Giovanni Luca Pirovano

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 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.