PURPOSE:Modern healthcare supply chains underscore the need for performance measurement systems that balance operational efficiency with resilience. This study aims to examine hospital-level pharmaceutical supply chains and propose a performance dashboard with key performance indicators (KPIs) from both areas. DESIGN/METHODOLOGY/APPROACH:A qualitative case study was conducted at the pharmaceutical warehouse of a large public hospital in Northern Italy, using process mapping, risk analysis (failure mode and effects analysis and fault tree analysis) and expert workshops to identify relevant metrics. FINDINGS:In total, 15 KPIs were initially selected from the literature and evaluated by a multidisciplinary panel of hospital professionals according to 5 criteria: relevance, measurability, data availability, interpretability and actionability. A final shortlist of nine operational and resilience indicators was incorporated into a prototype dashboard, supported by a roadmap for gradual implementation based on data maturity and staff readiness. The findings show that resilience indicators, such as time to recovery and failure rate, complement traditional metrics, such as stockout rate and order fulfilment, offering a more comprehensive view of performance. ORIGINALITY/VALUE:This study proposes a novel approach that addresses the tension between efficiency and resilience in the hospital supply chain. It provides managers with a concrete, actionable tool, moving beyond the fragmented and often theoretical nature of existing literature and demonstrating how the joint use of both operational and resilience indicators enhances the monitoring and management of drug supply chain vulnerabilities within a complex adaptive system.

In recent years, automated warehouses have become increasingly important to meet the rising demand of supply chain operations. Despite their growing relevance to industry, these systems remain largely underrepresented in academic settings, which contributes to a significant gap in student knowledge and preparedness. Traditional educational approaches often fail to equip future professionals with the practical skills required by modern logistics systems. While the learning factory paradigm partially addresses this gap, it typically places limited emphasis on logistics processes. To bridge this divide between theory and industrial practice, a hands-on learning experience was conducted in a logistics-focused learning factory involving Bachelor's and Master's engineering students. A structured questionnaire was administered to evaluate students’ perceptions of automated warehouses, and statistical methods were employed to analyze both the short- and medium-term impacts of the experience. Findings revealed a strong interest among students in industrial logistics, despite limited prior exposure to automation technologies. Consistent with previous research, the hands-on approach was particularly effective for Master's students, highlighting its potential as a valuable educational tool in logistics engineering.

Background: Personal Protective Equipment supply chains encountered severe shortages during the 2020 COVID-19 pandemic. Many manufacturers are located in China, the first country that issued lockdowns, and Personal Protective Equipment inventories, managed by the Just in Time policy, were unprepared for such a demand surge. The existing literature examines the impacts of COVID-19 on the global Personal Protective Equipment supply chain. However, five years after the onset of COVID-19, there is still a lack of studies focusing on Personal Protective Equipment supply chain behavior in Italy. Italy is a particularly significant case study, as it was the first Western country to be severely impacted by the pandemic. This work develops an empirical analysis to answer the following research questions. How did the main variables in the Italian Personal Protective Equipment supply chain change during the early stages of the pandemic? How can we explain such changes? Methods: A questionnaire survey was carried out among producers, importers, and distributors of Personal Protective Equipment operating in Italy. The responses to the questionnaire were analyzed by applying both descriptive statistics and the Kruskal–Wallis test. Results: The findings indicate that importers and distributors experienced more significant increases in orders than producers after the first lockdown, due to the new manufacturer’s setup period before full-scale operations. Conclusions: The study might encourage examinations of how material management strategies aimed at reducing inventory can impact situations involving unanticipated increases in demand. Moreover, it offers insights into the causes and consequences of the criticalities faced by the Italian Personal Protective Equipment supply chain during the first pandemic phases, contributing to creating knowledge that might be useful to define strategies to enhance supply chain resilience.

The enhancement of warehouse processes is pivotal in internal logistics activities and it is a lever of competitive advantage for companies that are operating in more turbulent business environment. Thus, this paper proposes a model for the accurate computation of travel time of automated warehouse systems under different configurations. The model is based on a simulation approach that is run with different levels of Input/Output (I/O) points, different shape factor values and rotational flows. The outcomes of the simulation are then analysed using regression analysis. The results obtained prove to be very accurate for a reliable calculation of travel times. Therefore, the proposed formula can be considered as an effective support for an accurate estimation of the travel time for a variety of automated warehouse configurations. The proposed work might be also supporting future studies focused on the warehouse design under different storage space shapes and operational conditions.

