Navigating Unpredictability: Collaborative Networks in Non-linear Worlds

This paper explores the potential of mass collaboration to improve the quality of life for people with disability (PwD), a group representing approximately 16% of the global population. Accessibility remains a significant challenge for PwD, impacting physical navigation, information access, and social participation. Additionally, disparities in healthcare, education, employment, and social support exacerbate these issues. Addressing these challenges requires a collaborative, inclusive approach, including co-creating knowledge with input from PwD, caregivers, policymakers, and other stakeholders to ensure initiatives are relevant, effective, and sustainable. In this context, this work outlines a framework for enhancing well-being through mass collaboration, focusing on four key objectives: identifying current support initiatives across governmental, societal, and technological dimensions; illustrating the benefits of mass collaboration tools through user-centric scenarios; describing existing mass collaboration initiatives as benchmarks for developing a knowledge network; and proposing a conceptual architecture for a mass collaboration platform specifically designed for PwD.

As the global elderly population continues to grow, social isolation emerges as a critical factor contributing to the incidence of frailty. This paper explores the integration of social interaction functionalities within the Senselife framework, a service recommendation platform designed for frailty prevention in older adults. We propose enhancements to Senselife that facilitate community-building and social engagement through technology-driven interactions. By leveraging user-centered design, the paper discusses how enhanced social features can significantly improve the efficacy of our frailty prevention strategies, offering a holistic approach to elderly care. Our methodology includes the development of social interaction modules that encourage active participation and connectivity among elderly users, ultimately aiming to enhance their quality of life and reduce the risks associated with social isolation.

The advancement of technology enables better supporting the independence and well-being of elderly individuals. While there have been notable strides in healthcare solutions, there remains a need for innovative and non-intrusive approaches to enhance support for both the elderly and their caregivers. As a contribution in the context of Collaborative Networks, we present PRO-@GE, a collaborative communication and monitoring ecosystem platform designed to create an environment where different entities, including governmental and non-governmental organizations, individual or cooperative professionals, family members, and caregivers, can work together to offer more integrated and personalized services in elderly care. The functionality of this platform goes beyond providing essential information to individuals adhering to specific routines, such as medication schedules. It also enables families to stay informed about significant events in their elderly loved ones’ daily lives and interact with them at any time. Furthermore, PRO-@GE facilitates homecare provision companies in adopting novel business models, enabling the remote management of multiple individuals and ensuring prompt responses to health-related concerns. While technology plays a vital role, it is the collaborative network that truly enhances the social transformation and digital inclusion of the elderly in the modern era. Our contributions include a discussion on potential technologies for active aging, a business strategy, a conceptual model, and the implementation and validation of the proposed platform. Additionally, we discuss future directions involving Generative AI and its potential implementation in PRO-@GE.

Emerging challenges in the manufacturing industry, such as supply chain disruptions, political instability, and difficulty in accessing skilled workforce necessitate a more pragmatic approach to survive in this highly intertwined ecosystem. These approaches aim to empower manufacturing companies’ efficiency and agility while providing a certain degree of resilience. Hence, collaboration among stakeholders by sharing manufacturing resources and information is vital. However, despite the advancements in digital platforms for collaborative manufacturing, there is a need for effective planning of shared resources which is computationally intractable. Approaching this challenging problem from a multi-agent perspective brings new opportunities for modeling and solving. In a collaborative manufacturing network, as a multi-agent system, each manufacturing stakeholder, or agent, can pursue their objective, such as minimizing production time, reducing costs, or improving product quality while a coordinator agent monitors and ensures a solution that is best for all agents. This paper proposes systematic and heuristic methods for planning and scheduling collaborative manufacturing resources using a multi-agent modeling paradigm. The efficiency of the developed methods is benchmarked with randomly generated instances that show promising results for the manufacturing industry.

The Physical Internet (PI) is a modern logistics pattern based on resources and information sharing, modeled after the Digital Internet (DI). Running a PI network demands a participatory and distributed decision-making system, requiring communication protocols between nodes and optimization algorithms. Although different in nature, the Protocols and Algorithms (P&A) of the PI are often functionally equivalent to P&A of DI. This paper presents a systematic literature review on the control methods of a PI network. The results show that P&A can be “local”, designed to operate in a distributed manner; “global”, designed to centrally control the PI or parts of it; or for “orchestration”, designed to centrally define operational parameters while avoiding direct control. This paper also draws considerations on the maturity state of PI’s P&A, comparing them with those of the DI and with the process that led to their definition.

