Nicolò Saporiti

In the context of Industry 5.0, the development of skills through experiential learning is becoming increasingly important in industrial engineering education. However, traditional assessment methods often fail to capture the effectiveness of these activities and the actual skills acquired by students. This gap calls for new, more adaptive and dynamic approaches to evaluation. To address this need, this study proposes an innovative solution that employs recent Generative Artificial Intelligence (GenAI) technology to develop a dynamic and self-adaptive assessment system designed specifically for experiential learning environments. The proposed model uses a web-based, self-correcting quiz integrated with ChatGPT via OpenAI’s API. Questions are dynamically generated according to Bloom's taxonomy, and the student's responses are checked in real time to adapt the subsequent questions accordingly. At the end of each session, the system automatically provides both quantitative scores and qualitative feedback for each response and for the overall performance. An application case was conducted in i-FAB, the learning factory at Università Carlo Cattaneo - LIUC, in which students were involved in an experiential learning activity aimed at learning and practicing the Data Analytics skills considered fundamental in the Industry 5.0 context. The results obtained from the test of the method demonstrated its validity and consistency with the set objectives. The proposed method is thus a significant contribution to experiential learning research, filling the gap of inadequate assessment systems while leaving room for possible future improvements.

The scope of this study is the development methodology of a Gesture Recognition System (GRS), making use of Artificial Intelligence (AI), and integrated into the Manufacturing Execution System (MES) provided in i-FAB, a learning factory in Università Carlo Cattaneo – LIUC, aiming at improving time tracking and displacement of unnecessary movement on the shop floor. Concerning the Human-Centricity pillar of the Industry 5.0 paradigm, this approach aims at enhancing well-being through the reduction of repetitive and inefficient tasks hence, making the MES systems more user-centric. The developed GRS can recognise certain hand movements related to various inputs to the MES so that users spend less time using a keyboard and touching devices. This leads to a reduction of time loss due to the time associated with tracking time and activity data which in the end optimizes production. Moreover, the system also lessens ergonomic risks that pertain to the tasks being performed, since unnecessary and repetitive movements of the operators are greatly reduced. The research findings indicate that this considerably improves the pace of completion of time and activity tracking on MES systems in a way that is designed to meet the requirements of Industry 5.0 which is focused on promoting a collaborative, safe and healthy environment.

The interest of the literature in the theme and application of Virtual Reality in industrial contexts has increased in the last years. Virtual Reality has proven to be a promising technology in a vast variety of applications. Among these, Virtual Reality can be used as a tool to enhance the training of operators under certain conditions, and it can be considered as a way to perform a gamification of the training process. In this research, an application of Virtual Reality to this extent is presented. In particular, the research shows how to implement this technology to provide several advantages to companies, such as the possibility to train operators without line stops as well as to perform training in environments that are not physically developed yet. Indeed, the research presents the development of an application based on Virtual Reality technology in the pharmaceutical sector, aimed at enhancing the training of the operators that need to avoid the cross-contamination of production lots in the white room of the production facility. The results show how to build and structure an application of Virtual Reality that can be used to certify operators in performing certain delicate operations. In doing so, the procedure to develop the application, as well as the tools used in the developing process are presented. Moreover, the research shows also how Virtual Reality technology can be used to train operators to perform operations in environments not yet developed. Finally, an analysis of the response to the gamification of the training process from the operators is presented, to show feedback and issues that may arise in the usage of the application.

Ten years after the first appearance of the term “Industry 4.0” in the Hannover fair, the advancements of this paradigm are manifold. Among the technologies that constitute Industry 4.0, i.e., Industrial Internet of Things, cloud computing, additive manufacturing, vertical and horizontal integration, big data and analytics, cyber-physical systems, simulation, augmented reality and cyber security, a variety of applications have been developed in relation to products, factories, and cities. From an industrial point of view, the changes at the shop floor and supply chain level will affect the way the supply chain and operations management activities will be conducted. Mapping the path of this growth highlights today’s opportunities and challenges related to Industry 4.0 and helps researchers and practitioners in taking chances and dealing with issues. Hence, the aim of this work is to identify the main trends of evolution of this paradigm by means of a review of literature on the topic. To achieve such a result, this research adopts a dynamic and quantitative bibliometric method including works citations, keywords co-occurrence networks, and keywords burst detection. The aim is to study and analyze the main contributions to this research area and identify prevalent topics and trends over time. The analysis performed on citations traces the backbone of contributions to the topic, highlighted within the main path. Keywords co-occurrence networks depict the prevalent issues addressed, tools implemented, and application areas. The burst detection completes the analysis by identifying the trends and most recent research areas characterizing research on Industry 4.0.