Artificial Intelligence (AI) has recently been established in healthcare management to support clinical activities and pharmaceutical research and development. Moreover, AI can potentially improve decision-making in healthcare supply chains (HSCs) by leveraging the information provided by various sources. However, research on the application of AI to HSCs is still in its infancy. This work presents a Systematic Literature Review to identify the main trends and future research directions. The analysis of the 23 pertinent papers suggests that more quantitative case studies on AI implementation in HSC are necessary. Additionally, the role of AI in facilitating logistics and supply chain management activities, promoting supply chain resilience, and ultimately creating integrated and agile HSCs should be investigated. Further literature reviews on AI-driven HSC management will help to keep the focus on this research field and its relevant developments.

The lockdowns caused by the COVID-19 pandemic between 2020 and 2021 resulted in a substantial increase in e-commerce purchases, with the consequent growth of logistics services. Thus, this paper is aimed at studying the effects of the pandemic on the operational processes of logistics service providers. To this end, a survey questionnaire was developed and administered to a sample of identified respondents. The collected data were quantitatively analyzed via the Kruskal–Wallis test. The outcomes point out that logistics operators faced an increase in the distances traveled to carry out pick-up and delivery activities, and larger companies added more light vehicles to their fleets, proving that the company size was a relevant aspect of ensuring a quick response to the pandemic. These results show an increased business-to-consumer market share that is leading to a redesign toward more sustainable operational strategies.

The recent Covid-19 pandemic has caused major disruptions in healthcare systems, in particular in the Personal Protective Equipment (PPE) supply chain. The present paper aims at studying the effects of Covid-19 on its main supply chain variables and at investigating how viability and resilience concepts were applied during this period. A Systematic Literature Review helps identify the variables and strategies most commonly considered, forming the basis of a survey then carried out among French and Italian companies operating in the PPE supply chain. The results allow to derive both academic and practical implications. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Purpose This paper aims to propose a simulation model integrated with an empirical regression analysis to provide a new mathematical formulation for automated storage and retrieval system (AS/RS) travel time estimation under class-based storage and different input/output (I/O) point vertical levels. Design/methodology/approach A simulation approach is adopted to compute the travel time under different warehouse scenarios. Simulation runs with several I/O point levels and multiple shape factor values. Findings The proposed model is extremely precise for both single command (SC) and dual command (DC) cycles and very well fitted for a reliable computation of travel times. Research limitations/implications The proposed mathematical formulation for estimating the AS/RS travel time advances widely applied methodologies existing in literature. As well as, it provides a practical implication by supporting faster and more accurate travel time computations for both SC and DC cycles. However, the regression analysis is conducted based on simulated data and can be refined by numerical values coming from real warehouses. Originality/value This work provides a new simulation model and a refined mathematical equation to estimate AS/RS travel time.

Warehouses usually include a number of non-value-added activities that increase costs, so they are a suitable field for applying Lean Thinking. In fact, Lean Warehousing has recently established as a promising research topic. However, most of the available contributions lack a practical though generalizable and theoretically relevant approach. Additionally, Lean is currently not enough explored as a preliminary effort to pave the way for a smooth application of Industry 4.0, neither in manufacturing nor in logistics. Inspired by Desing Science Research, the present work develops a structured process for implementing Lean Warehousing aimed at fostering continuous improvement to facilitate the introduction of Industry 4.0 technologies. To this end, multiple Lean tools, such as Value Stream Mapping, Spaghetti Chart, 5W+1H, and 5S analysis, are integrated. The application to a warehouse in the food industry is illustrated. Future research will focus on carrying out a complete validation. Moreover, performance measurement through a set of warehouse Key Performance Indicators will be introduced to assess both wastes and the associated improvements after implementing the appropriate corrective actions.

Manufacturing companies face severe challenges from rapid technological developments. Industry 5.0 indicates the need for a sustainable, human-centered, and resilient industry. In striving for transformation, innovation becomes critical. However, a careful allocation of resources implies the evaluation of innovation projects. Moreover, diverse types of innovation and limited amounts of information represent a significant challenge. Therefore, this contribution presents an approach for holistically assessing innovation in manufacturing. First, a systematic literature review (SLR) was conducted to frame the current research state and identify assessment criteria. Second, a multiple-attribute decision-making method (MADM) was developed using the findings of the SLR and expert interviews. Finally, the criteria and the assessment approach were verified and validated by expert interviews, a workshop, and an industrial use case application. As the main findings, three criteria groups were derived and detailed: potentials, efforts, and risks. These criteria groups were used in a MADM approach incorporating Fuzzy set theory within a hybrid technique, combining the Analytical Hierarchical Process with the Technique for Order Preference by Similarity to Ideal Solutions. In conclusion, an enhancement of innovation assessment in manufacturing was achieved through the integration of different criteria and the balance between complexity and industrial applicability.