In the era of rapid technological growth and digitalization, small and medium-sized enterprises often need help to keep up with innovation, facing budget constraints and integration difficulties. The Industry Modular Operating System (IMOS) presents a different approach to software integration, distribution, and collaboration. It aims to boost application availability and integration by fostering collaboration between software developers and manufacturing companies. This platform simplifies the distribution of industry-specific resources, providing containerized software solutions through its integrated marketplace. The platform allows industry professionals to access various applications, services, and data tailored to their unique needs and requirements. By offering this dynamic ecosystem, IMOS aims to bridge the gap between development and production, promoting seamless interaction, problem-solving, and resource-sharing within a community of industry experts.

Digital twins became a major research topic throughout diverse domains that started around 2000 in manufacturing. This paper aims at establishing comprehensible digitalization environments by introducing patterns contributing to structuring digitalization efforts. An underlying conceptualization of digitalization environments consisting of digital models, digital-physical interactions, and physical realizations is proposed and used to depict interdependencies among digital and physical building blocks. Digitalization patterns that conceptualize monitoring and controlling techniques are selected based on the application scenario and raise requirements for processing mechanisms as well as for the realization with physical devices. To reflect on the conceptualization and the digitalization patterns, project MEASURE is introduced. Concepts are applied to the emergency exercise context by addressing the application scenario modelling, the interaction between digital-physical world as well as the realization. The discussion derives requirements from the digitalization patterns and reasons on the underlying meta model linking the building blocks of digitalization environments.

The increasing use of Digital Twin (DT) solutions in different domains demands for development of Verification and Validation (V&V) frameworks to guarantee the effectiveness of the implemented DTs. However, a considerable research gap has been identified in this field. Current state of the research is mainly concentrated on V&V of models in DTs and excluded important aspects such as data interoperability and functionality of DT services. To extend the scope of V&V, it is crucial to include these aspects. This paper presents a novel framework for V&V of DTs that considers all the mentioned aspects. This framework combines formal methods with software testing methods for V&V. It utilizes formal methods in a top-down manner and it will then use the software testing methods in a bottom-up manner.

Digital Twin (DT) provides a digital representation of a real-world entity (process or product) that is continuously synchronized with a specified frequency. In this regard, DT utilizes a set of models that capture the various aspects of the real system to provide a deeper understanding and analysis of its real counterpart. The data within the DT holds paramount significance and serves as the foundation for model updating, refining, interoperability, validity, usability, etc. Accordingly, DT requires rigorous data management throughout its entire life cycle. This paper explores data knowledge areas related to DT (i.e., data governance, architecture, modeling, integration, interoperability, quality, uncertainty, visualization, and security) and also highlights their best practices, and proposes a Data Management Framework for Digital Twin (DMFDT) to facilitate a better understanding of the DT data related requirements and proven practices. Validation and application of the DMFDT is done through the high-level DT architecture and a case study of the proposed framework is also presented by a DT developed to study the mobility system at the University of Bordeaux in France.

This article examines the role of digital twins in advancing regeneration processes within the circular economy. We focus on the capabilities of digital twins to model product life cycles and improve sustainability by supporting regeneration strategies. Our results show that digital twins facilitate accurate simulation and prediction of product lifecycle phases, which is essential for decision-making. Notably, real-world implementation of digital twins has improved resource management and operational efficiency, proving that they can transform sustainability practices in manufacturing industries. This study highlights not only the operational benefits of digital twins, but also their strategic role in promoting environmental sustainability through improved resource efficiency. In addition, it identifies opportunities and challenges, which provides a model for using digital twins to obtain environmental and economic results in the manufacturing industry.

