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Participation as Fuel for Transformation - An Approach to the Interrelations Between Digitalization, Participation and Values in NPOs
No full textPart 8: Collaborative Networks as Driver of Innovation in Organizations 5.0: ParticipationInternational audienceNon-profit organizations (NPOs) differ from profit-oriented organizations not only in their motivation but also in their internal organizational structures as well as in their network embedding. In the implementation of digital structures within an NPO’s very commonly, managerial methods that were developed for for-profit organizations, are applied because of the lack of suitable alternatives. Yet, it is important to respect the organizational and motivational differences, as they have a crucial impact on the success of digital transformation processes. This paper wants to explore the state-of-the-art, both practical and theoretical, how digitalization transformation and organizational value can be brought together and be assessed by a participative approach, and how this can be a success factor in the development of the organization. The research is based on a mixed-methods approach that includes a literature review and an expert workshop with practitioners
Integrating Perception and Systemic Theories in Collaborative Networks-Enhancing Adaptability and Resilience in a Nonlinear World
No full textPart 3: Emotions and Collaborative NetworksInternational audienceThis paper explores the integration of Hoffman’s Perception Theory and Klir’s System Theory to enhance Collaborative Networks (CN). Hoffman’s theory emphasizes subjective experiences and cognitive processes, focusing on survival and growth, while Klir’s theory introduces a hierarchical model of systems, highlighting the importance of supportive variables including time and space in dynamic situations. By combining these perspectives, the paper proposes a dual-framework approach to improve adaptability and resilience in CNs. Using the COVID-19 pandemic response and Global Supply Chain Management as case studies, we demonstrate the practical advantages of this integrated model in navigating complex, nonlinear environments
Leveraging Collaboration for Industry 5.0: Needs, Strategies and Future Directions
No full textPart 7: Collaborative Networks as Driver of Innovation in Organizations 5.0: ModelsInternational audienceThis paper analyzes the identified key elements that are needed by the Industrial sector to ensure the seamless transition of enterprises from the “4.0 paradigm” – based on digitalization and technologies, to the “5.0 paradigm” – focused on resilience, “green” mindset and human-centric approach. The basis of this analysis is a research survey conducted within a century-old European technology company, focused on automotive products, with over 20 locations spread on more than 10 countries and on 4 continents. The goal of this paper is to identify both the needs of the Industrial sector concerning the “5.0 Transition” and the focus points for the CoDEMO 5.0 project, concerning what the Industry players expect from this Consortium. Moreover, this paper aims to define a process in which these two elements combine harmoniously so that a continuous positive feedback loop is established and, if implemented on a wider scale, could establish a new way of working among the EU Industrial actors, thanks to the collaborative network that shall be established. Concerning this study, such a collaborative network is composed of a “triple-helix” formed by the Customer (OEM manufacturer), the Supplier (automotive company), and the Academic Partner. Such partnership will enable the OEM to come out with new, and groundbreaking products that stand out from their competitors, thanks to the supplier that is creating these products which in turn, can develop them thanks to the academic partner that prepares highly skilled professionals for the current and future emerging technologies from Industrial sector like Artificial Intelligence, machine learning, blockchain, quantum computing, IIoTs, etc
Sovereign Citizen on Digital Regulated Services Ecosystem
No full textPart 9: Trust and Trustworthy Technologies in Collaborative NetworksInternational audienceThe infrastructure and application systems shaping digital society have evolved rapidly over the last two decades, driven by unprecedented collaborative efforts within the industry and motivated by the need to scale. The demand for reduced operational costs and enhanced scalability motivated many to join the pioneers who decided to share their data centers in public clouds. This shift has accelerated new business ventures across various societal sectors. However, the absence of a unified reference model has hindered the creation of a trustworthy digital ecosystem that can be both simple and secure for citizens and companies. A pressing concern remains: how can citizens trust that their data is maintained private by business providers of different scales and following different models? Building on previous research focused on unified mobility payment services and trusted digital services for citizens, this position paper proposes a strategy for a unified model that enables citizens to adopt a service provider to “navigate” across the digital ecosystem. While the payment perspective is included, herein our primary focus is on the data. The key idea is to offer citizens a unified digital identity and a personal “safe vault” on the “digital ecosystem”. This ecosystem is envisioned to develop parallel to the banking system, where a regulated model assures clients that their savings are secured. The paper discusses the Secure Citizen on Digital ecosystem (SCOD) concept and emphasizes the need for an open business-technology ecosystem based on the Collaborative Networks concepts
Evaluating Privacy Patterns Within Collaborative Frameworks for AI Ecosystem Development
No full textPart 5: Collaborative Ecosystems: Technologies for Resilient FuturesInternational audienceRobust data privacy is crucial for mitigating financial, legal, and reputational risks in organizations. While legislative frameworks like the EU GDPR mandate comprehensive data protection measures, integrating these into information systems presents significant challenges. Privacy Patterns (PP) aim to bridge this gap by translating legal requirements into actionable data protection strategies, yet their effectiveness in practical scenarios is not well-documented. This study explores the applicability, effectiveness, and limitations of PP in the collaborative development and operation of an AI-driven ecosystem aimed at automating the handling of legal declarations to enforce consumer rights