Healthcare facilities require flexible layouts that can adapt quickly in the face of various disruptions. COVID-19 confirmed this need for both healthcare and manufacturing systems. Starting with the transfer of decision support systems from manufacturing, this paper generalizes layout re-design activities for complex systems by presenting a simulation framework. Through a real case study concerning the proliferation of nosocomial cross-infection in an intensive care unit (ICU), the model developed in systems dynamics, based on a zero order immediate logic, allows reproducing the evolution of the different agencies (e.g., physicians, nurses, ancillary workers, patients), as well as of the cyber-technical side of the ICU, in its general but also local aspects. The entire global workflow is theoretically founded on lean principles, with the goal of balancing the need for minimal patient throughput time and maximum efficiency by optimizing the resources used during the process. The proposed framework might be transferred to other wards with minimal adjustments; hence, it has the potential to represent the initial step for a modular depiction of an entire healthcare facility.

Environmental sustainability in transportation operations is acquiring an increasing importance in recent years and a lot of Logistics Service Providers (LSPs) are including green practices in their business. However, the interests of logistics operators and the related level of awareness about the adoption of environmental friendly practices are still not deeply analyzed in literature. Therefore, the proposed paper is intended to investigate the perception of LSPs about the environmental issues and their willingness of pursuing future green strategies. To this end, based on a literature analysis aimed at identifying a comprehensive list of green practices, a questionnaire survey is administered to LSPs operating in the Italian market. The data gathered are then analyzed via the Kruskal-Wallis test and the questionnaire outcomes discussed with the LSPs participating to the survey through face to face interviews. Results show that the environment is highly considered by the freight carriers of the sample, both small and large ones. In addition, personnel involved in different company roles appear to pay diverse levels of attention to the sustainability issue. In particular, the reduction of pollutant is considered more crucial for employees in charge of dealing with operations (median equal to 5) and reverse logistics is perceived less important by managers (median equal to 3). The outcomes of the study might support companies to achieve sustainability and promote the green awareness issue. At the same time, policy makers might be facilitated by this study in designing environmental friendly programs in the logistics field.

The development of digital technologies in all aspects of human life leads to increasing the necessity for investigating them in the Supply Chain (SC) as the main channel to provide products. Moreover, Lean principles, with the aim of reducing wastes, could be one of the main research streams in SC in recent years. Therefore, it is valuable to figure out the mutual effects of Lean principles and digital technologies as two growing areas in SC. Previous works did not pay attention to investigating this relationship at the SC level and were more focused on the production level. However, the present work addresses this issue by conducting a multi-perspective Systematic Literature Review (SLR). Additionally, in the present SLR, the impact of individual Industry 4.0 technologies in relation to Lean principles was investigated from various SC perspectives. The results reveal the necessity of studying single SC processes in Lean Digital SC. Moreover, the applicability of each technology should be illustrated to alleviate SC operational and organizational issues. The results provide useful insights about applying single digital technologies as well as a combination of them to each SC process to solve specific issues.

A blood supply chain (BSC) is a very long and complex sequence of processes heavily sequential. If one of them is executed in an incorrect way and this error is not detected, it leads to an incorrect transfusion outcome, that could seriously affect patients. For this reason, there is a strong need to identify and prevent adverse events along the entire BSC, in order to reduce their probability of occurrence. This also helps improving BSC sustainability from both the environmental and the social perspectives. The paper extends an existing healthcare supply chain risk management framework already applied to the blood transfusion process to address multiple BSC echelons and identify the cause and effect relationships among the adverse events that might occur. To this end, Fault Tree Analysis is added to the risk management tools part of the original framework as well as Key Performance Indicators are applied to detect risky event manifestation. The first application of the proposed approach to a blood bank and a hospital ward revealed its effectiveness in identifying the BSC activities most subjected to risk. Also, connections between adverse events and causal relationships among their sources were found, leading to understanding whether an adverse event is caused by a risk source in the same echelon where it occurs or by the concurrent manifestation of several adverse events upstream in the BSC. Future research will be devoted to numerically evaluate probability of occurrence and impact of risky events as well as integrating the framework with a classification of criticalities based on their severity.

