Rossella Pozzi

Learning Factories (LFs) have become increasingly important in engineering education, offering environments where students can engage in experiential learning and connect theoretical knowledge with industrial practice. In 2016, LIUC University established the i-FAB LF, a foosball assembly line where students experience real factory scenarios, as part of educational formats designed to develop skills in lean manufacturing and Industry 4.0. In this context, a variety of activities devoted to industrial engineering students has been developed. Among these, a learning activity centered on the integration of Artificial Intelligence (AI) into quality management stands out as a representative example, fostering the development of the skills required by the new workforce in the context of Industry 5.0 (I5.0). This activity offers a hands-on, team-based learning experience, with its pedagogical foundation rooted in the A3 problem-solving framework, which guides students in systemat ically identifying quality issues, collecting relevant data, and analyzing root causes in the production process. Following this structured analysis, students apply their insights to develop an AI tool leveraging convolutional neural networks to automati cally detect and classify assembly defects. In this way, the A3 process provides both the diagnostic foundation and the data driven roadmap for AI implementation, connecting structured problem solving with innovative technological countermeasures. Taken together, these elements foster skills in creative and innovative thinking, collaboration, data analytics and problem solving – considered as key competencies in the I5.0 paradigm. The contribution of this study is to present the LF-based educational activity and to report students’ perceptions collected through a structured questionnaire on three dimensions: learn ing experience and pedagogical design, collaboration and motivation, and quality of the learning environment.

Purpose: This study aims to investigate the impact of Lean Production (LP) and Industry 4.0 (I4.0) on the adoption of Circular Economy (CE) within manufacturing firms, also considering the potential moderating effect of I4.0 on the link between LP and CE. By doing so, it addresses the limited and contrasting empirical evidence on how operational practices facilitate the adoption of CE. Design/methodology/approach. This study adopted the Practice-Based View (PBV) as a theoretical lens and employed a quantitative research design. Data were collected via a survey of 151 manufacturing companies operating in Italy and analyzed using hierarchical multiple regression analysis to test the conceptual model. Findings: The empirical results confirm that both LP and I4.0 are significantly and positively associated with CE adoption. I4.0 technologies have a stronger individual impact on CE adoption than LP. However, the hypothesized moderating effect of I4.0 on the LP–CE relationship is not supported. In other words, the implementation of technologies may not contribute to a positive variation in the level of CE adoption that is already achieved due to the implementation of LP practices. Hence, managers should pursue distinct CE strategies aligned with the unique capabilities of LP and I4.0, respectively. Originality/value: This research contributes to the operations management and CE literature by providing empirical evidence on the role of LP and I4.0 in enabling CE adoption. It offers valuable insights for practitioners and policymakers aiming to foster CE adoption in manufacturing, dealt with the links adopting the PBV, challenging the presumed moderating effect of I4.0, and providing actionable recommendations.

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.

Recent years have seen a growing interest among academics and practitioners in the approaches of Industry 4.0 (I40), Lean Production (LP), and Circular Economy (CE). Scientific studies have largely examined these approaches separately, or in a dual, pairwise combination. More recent research has also shown how I40 technologies and LP practices affect the implementation of CE strategies. In particular, it has been noted that I40 technologies and LP practices mutually not only enhance each other's efficacy but also have a positive impact on CE strategies. Despite this evidence, many of the existing works leave a critical gap in our knowledge about an integrated perspective among these three approaches. In other words, a more synergistic interaction among the I40 technologies, LP practices, and CE strategies is not yet well explored in the existing academic literature and needs to be developed. To address this research gap, this study leverages a multiple case study analysis of six companies operating in the manufacturing sector that operate with I40 technologies, LP practices, and CE strategies. Our results confirm that I40 technologies and LP practices foster each other and enable CE strategies. In addition, our empirical analysis adds to the existing studies that the synergistic interaction among the three approaches lies in the fact that the implementation of one approach triggers another one sequentially. In other words, the implementation of I40 technologies contributes to the activation of LP practices, which in turn enable the adoption of CE strategies. The evidence of our results has been visualized in empirically based framework that highlights for scholars and managers how manufacturing companies can optimize their transition pathway towards CE through I40 technologies and LP practices and paving thus the way for a more sustainable and effective industrial environment.

I n the current healthcare setting, challenges related to increasing demand for care, scarcity of resources, and the need to ensure high quality standards require a rethinking of organizational and management models. The introduction of advanced methodologies such as Lean Management and Operational Excellence offers concrete tools to optimize processes, reduce waste and make the most of available resources, with the support of organizational change and training initiatives to ensure their effective adoption. The article presents a practical overview of these methodologies, highlighting how they can facilitate the transformation of healthcare processes.

The pharmaceutical sector is experiencing rapid growth in global medicine usage and spending. As the sector expands, so do risks. Blockchain technology promises significant benefits for addressing key issues in the pharmaceutical supply chains, such as enhancing traceability, preventing counterfeit drugs, and securing sensitive data. However, its adoption is not without criticism, facing challenges such as high implementation costs, regulatory uncertainties, and even skepticism about its purported benefits. This study provides the first empirical analysis of blockchain adoption challenges and countermeasures in the pharmaceutical industry. The research aims to identify key challenges and propose effective solutions to support broader implementation. A three-step methodology was employed: a systematic literature review to identify challenges, followed by a Delphi study to assess their relevance, and finally, a questionnaire collecting practical countermeasures. Seventeen major challenges and eight sets of countermeasures were identified and prioritized, with "IT security" being ranked as the most critical challenge and "Education and training" as the most effective countermeasure as it tackles more pressing challenges. The results were then analyzed using the Technology-Organization-Environment (TOE) framework and the Stakeholder Theory. The use of these complementary frameworks allowed to shed light on how different stakeholders can address the different challenges based on their roles, emphasizing the importance of their collective and collaborative efforts. This integration of theoretical frameworks provides valuable practical insights for addressing blockchain-related challenges and accelerating its adoption. Moreover, this research compares the pharmaceutical sector with other application areas, extending insights into blockchain adoption across industries.

The urgency of addressing global challenges such as climate change, resource scarcity, and environmental degradation has positioned the Circular Economy (CE) as a crucial strategy for sustainable development. Industry 4.0 (I4.0) technologies have been recognized as key enablers of CE. However, a significant knowledge gap exists regarding how organizations can effectively integrate these technologies with CE strategies. Existing integration frameworks often focus narrowly on specific industries, technologies, or the traditional 3R model, neglecting the broader 10R framework and, hence, offering limited guidance. This paper addresses these gaps by developing a comprehensive 10R-based integration framework, providing practical guidance on how I4.0 technologies can support the full range of CE strategies. Using a literature review based on keywords’ clusters analysis, this study explores how I4.0 can support both implementation and decision-making in the CE 10Rs, providing a practical guide for businesses and supporting a broader shift towards sustainable business models. The results show that IoT, Big Data, and Digital Twins effectively support Rs related to smarter product use and manufacturing processes. Additive Manufacturing, Augmented/Virtual Reality, and Cognitive Twins are crucial in extending the lifespan of products or components. IoT, Artificial Intelligence, Blockchain, and human-robot collaboration can improve recycling practices and material recovery. The study reveals that while ’Reduce’ and ’Recycle’ dominate the literature, integrating I4.0 technologies with lesser-explored strategies like ’Reuse,’ ’Repurpose,’ ’Refurbish,’ and ’Remanufacture’ offers significant potential for future research. It also stresses the need to assess the energy and environmental impacts of I4.0 technologies themselves in the CE context.