Digital twins have emerged as a promising technology in sustainability efforts, particularly in managing critical resources. Of these resources, water has assumed critical importance across various industries, necessitating effective management strategies. This paper explores the application of digital twins that include not only product flow but also other as energy and fluid flow. Our main focus is to build a Digital Twin that serves as a decision-support tool in the context of water distribution across diverse industries with varying needs. Specifically, we propose a digital twin framework aimed at analysing and optimizing water consumption within a collaborative network comprising various stakeholders, for example, water suppliers, regulatory authorities (such as the Prefecture), network controllers, and end-users within a circular economy context. Furthermore, a case study is presented to illustrate the implementation of the initial phases of the proposed framework.

Circular economy (CE) is considered to be the business model of the future, since it enables decoupling of economic growth and resource consumption. Digitalization is an enabler for companies to accomplish the transition to circular business models, as it enables automated data sharing and usage, but it also poses an enormous challenge. The data required for the implementation of circular business models is generated during the entire life cycle of a product. Digital product passports (DPP) represent a solution for the exchange of product-related data across the entire life cycle and various stakeholders. So far, they have hardly been integrated into production planning and control (PPC) systems. This paper describes requirements, specific use cases and related data flows for an integration of DPP and PPC systems. Finally, a model is presented that enables event-driven creation and use of data for the bidirectional integration of DPP into PPC systems.

Conventional painting inspections in the wind industry rely on human labour to visually inspect the parts, and the mental and physical fatigue of the operator influences the accuracy. The advances introduced on camera sensors, robots, and artificial intelligence algorithms can create a collaborative working environment between operators and automated visual inspection system.

One of the main challenges for the automatic painting inspection system is the integration with autonomous guided vehicle to perform the inspection routes, and the communication of the data generated during the inspection (defect detection, location, etc.) to the augmented reality platform to send the specific information to the operator to perform the painting repairing operation.

Current research aims to define a framework where integrating non-destructive inspection technologies, autonomous guided vehicles, and augmented reality will allow a symbiotic collaboration between humans and robots. The interoperability between these three solutions will increase the Human-Robot Collaboration in a wind tower painting process, improving product quality assurance.

New conflicts, population growth, and global warming are only some of the reasons that have led to a slowdown in achieving the Sustainable Development Goals (SDGs) companies had set for 2030. In this context, resource efficiency can help manufacturing companies improve their environmental sustainability performance towards Zero Waste. Artificial Intelligence (AI) has been applied to improve resource efficiency; however, its wide scope and lack of a universal definition have made it challenging to identify this technology’s potential clearly. This paper presents an overview of AI use in manufacturing to enhance resource efficiency. A systematic literature review (SLR) was conducted using the PRISMA guidelines. The articles have been assessed against the resource efficiency enhancement and strategic impact within the company and classified under AI task and AI algorithm used. The twenty-five articles explicitly aimed at improving resource efficiency were inserted in a classification framework. The research revealed the prominence of energy efficiency objectives, machine learning algorithms, and a limited number of explored manufacturing applications and fields. However, it uncovered significant gaps that should be assessed in the future.

By providing a consolidated view of demand, production capacity and inventory status, the master production schedule (MPS) enables collaborative networks to be optimised, planned and coordinated, which leads to more robust strategic decision making. In this context, flexibility, agility and automation are key elements to consider, and make machine-learning methods strong candidates for problem modelling. This paper researches some modelling alternatives to address the MPS problem subject to the eventuality of unsatisfied demand due to production capacity and inventory constraints by the deep reinforcement learning (DRL) method. The modelling of observation space, action space and reward function, and the choice of the DRL algorithm, among some of those currently at the forefront of the technique, are analysed and discussed. The sensitivity to the problem dimension, as a function that depends mostly on the number of considered product references, is also approached. As a major contribution, this study constitutes a valuable step prior to final modelling and subsequent validation by clearing the way for a wide range of possible implementations.

The Circular Economy is one of the promising solutions to address environmental challenges, such as climate change, resource depletion, pollution, and waste. However, the implementation of the Circular Economy remains limited due to economic and financial barriers. This paper proposes to overcome these barriers by designing a decision support system for hyperconnected circular supply chain network design, leveraging opportunities offered by collaborative networks and the Physical Internet. A methodology inspired by Herbert Simon's decision-making theory is developed, including the intelligence, design, choice, and implementation phases. Specifically, a matrix for strategic options is proposed to elaborate scenarios for the design phase. A case study on the electric retrofit of Internal Combustion Engine vehicles illustrates the proposed methodology.

