PRR Project

Summary

Traditional manufacturing systems often suffer from inefficiencies and lack of agility. The integration of artificial intelligence into manufacturing operations represents a transformative opportunity to unlock new paths for working in more adaptable, productive and responsive ways, allowing for an increase in competitiveness. The OEE (overall equipment efficiency) of industrial companies depends on the optimization of tens of process variables (such as temperature, pressure humidity, to name a few) that are typically adjusted (with delay) by humans. By integrating AI technologies, such as machine learning, predictive analytics and prescriptive analytics, into the manufacturing environment, the goal is to adapt to dynamic production demands in real-time, enhancing productivity, minimizing downtime and increasing quality indices of manufacturing. The challenge is to develop comprehensive analytical methods that will be flexible enough to address specificities of different manufacturing processes and environments. For instance, the identification of relations between process variables and the defect rate or processing speed of the machine, demands all the power of AI to drive real-time optimization. It is worth noting that Manufacturing Execution Systems (MES) play a pivotal role in the integration of advanced technologies, as artificial intelligence, within manufacturing environments. In terms of data integration and connectivity, MES serves as a central hub for collecting and integrating data from various sources across the manufacturing process, such as equipment sensors, production machines, and inventory systems. Furthermore, MES platforms offer real-time monitoring, allowing operators to track production activities and monitor key performance indicators. But, in order to respond to issues in real-time, which will trigger adaptive planning, quality management, and continuous improvement, AI-driven optimization is fundamental and must be seamlessly integrated into manufacturing environments.The position “Assistant Professor for Real-time manufacturing with AI” is fundamental to develop new skills at the Industrial Engineering and Management Department of FEUP for this emerging area. It is central for this department and FEUP to keep being recognized as an international and leading reference in industrial engineering, being able to integrate state-of-the-art technologies, such as artificial intelligence, in its research and teaching activities. The Assistant Professor will be responsible for developing comprehensive analytical methods tailored to the specific requirements of different real-time manufacturing processes and environments.The candidates need to have a hybrid profile, with high business expertise as strong knowledge of manufacturing (for instance, in processes, technologies, systems, automation, among others) and very strong analytical capabilities, in terms of advanced analytics and artificial intelligence. They should have a PhD in Engineering, Computer Science, Data Science, Industrial Engineering, or a related field. They must have demonstrated experience in developing and implementing AI solutions for real-world applications, preferably in manufacturing or industrial settings, as well as hands-on experience with data collection, preprocessing, and analysis techniques. Overall, the ideal candidates should possess a combination of technical expertise, practical experience, and strong interpersonal skills to drive research and innovation and deliver tangible results.To ensure the successful recruitment of talented researchers for the described position, an extensive communication campaign will be launched across multiple platforms and academic networks to reach a wide pool of potential candidates. The department can use its network of academic collaborators, industry partners and alumni to spread the word about the position and encourage qualified individuals to apply. In addition, the department plans to use the department´s flagship events, including IEMS and DEGI Club, to showcase the department´s research activities and highlight the opportunities available to candidates. These events can also provide an opportunity for candidates to interact with current faculty members and learn more about the department´s culture and research priorities.