran, Shreyas Mahimkar, Aayush Jain, Dr. Shakeb Khan, Prof.(Dr.) Arpit Jain. "Innovative Approaches to Header Bidding: The NEO Platform," IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume 9, Issue 3, Page No pp.354-368, August 2022, Available at: http://www.ijrar.org/IJRAR22C3168.pdf

• Phanindra Kumar Kankanampati, Siddhey Mahadik, Shanmukha Eeti, Om Goel, Shalu Jain, & Raghav Agarwal. (2022). Enhancing Sourcing and Contracts Management Through Digital Transformation. Universal Research Reports, 9(4), 496–519. https://doi.org/10.36676/urr.v9.i4.1382

• Satish Vadlamani, Raja Kumar Kolli, Chandrasekhara Mokkapati, Om Goel, Dr. Shakeb Khan, & Prof.(Dr.) Arpit Jain. (2022). Enhancing Corporate Finance Data Management Using Databricks And Snowflake. Universal Research Reports, 9(4), 682–602. https://doi.org/10.36676/urr.v9.i4.1394

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