Transport and mobility play a crucial role in collaborative networks, facilitating access to resources. While this strengthens economic and social integration, the expansion of collaborative networks poses major challenges in terms of effectiveness, sustainability and equity. Improving transport services is consequently crucial, particularly in sparsely populated areas, to reduce economic and social disparities. If urban areas benefit from Smart City principles to optimize the flow of people and goods, rural areas are often marginalized. Some authors have demonstrated qualitatively the potentiality of using Physical Internet and synchromodality paradigms to change this situation. But no quantitative demonstration has been done yet. The purpose of this research work is to design and present our multi-agent vision of a simulation framework for evaluating synchromodal transport solutions in low-density ecosystems. Composed of four components (demand estimator, transportation planner, simulator engine and performance assessor), this framework is intended to be tested on ECOTRAIN case study.

This 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.

Cities are complex entities that consists of many systems that interact with one another. Cities are expected to grow in size and diversity, face diminishing resources, climate decay, which all pose challenges to the governance of cities. These call for new ways of understanding cities and innovative approaches to simulate cities and their sub-systems. Complex Adaptive Systems have been used to describe and create simulation models of cities. This paper presents an agent-based model of a city, CitySim, which focuses on how traffic in the city evolves as various entities within the city change. The main research question this paper addresses is how could we create a model of a city as a Complex Adaptive System? The CitySim model was created using the Design Science Research Method. The benefits and challenges of the model are described through simulation experiments using the model.

Sales and Operations Planning (S&OP) is essential for aligning strategic plans with daily operations. However, the dynamic nature of modern Collaborative Network organizations presents challenges due to uncertainties and risks. This research aims to fill this gap by creating a maturity model to manage uncertainties in the S&OP process. A literature review was conducted, examining six key dimensions: Process, Tools, People, Objectives, Decisions, and Key Performance Indicators (KPIs). The review uncovered gaps and limitations in current research on uncertainty management in S&OP, underscoring the need for a maturity model for Collaborative Network organizations. As a result, a three-stage maturity model was developed to evaluate Collaborative Network organizations’ S&OP practices, providing guidance to manage uncertainties within the S&OP process. The study highlights the need for future research to develop well-defined procedures for effectively addressing uncertainties, including scenario planning methodologies and decision-making frameworks that account for uncertainties.

Product Service System (PSS) design is shaped by different needs, requirements, and stakeholders. It involves understanding the needs and preferences of various stakeholders, balancing conflicting requirements, and integrating different sustainability concerns. Thus, a successful PSS design requires a systematic approach to effectively guide multi-criteria and collaborative decision-making. This article focuses on the collaborative decision-making process for the sustainable design of PSS, with a focus on integrating economic, environmental, and social considerations. Specifically, our research explores the application of Multi-Actor Multi-Criteria Decision-Making techniques, such as PROMETHEE II, to address the intricate interplay of three sustainability dimensions. Through collaborative innovation and co-creation, facilitated by the involvement of multiple stakeholders, we aim to promote the creation of PSS solutions that not only enhance economic viability and environmental sustainability but also contribute positively to social well-being.

Demand forecasting is critical within collaborative networks, enabling suppliers, manufacturers, and retailers to synchronize their operations and achieve enhanced supply chain efficiency. Despite a wealth of research on time series forecast model selection and the availability of numerous forecast models, selecting the most appropriate model for a specific time series remains a challenging task. In this study, an automatic demand forecast model selection procedure is proposed that includes a wide range of statistical and machine learning forecast models. The optimization of the hyperparameters is performed on all the models. The study is validated on M3 monthly data, outperforming all submitted methods and demonstrating significant improvements in terms of accuracy. The approach was also applied to a collaborative network company.

Transitioning to a circular economy fundamentally changes the traditional, linear economic model. Establishing circular strategies requires adapting and expanding the roles within the value system to realize the decoupling of economic growth and resource consumption. In particular, a combination of several value-retention strategies implemented in parallel can increase the ecological and economic potential of circular economy. However, there is a lack of description of this type of value system in relation to the new required activities, and the resulting material and information flows within the value system. For this reason, literature research and exploratory analyses through expert workshops were used to identify variants for implementing the value system. The results include the definition of five possible variants of a value system for the parallel implementation of value-retention circular strategies in the white goods industry. The five possible variants are necessary to enable sustainable collaboration strategies between different stakeholders from which all of the stakeholders can benefit. The new activities for enabling the various value-retention strategies, e.g., product evaluation or disassembly, are distributed differently across the stakeholders of the value systems per variant. This leads to distinct material and information flows per variant.