Purpose The objective of this paper is to propose an approach to comparatively analyze the performance of drugs and consumable products warehouses belonging to different healthcare institutions. Design/methodology/approach A Cluster Analysis is completed in order to classify warehouses and identify common patterns based on similar organizational characteristics. The variables taken into account are associated with inventory levels, the number of SKUs, and incoming and outgoing flows. Findings The outcomes of the empirical analysis are confirmed by additional indicators reflecting the demand level and the associated logistics flows faced by the warehouses at issue. Also, the warehouses belonging to the same cluster show similar behaviors for all the indicators considered, meaning that the performed Cluster Analysis can be considered as coherent. Research limitations/implications The study proposes an approach aimed at grouping healthcare warehouses based on relevant logistics aspects. Thus, it can foster the application of statistical analysis in the healthcare Supply Chain Management. The present work is associated with only one regional healthcare system. Practical implications The approach might support healthcare agencies in comparing the performance of their warehouses more accurately. Consequently, it could facilitate comprehensive investigations of the managerial similarities and differences that could be a first step toward warehouse aggregation in homogeneous logistics units. Originality/value This analysis puts forward an approach based on a consolidated statistical tool, to assess the logistics performances in a set of warehouses and, in turn to deepen the related understanding as well as the factors determining them.

Industry 4.0 technologies, originally developed in the manufacturing sector, can be purposefully implemented to improve City Logistics (CL) processes by automatizing some of their operational tasks and enabling real-time exchange of information, with the ultimate goal of providing better interconnection among the actors involved. This work aims to identify the main social and economic contextual drivers for investing in the application of Industry 4.0 technologies to urban logistics. To this end, a dataset based on the primary collection of 105 CL projects exploiting the main 4.0 technologies has been built. After that, a regression model has been completed including potential economic, strategic, and demographic determinants of investments in CL 4.0. According to the obtained outcomes, Gross Domestic Product, Foreign Direct Investments, Research and Development Expenditure, Employment Rate, and Number of Inhabitants are significant contextual factors for the adoption of Industry 4.0 technologies in last mile logistics. The study might support academicians to investigate novel application fields of Industry 4.0 technologies. Also, it can serve as a roadmap for orienting the investments of private organizations and public entities to promote CL innovation and digitalization. Moreover, Industry 4.0 technology providers might find this study interesting to uncover prospective business sectors and markets. Future research efforts will analyse the impacts of internal business factors on CL 4.0 and the satisfaction levels of urban logistics stakeholders.

Blood transfusion is a critical health care process due to the nature of the products handled and the complexity driven by the strong interdependence among the sub-processes involved. Most of the errors causing adverse events originate during the blood logistics activities. Several literature contributions apply risk management to the transfusion process but often in a fragmented and reactive way. Moreover, few of them focus on logistics risks and assess the effectiveness of risk responses through operational key performance indicators (KPIs). The present paper applies a comprehensive and structured approach to proactively identify and analyse logistics risks as well as define responses to improve blood bag traceability, focusing on hospital wards. The implementation of such actions is monitored by specific KPIs whose measurement enables an improved communication flow among actors allowing to uncover residual risks. Future research will extend the application to further blood transfusion settings and supply chain echelons. The outcomes of this work might assist practitioners in improving policy making about blood supply chains. As a matter of fact, they allow a better understanding of the associated material and informational flows and the related risks, which supports setting effective strategies to either prevent adverse events or mitigate their effects.

This paper is aimed at identifying the main patterns related to the application of new Digital Supply Chain Technologies that through the Industry 4.0 paradigm, are redefining supply chain organisations. To this end, a set of DSC initiatives has been analysed via an Analysis of Variance (ANOVA) to understand the influence of several social and economical factors on the implementation of DSC solutions. Results show that the time factor, the Gross Domestic Product per capita, the amount of foreign investment, and the expenditure in Research and Development are significant drivers of DSC technologies. In particular, Big Data are associated with a higher economic effort than Blockchain, although these two technologies, together with Augmented Reality and Artificial Intelligence, usually characterise most recent implementations. The present work might foster research about the contextual factors affecting the DSC diffusion it could assist both practitioners and policymakers in defining appropriate DSC strategies.