Available Soon
Johan Stahre, Chalmers University of Technology, Sweden
Artificial Intelligence of Things in Manufacturing
Roland Essmann, Honeywell, Germany
Trustworthy Manufacturing: How AI Is Becoming Part of Manufacturing Operations Automation
Christos Emmanouilidis, University of Groningen, Netherlands
Giancarlo Fortino, University Calabria, Italy
(Cancelled)
Short Bio
Roland Essmann leads global Digital Factory initiatives at Honeywell Industrial Automation (IA), driving the adoption of AI- and IoT-based solutions in manufacturing environments.
He began his career as a mechanical engineer developing test and automation systems, and has been actively shaping Industry 4.0 transformations since his first MES implementation over a decade ago. His work focuses on bridging IT and OT domains and enabling scalable digital transformation across global factory networks.
With over 31 years of experience in manufacturing, Roland Essmann combines deep industrial expertise with a strong focus on innovation, mentoring the next generation of engineers and driving cross-functional collaboration between engineering, IT, and data science communities.
Abstract
How can we empower factory teams to achieve measurable productivity gains—quickly and at scale?
Artificial Intelligence of Things (AIoT), combining AI with industrial IoT, is transforming how data is collected, contextualized, and used across global manufacturing networks.
In this keynote, we are illustrating how AI-driven support systems enable faster, more informed actions on the shop floor.
Building on collaborations with leading universities and industrial partners, the talk will highlight emerging approaches, tools, and methods that move beyond data visibility towards actionable intelligence.
In particular, the role of Agentic AI and Physical AI will be discussed as enablers for autonomous decision-making and adaptive production systems—opening new opportunities for the factory of the future.
Short Bio
Christos Emmanouilidis is a Faculty Member at the University of Groningen, Netherlands, with 25 years of experience from in Industry, Academia, Research, & Innovation supporting organisations, and standardisation bodies, working at the intersection of engineering, computing and industrial management. He has had leading roles in projects related to Human-Centric AI & Cognitive Systems, Robotics & Automation, as well as Cyber-Physical Systems and Internet of Things Technologies to serve industrial application needs, in diverse application areas and socio-technical systems, with a particular focus on Production, Asset, and Maintenance Management. Recent EU-funded projects on Human-Centric AI in such domains include STAR, HumAIne, AI4Work, SkillAibility, and AIXpert. He has acted as Research & Innovation expert, having served as Innovation expert for Regional Government and the EC’s JRC and EIT regarding the introduction of innovative digitalization solutions in support of Smart Specialisation Strategies (RIS3), Industrial Transitions, and also for EIT’s KICs Monitoring and Evaluation. He has editorial appointments in several journals, including IEEE Transactions on Technology and Society, Engineering Applications of AI, Neural Computing and Applications, and Annual Reviews in Control. He is a Senior IEEE Member, a Founding Fellow of the International Society of Engineering Asset Management (ISEAM), and a member of IFIP WG5.7 ‘Advances in Production Management Systems. Christos has been the IFAC TC5.1 AMEST secretary between 2014 and 2020 and the chair between 2020 and 2026. He is the new IFAC TC5.1 (Manufacturing Plant Control) chair for the term 2026-2029, while also contributing to IFAC TC5.3, TC9.2, TC6.4, and TC4.1.
Abstract
Manufacturing operations have been among the main beneficiaries of automation over the years. Yet automation is now expanding to include AI automation and AI-enabled operations. This concerns the lifecycle management of production and its assets, including design, operations, maintenance, and end-of-lifecycle management. AI automation has similarities but also differences from the long-established concepts and technologies associated with industrial agents. Increasingly, it includes Large Language Models and extends them into action-capable solutions. Unlike traditional AI models that merely generate outputs, or industrial agents developed, deployed, and operated under well-defined operational contexts, Agentic AI systems exhibit more complex behaviors, making them applicable to wider domains. What sets them apart from previous agent-based approaches is their ability to integrate complex multi-step processes, akin to more broadly intelligent entities. This includes perception, goal setting, decision-making, and planning of actions to deliver on goals, including action execution. They often use a range of tools available to them, while maintaining different types of memory and learning capabilities to deliver context-adaptive behaviors. While individual such components have long been part of the evolving capabilities of autonomous and robotics systems, Agentic AI combines architectural and tools orchestration options that enable physical entities to become equipped with more generic intelligent capabilities. Availability of Agentic AI systems triggers also a redefinition of human roles. While much is said about automation replacing humans, AI-automation is also creating new opportunities for human-AI teaming to produce higher added-value outcomes, which would not have easily been produced by humans or automation acting alone. However, such AI-enabled systems are often less predictable due to the generative and non-deterministic nature of their function, creating trustworthiness risks and uncertainty when deployed in industrial settings. Trustworthiness is a quality property of systems and is about meeting stakeholders' expectations in a verifiable way. While test and verification of cyber-physical systems' trustworthiness has strong foundations, the same cannot be said for AI automation. When considering manufacturing settings, trustworthiness has multiple dimensions and needs to combine quality properties of AI and physical systems, but also of the collaboration between humans, AI, digital, and other physical technologies. Referring to examples and application cases of growing interest and uptake by industry, this talk will offer a bird's-eye view of AI-enabled manufacturing systems' trustworthiness and its implications for the management of production operations, including the redefined human roles in them.
