SmartManufAI: AI-Enabled Performance Analytics Platform for Intelligent Manufacturing Systems
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I4P135Keywords:
AI in Manufacturing, Predictive Analytics, Smartmanufai, Industry 4.0, Intelligent Systems, Digital Twin, Process Optimization, Machine LearningAbstract
The progression of smart manufacturing is essentially a major point where it moves from standard automation to a more flexible, data-driven system that it learns from, optimizes itself, and can also self-correct in real-time. In manufacturers' current ever-changing production environments, they are pressed hard to simultaneously achieve efficiency, quality, and sustainability requirements that cannot be fulfilled any longer by traditional methods of monitoring or control systems based on rules. Therefore, there is a demand for AI-powered platforms for performance analytics that can turn an ocean of operational data into a few handy pointers. SmartManufAI is the answer to all problems, which can also be seen as a comprehensive device coupling the features of on-demand analytics, predictive maintenance, and optimization of the capabilities of one unified platform for future smart factories. It uses the latest machine learning algorithms, virtual models, and data from IoT sensors to keep checking machine operations, forecast inevitable breakdowns, and adjust production workflows automatically. Their main goal driving the research behind SmartManufAI was to make manufacturing processes more transparent, to minimize the time of unplanned machine stoppages, and also to maximize the use of resources through smart insights. Their method involved obtaining data from the interlinked systems, feature engineering for performance indicators, and the use of hybrid predictive models validated through on-site pilot studies. They have been shown to be very effective in improving overall equipment effectiveness (OEE), forecasting maintenance schedules, and saving operational costs, thus positioning SmartManufAI as a technology that can be extended in various ways to integrate into the Industry 4.0 ecosystem. The platform powered with AI, cloud analytics, and industrial IoT is not only about manufacturing performance monitoring but also about performing redefinition through continuous learning and adaptive optimization, thereby opening the doors for the next level of intelligent manufacturing ecosystems.
References
1. Bu, Lingguo, et al. "An IIoT-driven and AI-enabled framework for smart manufacturing system based on three-terminal collaborative platform." Advanced Engineering Informatics 50 (2021): 101370.
2. Lee, Jay, et al. "Industrial AI and predictive analytics for smart manufacturing systems." Smart manufacturing. Elsevier, 2020. 213-244.
3. George, A. Shaji. "AI-enabled intelligent manufacturing: A path to increased productivity, quality, and insights." Partners Universal Innovative Research Publication 2.4 (2024): 50-63.
4. Parakala, Adityamallikarjunkumar. "Emergence of AI Trust Layers & Governance." International Journal of Artificial Intelligence, Data Science, and Machine Learning 6.2 (2025): 144-152.
5. Wan, Jiafu, et al. "Artificial-intelligence-driven customized manufacturing factory: key technologies, applications, and challenges." Proceedings of the IEEE 109.4 (2020): 377-398.
6. Horobet, Alexandra, et al. "Artificial intelligence and smart manufacturing: An analysis of strategic and performance narratives." Amfiteatru Economic 26.66 (2024): 440-457.
7. Guntupalli, Bhavitha. "Code Reviews That Don’t Suck: Tips for Reviewers and Submitters." International Journal of Emerging Research in Engineering and Technology 6.2 (2025): 71-80.
8. Tyagi, Amit Kumar, et al. "Artificial intelligence empowered smart manufacturing for modern society: a review." Artificial Intelligence‐Enabled Digital Twin for Smart Manufacturing (2024): 55-83.
9. Akhtar, Z. B. "Artificial intelligence (AI) within manufacturing: An investigative exploration for opportunities, challenges, future directions. Metaverse. 2024; 5 (2): 2731." Computers in Industry, 1990,
10. Ponnusamy, Vijayakumar, Dilliraj Ekambaram, and Nemanja Zdravkovic. "Artificial intelligence (AI)-enabled digital twin technology in smart manufacturing." Industry 4.0, Smart Manufacturing, and Industrial Engineering. CRC Press, 2024. 248-270.
11. Okuyelu, Olanrewaju, and Ojima Adaji. "AI-driven real-time quality monitoring and process optimization for enhanced manufacturing performance." J. Adv. Math. Comput. Sci 39.4 (2024): 81-89.
12. Parakala, Adityamallikarjunkumar. "Self‑Learning Bots & Cloud‑Native Platforms." International Journal of Emerging Trends in Computer Science and Information Technology 5.4 (2024): 132-141.
13. Al Mamun, Abdullah. AI-enabled modeling and monitoring of data-rich advanced manufacturing systems. Mississippi State University, 2023.
14. Zahoor, Sajjad, et al. "Artificial intelligence application and high-performance work systems in the manufacturing sector: a moderated-mediating model." Artificial Intelligence Review 58.1 (2024): 11.
15. Guntupalli, Bhavitha. "Data Lake Vs. Data Warehouse: Choosing the Right Architecture." International Journal of Artificial Intelligence, Data Science, and Machine Learning 4.4 (2023): 54-64.
16. Alamin, Khaled Sidahmed Sidahmed, et al. "An AI-Enabled Framework for Smart Semiconductor Manufacturing." 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024.
17. Wang, YuanBin, et al. "Smart additive manufacturing: Current artificial intelligence-enabled methods and future perspectives." Science China Technological Sciences 63.9 (2020): 1600-1611.
18. Lv, Zhihan, et al. "AI-enabled IoT-edge data analytics for connected living." ACM Transactions on Internet Technology 21.4 (2021): 1-20.
19. Immaneni, Jayaram. “Facilitating Real Time Data Consumption by Using a Graph Path Cache”. International Journal of AI, BigData, Computational and Management Studies, vol. 6, no. 3, July 2025, pp. 65-73
20. Dhanalakshmi, R., et al. "Smart analytics and AI for managing modern performance management systems." Smart analytics, artificial intelligence and sustainable performance management in a global digitalised economy. Vol. 110. Emerald Publishing Limited, 2023. 243-263.
21. Vemula, V. R. (2024). Cognitive artificial intelligence systems for proactive threat hunting in AI-driven cloud applications. AVE Trends in Intelligent Computing Systems, 1(3), 173-183.
