AI Agents in the Manufacturing Industry, Enhancing Manufacturing Excellence
¹Renjith kumar Surendran pillai PDENG Student, Faculty of Science and Engineering, University of Limerick, Ireland; surendranpillai.renjithkumar@ul.ie
2 Patrick Denny Department of Computer Science and Information Systems (CSIS), D2ICE Research Centre, Faculty of Science and Engineering, University of Limerick, Ireland; patrick.denny@ul.ie
3 Eoin O’Connell Department of Electronics and Computer Engineering (E&CE), Faculty of Science and Engineering, University of Limerick, Ireland; eoin.oconnell@ul.ie
4 Muhammed Shaffeq Faculty of Science and Engineering, University of Limerick, Ireland; 24287397@studentmail.ul.ie
* Correspondence: patrick.denny@ul.ie
Abstract: The rapid expansion of digital technologies and the growing demands of industrial standards are elevating the manufacturing sector to a more advanced level. This study proposes the integration of AI agents, specifically AI callbots, into the manufacturing environment and examines performance factors, downtime, meaningful insights, and decision-making. The study further investigates the use of AI callbots in optimizing performance, reducing downtime, and promoting prompt decision-making. The challenges of conventional systems are thoroughly examined, including troubleshooting latency, inconsistent data management, and a lack of real-time monitoring. The proposed solution considers the employment of AI agents combined with machine learning algorithms and natural language processing, facilitating human-machine integration, predictive maintenance, and timely scheduling. The methodology encompasses the optimal use of a unified namespace, enhancements in digital twin technology, and the application of augmented reality and virtual reality with real-time feedback adaptation. The results indicate a substantial increase in efficiency, client satisfaction, and compliance with industrial standards. The article concludes stating AI Agent as an effective AI-driven tool offering a sustainable approach in achieving manufacturing excellence.
Keywords: CNC fault detection; digital twins; food and beverage optimization; Industry 4.0; MedTech applications; machine learning; motor fault prediction; predictive maintenance; real-time data analytics; robots and cobots; variable speed drives; virtual inspections