Short Bio
Giancarlo Fortino (IEEE Fellow 2022) is Full Professor of Computer Engineering at the Dept of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a PhD in Computer Engineering from Unical in 2000. He is also a distinguished professor at Wuhan University of Technology (China), a high-end expert at Huazhong University of Science and Technology (China), a senior research fellow at the Italian ICAR-CNR Institute, CAS PIFI Group international fellow at SIAT (Shenzhen), and Distinguished Lecturer for IEEE Sensors Council, SMC society, and IoT TC. He was also a visiting researcher at ICSI, Berkeley (USA), in 1997 and 1999, and a visiting professor at Queensland University of Technology in 2009. At Unical, he is the chair of the PhD School in ICT, the director of the SPEME lab and of the Radiomics lab, and the director of the Postgraduate Master course in AI-driven Radiomics, as well as co-chair of Joint labs on IoT technologies established between Unical and the WUT, SMU, and HZAU Chinese universities, and the AI-driven Robotics Lab funded with the J.C. Bose University of Science and Technology, YMCA. Fortino is also the scientific responsible of the Digital Health group of the Italian CINI National Laboratory at Unical. He is a Highly Cited Researcher 2020-2025 in Computer Science by Clarivate (the only Italian professor ranked). He had 25+ highly cited papers in WoS, and an h-index of 88 with 33000+ citations in Google Scholar. His research interests include wearable computing systems, e-Health, Internet of Things, and agent-based computing. He is the author of 750+ papers in international journals, conferences, and books. He is (founding) series editor of IEEE Press Book Series on Human-Machine Systems and EiC of Springer Internet of Things series and AE of premier int'l journals such as IEEE TASE (senior editor), IEEE TAFFC-CS, IEEE THMS, IEEE T-AI, IEEE SJ, IEEE JBHI, IEEE OJEMB, IEEE OJCS, Information Fusion, EAAI, etc. He chaired many international workshops and conferences (130+), was involved in a huge number of international conferences/workshops (800+) as an IPC member, is/was a guest editor of many special issues (80+). He is cofounder and CEO of SenSysCal S.r.l., a Unical spinoff focused on innovative IoT systems, and, recently, cofounder and vice-CEO of the spin-off Bigtech S.r.l., focused on big data, AI, and IoT technologies.
Fortino is the VP of Cybernetics (term 2026-2027) of the IEEE SMCS, member of the IEEE SMCS ExCom, and former chair of the IEEE SMCS Italian Chapter.
Abstract
Digital Twins (DTs) are software replicas that not only mirrors physical entities but can also proactively predict, control, optimize and simulate their behavior. Born in the manufacturing sector, this concept, after an initial hype, stayed untouched for decades. The rise of the Internet of Things (IoT) and Artificial Intelligence (AI) enabled DT, respectively, to exchange real-world data and to fully exploit it for fulfilling its own goals. Very recently, Generative AI (Gen-AI) methods started being sporadically applied to DT in different contexts and with different targets. In this talk, starting from our experiences on design, implementation, and evaluation of DTs and, more recently, of Opportunistic DTs, we first provide a definition for the Generative DT (GDT) which embraces the main distinctive aspects and potential of current and future Gen-Al-aided DTs.
In particular, we disclose the role of Gen-AI in conciliating the model- and the data-driven approach for the development of DTs. Then, we analyze the added value of main Gen-AI architectures and development methodologies for maximizing the effectiveness and the performance of DTs operating in the IoT domain and deployed in the device-edge-cloud continuum.
Finally, we illustrate the potential of GDT in emblematic use cases in the Smart City, Smart Manufacturing, Smart Water Systems, Smart Robotics, Smart Education, and, more in general, in Smart IoT-driven domains