NEMO 2026 Programme
All times are UTC+3 (Athens, EEST - Eastern European Summertime).
CoDEMO: A Journey from Digital Innovation to 5.0 Community of Practice
École des Mines de Saint-Étienne, France
This lecture will give a general introduction to NEMO 2026 Innovation Camp, created as one of the event contributing to the EU ERASMUS+ Project CoDEMO. The lecture will remind the ambition of CoDEMO Project, dedicated to boost the European transition towards Society 5.0. The lecture will start with key insights on the international vision of the transition towards Organizations 5.0, pushing a digital transition emphasizing the dimensions of Human centricity, Resilience and Sustainability. The lecture will then address two main topics
A presentation of the main outputs of CoDEMO EU project, covering (i) the creation of an international network of innovation platforms, (ii) a strong contribution to the deployment of 5.0 skills and competencies and (iii) a bank of innovation case studies of 5.0 transitions.
A presentation of a structure approach to align the deployment of innovation projects with 5.0 ambition. This general introduction will then open the way to highlight the structure of the quick innovation process proposed for experimentation in NEMO Innovation Camp, including three main phases: (1) Disruptive Business Scenario, (2) Conceptual Modelling for solution design and AI integration and (3) Quick innovation prototyping. The lecture will end by introducing CoDEMO 5.0 community of practice, supported by OMILAB Innovation Community.
A presentation of the main outputs of CoDEMO EU project, covering (i) the creation of an international network of innovation platforms, (ii) a strong contribution to the deployment of 5.0 skills and competencies and (iii) a bank of innovation case studies of 5.0 transitions.
A presentation of a structure approach to align the deployment of innovation projects with 5.0 ambition. This general introduction will then open the way to highlight the structure of the quick innovation process proposed for experimentation in NEMO Innovation Camp, including three main phases: (1) Disruptive Business Scenario, (2) Conceptual Modelling for solution design and AI integration and (3) Quick innovation prototyping. The lecture will end by introducing CoDEMO 5.0 community of practice, supported by OMILAB Innovation Community.
Lecture at
NEMO2026
Date/Time: Monday, July 27, 2026 at 08:30 UTC+2/CEST
Business Scenarios: Digital Servization Strategies
University of Bergamo, Italy
Despite their increasing relevance in both academia and industrial practice, Product-Service System (PSS) business models require further clarification and development to address the complexities and potential introduced by digital technologies. This lecture aims to provide foundational knowledge on servitization strategies, with a specific focus on digital servitization (e.i, the integration of advanced digital technologies into service-oriented business models). Through real-world case studies and practical examples, participants will gain a comprehensive understanding of how companies are rethinking value creation by shifting from product to service-oriented paradigms. A special emphasis will be placed on identifying barriers and opportunities in the digital transformation journey, including organizational, technological, and cultural challenges. Furthermore, the lecture will explore how an engineering approach, supported by appropriate tools and methods, can effectively support the conceptualization and delivery of PSS. This involves a systematic integration of product and service components to ensure value consistency throughout the lifecycle.
Lecture at
NEMO2026
Date/Time: Monday, July 27, 2026 at 09:30 UTC+2/CEST
Conceptualization of knowledge: ideation, design, and realization
Babes-Bolyai University, Romania
Digital Innovation is fundamentally a knowledge-driven effort. Explicit knowledge takes many forms of representation, with different degrees of structure, refinement or operationalization. Over time, knowledge has been captured as natural language text, visual representations, machine-interpretable ontologies, formal rules and logics - each requiring specific management systems and frameworks, built on diverse kinds of knowledge repositories. In the age of Artificial Intelligence, knowledge exchanges and streamlining take place not only between human knowing subjects, but also during human-AI collaborations or directly between AI agents. This requires a revisitation of traditional knowledge management paradigms, considering the new types of interactions and knowledge conversions manifesting in organizations that employ hybrid workforce and value creation teams. Innovation management methods must leverage these new paradigms, as they take an amorphous innovation idea and must gradually refine it through different stages of conceptualization - from early-stage ideation to structured blueprints, simulateable virtualizations and actual realization. Knowledge representation must be able to support all these stages and their progression, and this requires us to investigate the conceptualizations required at each stage and their streamlining possibilities. The talk will reflect on the requirements and expectations arising from this, while giving suggestions and examples on the potential of interplay between enterprise modelling, knowledge graphs and large language models.