Canvas have for long been embraced as a popular design tool. Initially aimed towards, business model development, the model of a one page, visual and collaborative tool has spread to the design of many different artifacts. Digital platforms, with its conjugation of business, technical, and social facets have benefited from the canvas model for its design practices, from both scholars and practitioners. Nonetheless, the recent push for more industry-specific and holistic digital platform research agenda is bound to have an impact in the tools used for platform design. In this paper, we apply a literature review method to examine existing canvas, inspired by the Business Model Canvas, as tools for the design of digital platforms. Using conceptual platform design research as a frame of reference, we review eight canvas specific for digital platform design, highlighting four critical limitations in their application regarding (1) adopted broad platform conceptualizations; (2) a restricted focus on business elements; (3) a lack of focus on platform evolution; and (4) a lack of guidance in the translation of canvas to explicit platform design propositions and requirements. By addressing these limitations, we set a path for the evolution of canvas as collaborative tools that can better support the more comprehensive and nuanced approaches required for the design of digital platforms acting in an evermore non-linear, volatile, uncertain, complex, and ambiguous environments.

In modern organisational management research, territorial characteristics have a non-negligible impact on organisational collaboration. Territoriality’s special qualities may have a direct and significant influence on the effectiveness of organizational business collaboration, besides fostering the development of local knowledge to support organizational collaboration indirectly. Therefore, the aim of this paper is to explore how organisational territoriality characteristics affect the efficiency of collaborative networks. To achieve this goal, we propose a method called Territorial Feature Dimension Mining Method Based on Business (TFD-B) on the basis of the Collaborative Context Metamodel. Firstly, when building the collaborative network, the unique business scenario factors of organizational collaboration are taken into account completely. Subsequently, we conduct further mining and sorting to identify the territorial factors that might have an impact on the collaborative network. Finally, the State Grid Power Supply Company is used as a real case to validate the methodology, and we employ QAP analysis to quantitatively investigate the specific influence relationship between these characteristics and the efficiency of the collaborative network. The main contribution of this study is the proposed TFD-B method, which effectively mines the territorial characteristics affecting collaborative networks at the business level.

LTE-R will definitely substitute GSM-R to railway communications, especially in high-speed trains, due to its inherent improvements such as coverage and performance. The placement of cells along the route of the train is of paramount importance. GSM-R is a quite old method that suffers from the drawbacks of the GSM technology, while LTE is faster and more reliable. The concept of substituting the GSM-R with LTE-R is a further improvement to railway communications. In this paper we take onboard a linear LTE-R cell network that caters to high-speed train passengers and nearby regions. Addressing inter-cell interference, it’s vital for cells to adapt coverage dynamically, zooming in or out to optimize costs and maintain uninterrupted coverage. We conceptualize this challenge as a linear graph-coloring problem with two colors, enabling cells to transition between micro and macro configurations as required. Our goal is to minimize costs while guaranteeing seamless coverage. This collaborative approach enhances service quality for train passengers and neighboring areas by effectively meeting communication needs through adaptive coverage adjustments.

Half a century from the dawn of the digital, after the last couple of decades of intense innovation in products and services, it is time to rethink the digital ecosystem. This paper delves into network effects that lead to the concentration of online business in a few providers, exploring some of its emerging challenges and risks within increasingly complex data collection and sharing environments. Drawing on various studies, we propose conceptualizing a model that contributes to democratizing network effect on understanding digitalization as a sociotechnical process involving human interests and interactions, which is added by public participation. The paper discusses the concept of sovereign citizen on digital citizenship (SCOD), structured as a collaborative network of public and private interests. It suggests that the need to give citizens control of their own data generated when accessing digital services is central. The paper explores primary potentialities, such as data privacy, control, and autonomy, and develops some core ideas and recommendations for its operationalization at the member-state and European levels.