Integrating AI in Supply Chain Management: Using a Socio-Technical Chart to Navigate Unknown Transformations
No full textPart 1: AI and CollaborationInternational audienceFor decades, the collaborative networks community has studied supply chains, focusing on trust, visibility, collaboration, and innovation, with emergent technologies being a key area of research. The rise of digital technologies has led to extensive studies on supply chain digital transformation. With the surge of AI-based technologies, there is an increasing body of research on AI's human and social impact on Supply Chain Management (SCM). However, while Socio-Technical Systems (STS) thinking has been applied to digital transformations, it has not yet addressed AI-induced changes in supply chains. This paper synthesises recent research on AI integration in SCM and the use of STS thinking in AI systems design. We propose a mapping approach for profiling AI-induced supply chain transformations for strategic design. We also present the Supply Chain Socio-Technical AI (SC-STAI) profiling tool in practice, demonstrating how it maps supply chain participants’ current and desired states regarding AI integration
Hybrid Collaborative Networks in Energy Ecosystems
No full textPart 1: AI and CollaborationInternational audienceHuman-AI collaboration in renewable energy ecosystems can revolutionize the way communities achieve sustainable and efficient energy solutions. This synergistic approach combines the analytical prowess of AI with the expertise of human decision-making, fostering a comprehensive strategy for energy management. AI excels in tasks that are repetitive and mundane, while humans excel at decision-making tasks that reflect their preferences and community dynamics, ensuring that AI-driven solutions are aligned with societal values. This collaborative approach, representing a case of hybrid collaborative network, can lead to optimizing energy use, reducing dependency on the grid, and empowering communities to lead the energy transition, fostering more resilient and sustainable energy ecosystems. In this context, we explore possibilities of achieving “meaningful” energy conservation practices in a Collaborative Energy Ecosystem (CEE) using human-AI collaboration. As such, we expand and discuss the CEE model from the perspective of hybrid human-AI collaboration, present pilot implementation results, and discuss future research directions
Simulation-Based Learning for Agri-Food Industry: A Literature Review and Bibliometric Analysis
No full textPart 6: Simulation FrameworksInternational audienceThis paper performs a literature review and bibliometric analysis to assess studies of simulation-based learning in the context of food industry and agriculture. The articles published in Web of Sciences database, between 2000 and January 2024 were considered. Several articles examine the application of simulation models in food engineering higher education while others are focused on processes conduct/safety/tools/supply chain from food industry area and monitorization/modeling/prediction in agriculture. The paper presents the status of the research in the context of simulation-based learning for agri-food industry and can serve as a basis for future studies regarding the transition from agri-food 4.0 to 5.0 and stimulating the transfer of IT skills, knowledge and digital technologies to agriculture, and food industry, providing updated competencies requested by the work market
A Constraint-Based Greedy-Local-Global Search for the Warehouse Location Problem
No full textPart 3: Data Mining/ModelingInternational audienceConstraint optimization problems offer a means to obtain a global solution for a given problem. At the same time the promise of finding a global solution often comes at the cost of significant time and computational resources. In contrast, greedy search and local search represent two alternative approaches, which can lead fast to local optima. In this paper, we explore the advantages of incorporating greedy search and local search into constraint optimization methods without forsaking the pursuit of a global solution. The different instances of our global search process are designed to initially behave akin to a greedy search and a local search while they are integrated in a global search approach. This dual strategy aims to achieve two key objectives: firstly, it accelerates the attainment of an initial solution, and secondly, it ensures that this solution possesses a high level of optimality. Even though constraint programming theoretically finds a global optimum, in practice, this may not always be the case due to time and hardware limitations. Our approach improves upon the general Branch-and-Bound approach in constraint programming, aiming to find a good or optimal solution faster than the conventional method. Finally, we validate our findings using the warehouse location problem as a case study
Lightweight Inference by Neural Network Pruning: Accuracy, Time and Comparison
No full textPart 2: Graphs/Neural Networks/Machine LearningInternational audienceThis paper addresses the application of neural networks in resource constrained edge-devices. The goal is to achieve a speedup both in inference and training time, with minimal accuracy loss. More specifically, it brings to light the need for compressing current models, which are mostly developed with access to more resources that the device that the model will potential run on. With the recent advances of Internet of Things(IoT) the number of devices has and is expected to rise. Not only are these devices computationally limited, but their capabilities are nor homogeneous nor predictable at the time of the development of a model, as new devices can be added anytime. This creates the need to quickly and efficiently produce models that fit each devices specifications. Transfer learning is a very efficient method, in terms of training time, but confines the user to the dimensionality of the pretrained model. Pruning is used as a way to overcome this obstacle and carry over knowledge to a variety of model, that differ in size. The aim of this paper is to serve as an introduction to pruning as a concept, as a template for further research, quantify the efficiency of a variety of methods and expose some of it’s limitations. Pruning was performed on a telecommunications anomaly dataset and the results were compared to a baseline, in regards to speed and accuracy