Lecture at
NEMO2026
Date/Time: Tuesday, July 28, 2026 at 08:30 UTC+2/CEST
AI Foundations: Human and Technology Perspective
University of Applied Sciences and Arts Northwestern Switzerland FHNW, Switzerland
TBA
Lecture at
NEMO2026
Date/Time: Tuesday, July 28, 2026 at 09:30 UTC+2/CEST
Machine Learning Models: Select, Train and Use
Machine learning can often feel like a complex black box, but mastering its practical application boils down to these fundamental phases: Select, Train, Use (and Evaluate). In this talk, we will try to demystify the end-to-end ML lifecycle. We will cover elements of strategic decision-making behind choosing the right algorithm for your specific data problem, the best practices for training and fine-tuning robust models, and the crucial steps for deploying those models into real-world applications. More precisely, we will discuss:
Select:How to evaluate trade-offs and match the right algorithm, from simple regressions to complex neural networks, to your problem.
Train: Essential techniques for preparing data, optimizing hyperparameters, and avoiding common pitfalls.
Use: Bridging the gap between a local environment and production, including deployment strategies and performance monitoring. Whether you are a data scientist or a developer looking to integrate AI into your workflow, you need a roadmap on how to use ML and AI models for turning raw data into reliable predictions.
Select:How to evaluate trade-offs and match the right algorithm, from simple regressions to complex neural networks, to your problem.
Train: Essential techniques for preparing data, optimizing hyperparameters, and avoiding common pitfalls.
Use: Bridging the gap between a local environment and production, including deployment strategies and performance monitoring. Whether you are a data scientist or a developer looking to integrate AI into your workflow, you need a roadmap on how to use ML and AI models for turning raw data into reliable predictions.
Lecture at
NEMO2026
Date/Time: Wednesday, July 29, 2026 at 08:30 UTC+2/CEST
Human Digital Twins and Generative Agents: Personalized and Adaptive Human-AI Interactions
Hochschule Bielefeld, Germany
While digital twins of machines have been widely discussed and implemented in research and practice the lecture "Human Digital Twins and Generative Agents: Personalized and Adaptive Human-AI Interactions" explores the emerging paradigm of Human Digital Twins and Generative Agents as a foundation for personalized and adaptive human–machine interaction. As organizations increasingly integrate AI into complex work and production systems, the need for human-centered, context-aware, and continuously learning human-machine interaction processes becomes a critical success factor. The session introduces the concept of the Human Digital Twins as a dynamic digital representation of individuals (e.g. workers) that capture elements like preferences, behaviors, competencies, decision processes and situational context. In combination with generative agents, these models enable AI-based systems to simulate, anticipate, and adapt to human preferences or decisions in real time and thus create a dynamic interface between humans and machines in human-machine interaction. Building on recent advances in generative AI, the lecture discusses how such systems can support co-creative decision-making, collaboration, and co-learning in digitalized work environments. Drawing on examples from industry and current research, the talk concludes by outlining practical implications for designing adaptive, personalized, and responsible interaction systems in line with the broader goals of AI-driven innovation for modern business and society.