The combination of the industrial paradigms of Circular Economy (CE), Industry 4.0 (I40), and Lean Production (LP) has been debated by academics and practitioners in the last years, demonstrating that I40 technologies and LP enable CE, and that I40 and LP mutually support each other. The analyses conclude that several economic and environmental benefits can be achieved from these synergies. However, given most of the studies in literature focused on the dual combination between these paradigms, there is a need for understanding how all three are related to each other simultaneously. Accordingly, the proposed research defines a model that shows how the circular transition of manufacturing companies can be enhanced through the exploitation of LP practices and the key enabling technologies of I40. To achieve this result, the proposed research conducts a bibliometric review of the literature extracted from Scopus, exploiting a systematic literature network analysis methodology to detect and then analyze clusters of themes. The study observed that employing LP practices and I40 technologies support manufacturing companies towards a more effective circular transition and proposes future research avenues to be addressed by future studies at the intersection between the topics of I40, LP, and CE.

The interest of the literature in the last few years around the theme of Digital Twins (DT) and its connection with the pillars of Industry 4.0 (I 4.0) paradigm, in particular relating with Virtual Reality (VR) and Augmented Reality (AR) technologies has strongly grown. As a matter of fact, since 2018 it can be noticed a steady increase in publications that relates to these technologies, especially DT and VR. However, even if the body of the literature is rich in journal papers, conference papers and book chapters, it lacks specific literature reviews that connects these technologies. Therefore, the main aim of this paper is to provide a clear overview of the applications of VR and AR concerning the brand-new concept of Digital Twins. In doing so, this study conducts a bibliometric review carried out with a Systematic Literature Network Analysis (SLNA) on the relationship between VR, AR and DTs. Firstly, by analysing the literature, this article describes the main contributions to the research on VR and AR-related DT applications. Secondly, this paper aims at identifying the main topics to which DTs have been related when matched with VR and AR concepts, and therefore to identify the importance of these technologies on DTs applications. Finally, the paper provides an analysis of the past, present and future trends of DT applications in the literature when related to VR and AR. In doing so, a new visual analysis and representation of keywords trend in time, based on the co-word network analysis typical of the SLNA, is presented, i.e. the Keyword Trend Detection (KTD). The study leads to two main conclusions. The first is related to contributions branches identification. Indeed, two main branches of contributions have been identified; the first is related to the concept of virtualization of manufacturing resources, while the second is focused on the applications of DT and VR. Secondly, as a result of the application of keyword clustering techniques, this research identified five clusters of research streams, i.e., AR-, Robotics-, Virtual-, I 4.0 pillars-and DT-related clusters.

The emergence of technologies linked to the Industry 4.0 paradigm is increasingly influencing the design and management of production systems. However, applications related to assembly lines are scarcely explored in the literature. Hence, in this paper, a Digital Twin-based approach to real-time assembly line balancing problem (ALBP) in the i-FAB learning factory of Università Carlo Cattaneo – LIUC is presented. The results show that the implementation of a Digital Twin (DT) can enhance the overall productivity of a manual assembly line to smooth the effects of disruptions.

The development of innovative simulation and integration technologies, led by Industry 4.0, brought increasing attention to the theme of “Digital Twins” (DT) in manufacturing. As a matter of fact, since 2016 the number of papers related to DT has been strongly growing in the industrial engineering body of literature. Articles, conference papers, and book chapters can be found, presenting models and applications of DT in different manufacturing realities. Also, reviews published from 2018 have analysed the current state-of-the-art and opened interesting future research directions in terms of DT methods, tools, and technological issues. These are key contributions to provide support to the decision-makers in integrating the benefits of different technologies and developing the idea of Smart Factory. However, achieving a successful DT-driven Smart Factory within industrial realities is a demanding task, and nowadays companies still struggle in understanding how to face the challenges related to create and maintain DT. These challenges are not only related to technological barriers, but also to managerial, cultural, and organisational barriers. Nevertheless, a complete overview of the barriers and the consequent enabling factors to DT implementation are still missing in the literature. Hence, the aim of this paper is to present a general overview of the literature on barriers and enablers to DT implementation and understand the current gaps that need to be filled in this research area. In doing so, the study conducts a systematic review of the literature on DT for manufacturing applications, presenting a descriptive and thematic analysis of the existing contributions. By analysing the DT literature, this article develops a taxonomy of the main barriers and enablers for the implementation of DT and presents a research agenda to define future research directions and guide new contributions to the DT knowledge.