Eco‐Holonic 4.0 Circular Business Model to Conceptualize Sustainable Value Chain Towards Digital Transition

The purpose of this paper is to conceptualize a circular business model based on an Eco-Holonic Architecture, through the integration of circular economy and holonic principles. A conceptual model is developed to manage the complexity of integrating circular economy principles, digital transformation, and tools and frameworks for sustainability into business models. The proposed architecture is multilevel and multiscale in order to achieve the instantiation of the sustainable value chain in any territory. The architecture promotes the incorporation of circular economy and holonic principles into new circular business models. This integrated perspective of business model can support the design and upgrade of the manufacturing companies in their respective industrial sectors. The conceptual model proposed is based on activity theory that considers the interactions between technical and social systems and allows the mitigation of the metabolic rift that exists between natural and social metabolism. This study contributes to the existing literature on circular economy, circular business models and activity theory by considering holonic paradigm concerns, which have not been explored yet. This research also offers a unique holonic architecture of circular business model by considering different levels, relationships, dynamism and contextualization (territory) aspects

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

An overview on European Manufacturing research visions and roadmaps as an answer to economical and societal challenges and opportunities

This document provides an overview on European Manufacturing research vision and roadmaps. In 2017, a year of crossroads for the research community, a joint and collaborative Europe-wide effort is needed to define the main research priorities. This paper provides an overview on the ongoing roadmapping work and proposes some research lines that could become the core of the European manufacturing research strategy

Conceiving a Digital Twin for a Flexible Manufacturing System

Digitization and virtualization represent key factors in the era of Industry 4.0. Digital twins (DT) can certainly contribute to increasing the efficiency of various productive sectors as they can contribute to monitoring, managing, and improvement of a product or process throughout its life cycle. Although several works deal with DTs, there are gaps regarding the use of this technology when a Flexible Manufacturing System (FMS) is used. Existing work, for the most part, is concerned with simulating the progress of manufacturing without providing key production data in real-time. Still, most of the solutions presented in the literature are relatively expensive and may be difficult to implement in most companies, due to their complexity. In this work, the digital twin of an FMS is conceived. The specific module of an ERP (Enterprise Resources Planning) system is used to digitize the physical entity. Production data is entered according to tryouts performed in the FMS. Sensors installed in the main components of the FMS, CNC (computer numerical control) lathe, robotic arm, and pallet conveyor send information in real-time to the digital entity. The results show that simulations using the digital twin present very satisfactory results compared to the physical entity. In time, information such as production rate, queue management, feedstock, equipment, and pallet status can be easily accessed by operators and managers at any time during the production process, confirming the MES (manufacture execution system) efficiency. The low-cost hardware and software used in this work showed its feasibility. The DT created represents the initial step towards designing a metaverse solution for the manufacturing unit in question, which should operate in the near future as a smart and autonomous factory model.Thanks are due to Elkartek 2022 project LANVERSO, and in some sections (simulations) to Basque government university group IT 1573-22

The last five years of Big Data Research in Economics, Econometrics and Finance: Identification and conceptual analysis

Today, the Big Data term has a multidimensional approach where five main characteristics stand out: volume, velocity, veracity, value and variety. It has changed from being an emerging theme to a growing research area. In this respect, this study analyses the literature on Big Data in the Economics, Econometrics and Finance field. To do that, 1.034 publications from 2015 to 2019 were evaluated using SciMAT as a bibliometric and network analysis software. SciMAT offers a complete approach of the field and evaluates the most cited and productive authors, countries and subject areas related to Big Data. Lastly, a science map is performed to understand the intellectual structure and the main research lines (themes)

Behavioral anomaly detection system for the wellbeign assessment and lifestyle support of older people at home

The wellbeing assessment of older people is becoming crucial in today’s era of aging and home care in order to provide the best possible care. New technologies are being used to assist older people at home, which generates an extensive amount of health and wellbeing information. The application of artificial intelligence algorithms to this healthcare and wellbeing data can enhance patient care and provide support to professionals by reducing their cognitive load. These algorithms can detect anomalous physiological, physical, and cognitive conditions in older individuals, which can help to identify emergency situations, or the early detection of an emerging health condition. However, while there has been relevant research in the field of anomaly detection for various engineering applications, there is little knowledge about healthcare and wellbeing-related anomaly detection. To this end, in this article, we propose an innovative system for detecting behavioral anomalies for older people that are being monitored at home with the aim of improving their lifestyle and wellbeing as well as the early detection of any physical or cognitive conditionThis project has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No 857159 SHAPES Project and from the Basque Government’s HAZITEK innovation program under Grant Agreement No ZL-2021/00025 SERWES Project

European smart specialization for Ukrainian regional development: path from creation to implementation

The focus of the research is to develop recommendations of smart specialization (SS) for Ukrainian policymakers using European approaches. The authors revealed that the main SS projects are presented in such sectors as agri-food, industrial modernization and energy. More than 12 EU countries were the plot for conducted analysis of SS, as a result of which the level of activity of each country was determined. The creation of consortiums, including SMEs, associations, universities and other participants, disclosed the successful way of SS realization. The structure of SME’s innovative potential in Ukraine was identified underlining their main characteristic features like types of innovations and innovative activity, differentiation according to enterprise size, their regional distribution. The authors explored lack of innovations on regional and national level and significant territorial disparities, which could be eliminated through policy implementation of regional SS. The existing legislative norms for possibility of SS implementation in Ukraine were analyzed due to correspondence with the EU ones. The analysis provides the opportunity to consider them only as general framework documents without any action plans and sectoral prioritization at all. The weak points of these law documents are emphasized. As a result of research, the authors developed recommendations presented by direct action plan for Ukrainian policymakers, which include such activities as underlining key priorities (especially ICT applicability in every SS project) and their correspondence with the EU ones; eliminating regional imbalances by focusing on innovation development and reorientation of some regions according to SS priorities; respecting regional existing capacities; providing organizational mechanism for cooperation of stakeholders and financial mechanism for SS support through the EU structural funds

Exploring the influence of industry 4.0 technologies on the circular economy

In the last decade, both Industry 4.0 technologies and the circular economy have expanded exponentially and they have received epistemological attention. However, there is a lack of studies about the influence that each of these technologies has on the main areas of action covered by the circular economy. This study responds to this gap by investigating the influence of the major technologies: Additive Manufacturing, Artificial Intelligence, Artificial Vision, Big Data and Advanced Analytics, Cybersecurity, Internet of Things, Robotics, and Virtual and Augmented Reality on the main areas of action covered by the circular economy. Namely, reduction of inputs consumption, reuse, recovery, recycling and reduction of waste and emissions. An initial study, based on a survey of 120 project managers, and a multiple case study of 27 projects, through 31 personal interviews and review of internal and external documentation have been conducted in order to investigate the real influence of each technology on the circular economy. Overall, the results confirm the existence of a wide range of influences that Industry 4.0 technologies offer to companies for improved circularity. These improvements are mainly related to reduce material and energy consumption, and waste and emissions generation. However, there are important differences between the potential impacts of each technology. In particular, there is most evidence of the positive impact of additive manufacturing and robotics. Likewise, the results obtained suggest the need to continue exploring the new impacts generated by the continuous development and integration of technologies.This study was funded by the Basque Autonomous Government (Research Group GIC 15/176) and the project METASTANDARDS, funded by the Spanish Ministry of Science, Innovation and Universities, the Spanish State Research Agency (AEI). We also acknowledge the technical and human support provided by Circular Economy UniversityCompany Classroom (Faculty of Engineering Gipuzkoa, UPV/EHU, Provincial Council of Gipuzkoa)

Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management

This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services