Lecture at
NEMO2026
Date/Time: Wednesday, July 29, 2026 at 09:30 UTC+2/CEST
Data Science: User Experience Design
Osaka Seikei University, Japan
The rapid advancement of Generative AI is transforming business models, decision-making, and customer interactions across industries. In these disruptive scenarios, organizations must redesign user experiences (UX) that balance technological efficiency with human values such as well-being, trust, and ethics. This lecture explores how data science can serve as a foundation for UX design by integrating AI-driven information processing, optimization, and affect analytics with human judgment, empathy, and creativity. Drawing on the concept of Extended Service Capability (ESC)—an organizational ability to co-create value through human–AI collaboration—it proposes a three-layer framework: a philosophy layer defining where AI should and should not be used, an operational layer where data science and human insight jointly shape the experience, and an outcome layer placing well-being at the center of value. Insights from research on long-established Japanese firms illustrate how organizations sustain a clear identity—knowing "what we are" and "what we are not"—while adapting through innovation. The lecture argues that future competitive advantage will depend not only on data-driven optimization but also on the design of meaningful human experiences enabled by stakeholder value co-creation, presenting a framework for Human–AI Co-evolution that supports both organizational sustainability and societal well-being.
Lecture at
NEMO2026
Date/Time: Thursday, July 30, 2026 at 08:30 UTC+2/CEST
Neural Networks: An application in the Agricultural Domain
Artificial intelligence (AI), genetic algorithms (GAs), fuzzy rules (FR) are modernizing agriculture, contributing to solving global issues such as food security, food waste, digitalization and automation of processes and climate change management. By integrating Internet of Things (IoT) sensors, drones, satellite imagery and predictive analytics, traditional agriculture is becoming a high-performance, real-time data-driven industry capable of meeting modern societal needs. These technologies enable precise monitoring of soil, crops and livestock in real time, optimizing irrigation, fertilization and pest control transforming traditional decisions into data-driven processes. Automation through robots and autonomous machinery reduces operational costs and resource consumption (water, pesticides), reducing environmental impact. For countries with innovation potential, the adoption of these systems is essential to reduce the gaps in the European agricultural sector. Ultimately, AI does not replace the farmer or engineer but rather enhances his analytical and decision-making capacity, becoming an essential factor in building an efficient, resilient and sustainable agriculture and in modelling and optimization of different processes from the food engineering. This lecture starts by explaining the role of artificial intelligence (AI) in agriculture and in food engineering processes and how AI transforms traditional agriculture into a science based on accurate, sustainable and predictive data. Then continues with the basics regarding AI, GAs and AI-specific applications of monitoring and control from food safety. A key focus is the digitalization of the white wine fermentation process, where intelligent systems (integrating artificial intelligence and machine learning algorithms) can automatically monitor and control status variables. GAs are used for training weights of the neural network to increase prediction accuracy. A software application that controls white wine fermentation is presented.
Lecture at
NEMO2026
Date/Time: Thursday, July 30, 2026 at 09:30 UTC+2/CEST
Security Assessment: Leveraging Digital Twins
University of West Attica, Greece
SAPNet is an advanced framework designed to create and manage Security Digital Twins for the IoT ecosystem. By utilising a specialised ontology toolkit, SAPNet allows the transformation of IoT processes into high-fidelity Stochastic Petri Net (SPN) models that act as virtual mirrors of physical infrastructure. The platform bridges the gap between design and operation through two core capabili?es: (a) Dynamic Vulnerability Synchronisation: a modeller-friendly interface that allows for the real-time composition and updating of the security vulnerabilities list of the digital twin to ensure that the latter accurately reflects the evolving threat landscape of its physical counterpart, and (b) Proactive Security Intelligence: SAPNet delivers fast, high-accuracy security metrics at any stage of the twin's lifecycle. By simulating 'what-if' attack scenarios within the twin, SAPNet enables the modeller to validate the resilience of their twins before deployment, ensuring Security-by-Design in the digital transformation process.
Lecture at
NEMO2026
Date/Time: Thursday, July 30, 2026 at 10:00 UTC+2/CEST
Digitalization in Industry: A Hilti Consumable Use Case within OMiLAB
Global CTO, HILTI AG
TBC
Lecture at
NEMO2026
Date/Time: Wednesday, July 29, 2026 at 11:00 UTC+2/CEST