Tommaso Rossi

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 fourth and fifth industrial revolutions have significantly transformed manufacturing, introducing new perspectives and foundational beliefs, particularly in promoting sustainable manufacturing practices. This shift requires rethinking the competences and decision-making processes that workers must have to work in the new environment. To help students in being competitive on the job market, educational institutions must align their educational programs to address the new requirements. Thus, this research aims to identify the skills that future workers must have to be appealing for companies that want to operate in an environmentally conscious and socially responsible industrial landscape. To do so, a systematic review of existing literature was carried out. To offer a complete overview of the evolution of the competencies and identify important trends, the study tracked the frequency of citations for skills over time. These skills can be taught through learning factories, which can be used to provide students with practical applications of theoretical knowledge, ensuring that learned competencies are not only theoretical but also practical and applicable in real-world scenarios. This research offers valuable insights into the evolving debate on skills, which can be useful for the development of educational programs, learning activities and targeted training initiatives for workers.

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.

Of the drug distribution models implemented in the Italian National Healthcare Service provided to guarantee the administration of medication, the drug distribution performed through the hospital channel is an operative strategy that allows for savings in the public expenditure, but often creates higher social costs for patients and caregivers. This distribution model leads to high access to hospitals which, during pandemics, amplifies the risk of contagion, making these healthcare facilities a place where epidemics could spread and negatively affect high-risk patients. Considering their extensive local presence, primary care services and community pharmacies could play an active role to reach patients and ensure the proper distribution of drugs. Based on the differences in these two distribution models, a prescriptive tool could provide suggestions for the institutional decision-making process. When performed by different stakeholders (i.e., policy makers, health authorities or agencies), it could define which drugs should be distributed by primary care pharmacies for the treatment of chronic diseases and provide an answer to critical issues in case of future pandemic situations and healthcare emergencies. Prescriptive data analyses are known as the best methods for formulating prescriptions in the distribution field and constrained optimization sets the values of decision variables to achieve specific objectives, such as a reduction in the number of visitors to the hospital setting. Grounded on previous research in this field, the present study proposes a decision support tool based on a constrained optimization model, establishing which drugs currently dispensed by hospital pharmacies should be distributed by primary care pharmacies. This approach allows for limiting crowding and balances the distribution costs to guarantee equal access to care for patients. The model structure and the possible decision-making outputs reached by applying the prescriptive tool are discussed and the “what-if” analysis is used to ensure the robustness of the simulation approach.

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 link between Industry 4.0 (I40) and lean manufacturing has recently gained significant popularity in both academia and industry. The implementation of I40 has been proved to be beneficial for lean programs, supporting lean practices and increasing the flexibility of lean. In this context, the present paper introduces i-FAB, a learning factory developed by Università Carlo Cattaneo (LIUC) to demonstrate the benefit of the adoption of I40 technologies in a lean managed assembly system. The paper provides details on the i-FAB lean tools, I40 technologies and the training modules developed for Industrial Engineering and Management students and executive learning programs, showing empirical evidence of the benefits linked to the implementation of I40 technologies in a lean managed assembly system.

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.