Rossella Pozzi

This paper explores the educational demands on engineering graduates as the industry shifts from Industry 4.0 (I4.0) to Industry 5.0 (I5.0). It examines the integration of Learning Factories (LFs) in higher education, which provide hands-on learning in simulated industrial environments. Through a systematic literature review and empirical research, the study develops a framework highlighting key factors for effective learning: pedagogy, learning spaces, and technology. These elements enhance student readiness for I4.0 and I5.0 by fostering crucial skills like problem-solving and collaboration. Findings indicate that LFs help bridge the gap between theory and practice. The study offers recommendations for Higher Education Institutions (HEIs) to create immersive learning environments that address skill gaps and support sustainable, inclusive futures aligned with I5.0 ideals.

This paper presents a set of guidelines to facilitate in the design of engineering education in the context of I4.0 and I5.0. Based on current trends, the paper examines the evolving expectations for engineering competencies, considering technological changes and a shift towards more human-centric, sustainable, and resilient industrial systems. Through the integration of a Learning Factory and experiential learning, the authors propose a pedagogical framework tailored to the expectations of higher education and modern industry. Through a multifaceted learning domain, interdisciplinary skills, digital fluency, and soft skills, such as creativity and collaboration, are fostered to prepare engineers for the complexities of future industrial work. The guidelines are informed by literature analysis and practical insights drawn from academic-industry collaborations, to bridge the gap between theoretical education and real-world application.

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 so-called Industry 4.0 and Industry 5.0 have reshaped manufacturing, making it necessary for workers to develop a new set of practical and non-practical skills, which can allow them to remain competitive in the evolving context. Traditional educational models often fail to bridge the gap between knowledge and skills, highlighting the need for innovative teaching methods. Among the tools and technologies supporting teaching, Learning Factories offer a promising solution by providing students with life-like hands-on production experiences in a controlled environment. By combining these aspects, this paper proposes an approach, based on the theory of Constructive Alignment, for the design of a Learning Activity with the support of a Learning Factory. The approach is tested on a practical application involving Bachelor’s and Master’s students enrolled in engineering courses at the University of Bergamo. To gather feedback on the design of the Learning Activity, a questionnaire, that evaluates the learning experience according to a set of moderating variables, is submitted to the participants. Results show good acceptance by the students and give indications on how to improve the design of the Learning Activity to make it more effective.

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.

Lean Production (LP) has been widely and successfully employed in mass production situations, showing high capabilities in reducing non-added-value activities, providing process stability, high qualitative production outputs and competitive production lead times. In engineer-to-order (ETO) situations, instead, the high customisation and variability of the context bring complexity and make it very difficult to fully employ the potential of LP. Over the years several studies focused on this issue, studying the application of LP practices to ETO situations. However, recent literature reviews underlined that there is a lack of research addressing the issue of whether to adopt, adapt or reject LP practices in ETO situations, and there is still an ongoing debate on this field. To fill the gaps identified in the existing literature, this study aims to study what LP practices are implemented in ETO situations, as well as how, using multiple case study research. An original data set was constructed using a purposely defined research protocol using structured interviews. The findings of this study show what LP practices are implemented successfully in ETO situations and what, on the other hand, are not easily implemented. Also, the study analyses how LP practices are implemented and what adaptations they must undergo to be effective within the ETO situations.

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.

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.

Predictive maintenance (PM) involves the use of internet of things and machine learning techniques applied to machinery for remote monitoring of different variables to timely detect problems, before they require costly maintenance or generate customers’ complaints. Thus, it minimises the probability that the machine will break, extends its lifecycle and reduces the number of corrective actions. Despite the importance of PM and the growing implementation of Industry 4.0 technologies, a limited number of Italian machinery companies today includes PM systems in their products. Additionally, the topic has received little attention by literature. Consequently, there is a need to identify the barriers to the implementation of PM in the Italian machinery industry. Therefore, the aim of this research is to categorise the challenges to be considered when implementing a PM and propose a set of possible countermeasures. In doing so, the study reviews the existing literature on this topic and empirically explores three cases in the machinery industry. The results of the literature review show a list of barriers to PM implementation that can be related to the machinery industry. Then, the barriers are empirically validated, and inductively extended, and final set of countermeasures is proposed to overcome these challenges, in help of managers that are interested in adopting PM.