Nicolò Saporiti

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.

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.

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.

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.

Purpose: This works provides a thorough understanding of the challenges and opportunities associated with Additive Manufacturing (AM) adoption in the medical sector. Through this analysis, we aim to better understand when to adopt AM, how to do so, and how such adoption might change in the future. Design/methodology/approach: This research first conducted a systematic literature review (SLR) to identify AM challenges and opportunities in the medical sector, which were then validated through a Delphi study. The 18 Delphi study participants were also asked to suggest countermeasures for the challenges and help identify future AM adoption scenarios. Finally, these findings were analyzed according to the ecosystem pie model to design an ecosystem model for AM in the medical sector. Findings: Among the 13 challenges and 13 opportunities identified, the lack of a skilled workforce and the responsiveness achievable via AM were by far the most relevant challenge and opportunity. Moreover, the participants identified countermeasures for 10 challenges, as well as three future AM adoption scenarios. Finally, leveraging these findings, an ecosystem model was developed. Originality/value: This work contributes to the limited understanding of the AM challenges and opportunities in the medical sector. It helps medical practitioners to better understand the challenges and opportunities associated with AM and AM manufacturers to better identify where to focus their R&D efforts and how this would impact future AM adoption levels. Furthermore, this work extends current theory supporting the design of an ecosystem model for AM in the medical sector following the ecosystem pie model.

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.

This study examines the impact of Additive Manufacturing (AM) on supply chain resilience (SCR) using Pettit et al.'s framework, which links SCR to supply chain (SC) vulnerabilities and capabilities. Given the lack of quantitative and empirical research on this topic, a Delphi study was conducted to determine whether AM has a positive or negative effect on these factors. The findings indicate that AM generally enhances SCR by improving key SC capabilities and vulnerabilities such as “Adaptability” and “Efficiency”. However, it also presents challenges as it negatively affects SC capabilities and vulnerabilities like “Resource limitations” and “Flexibility”. As such, AM adoption is not universally beneficial. Managers should hence assess their companies' SC capabilities and vulnerabilities, and invest in AM only if it strengthens their weakest areas. Furthermore, this study explores potential future scenarios for AM adoption in SCR. The findings identify two potential scenarios, with expected increases in AM use for SCR of up to 30 %. The study also outlines the required interventions and investments for the realization of these scenarios, offering practical insights for companies and policymakers. These insights guide stakeholders in identifying necessary interventions and understanding how these investments will impact the use of AM for SCR. The focus is on the impact of AM on supply chain resilience (SCR).•AM is found to positively affect most of the SC capabilities and vulnerabilities.•However, AM also negatively affects relevant SC capabilities and vulnerabilities.•Potential future scenarios for the use of AM for SCR have been derived.•The results are obtained empirically, for the first time in the literature.

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 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.

The years 2021 and 2022 showed that maritime logistics are prone to interruptions. Ports especially turned out to be bottlenecks with long queues of waiting vessels. This leads to the question of whether this can be (at least partly) mitigated by means of better and more flexible terminal operations. Digital Twins have been in use in production and logistics to increase flexibility in operations and to support operational decision-making based on real-time information. However, the true potential of Digital Twins to enhance terminal operations still needs to be further investigated. A Delphi study is conducted to explore the operational pain points, the best practices to counter them, and how these best practices can be supported by Digital Twins. A questionnaire with 16 propositions is developed, and a panel of 17 experts is asked for their degrees of confirmation for each. The results indicate that today’s terminal operations are far from ideal, and leave space for optimisation. The experts see great potential in analysing the past working shift data to identify the reasons for poor terminal performance. Moreover, they agree on the proposed best practices and support the use of emulation for detailed ad hoc simulation studies to improve operational decision-making.