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.

The integration of emotional monitoring technologies in logistics represents a significant leap in enhancing operational efficiency and worker well-being. As the logistics evolves, understanding the acceptance and adoption of such technologies becomes crucial. This study employs the extended Technology Acceptance Model (TAM) as a framework to investigate the determinants influencing logistics workers' acceptance of emotional monitoring technologies, with a focus on facial expression recognition (FER), electroencephalography (EEG) and a bundle of physiological measures including Electromyography (EMG), Electrocardiography (ECG), and Galvanic Skin Conductance (GSC). We aim to explore how perceived usefulness (PU) and perceived ease of use (PEOU), core constructs of TAM, along with external variables belonging to social, individual and system levels, contribute to this acceptance. The questionnaire was administered to 45 warehouse operators from a logistics company in Italy. Results show that TAM models are able to explain the acceptance of these technologies in the examined working environment.

Integrating Digital Twins (DTs) within supply chain management offers transformative potential for optimizing operations, enhancing decision-making, and fostering resilience. However, existing literature often lacks practical insights into assessing their maturity. This paper addresses these gaps by proposing a comprehensive Maturity Model (MM) tailored for supply chain DT development. The proposed MM is applied to real-world case studies, highlighting its utility in evaluating DT readiness and guiding implementation. Key challenges, including modeling capabilities, data and system integration, and stakeholders' collaboration, are discussed alongside strategies for overcoming them. This research provides practitioners with actionable insights for building robust DT architectures, enabling organizations to leverage the full potential of digital transformation while ensuring scalability and sustainability. Copyright (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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.

Background: Warehouses play a vital role in logistics systems, not only for storing goods but also for providing value-added services. To improve warehouse productivity and reduce costs, it is essential to measure their performance and identify inefficiencies. Method: This paper introduces a new aggregated key performance indicator (KPI), called Overall Warehouse Effectiveness (OWE), to evaluate the efficiency effectiveness of the physical structure of a warehouse. OWE utilizes the concepts of Availability, Performance and Quality, similar to the Overall Equipment Effectiveness (OEE) metric used in manufacturing. Results: The proposed indicator is then applied to a case study to demonstrate its use and provide theoretical and practical implications. Conclusions: In terms of theoretical implications, the proposed metric fills a gap in the literature by providing an aggregated indicator specifically designed for storage systems. For practitioners, OWE enables the identification of efficiency waste, customer service faults and adequacy of inventory management policies.

Healthcare logistics involves significant complexity due to the high cost of goods purchased and stored, as well as the uncertainty of demand linked to the variability of patients' needs. Thus, effective inventory management could bring relevant benefits in terms of cost and quality of the service delivered. In such a context, automation is one of the most promising ways to improve warehouse processes. In this paper, the quantitative effects of implementing an automated storage system in a hospital warehouse are assessed. To this end, a dashboard of key performance indicators related to several dimensions is identified and measured. The results demonstrate that significant benefits have been obtained in terms of both quality and operational efficiency. In addition, the automated warehouse system has allowed relevant time savings for pharmacists who can be assigned to higher-value activities. Future research will assess the cost savings from efficiency gains achieved through warehouse automation. Copyright (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

The Make-to-Order (MTO) supply chain seeks to balance cost reduction with satisfactory customer service, especially concerning order lead times. Simulation plays a vital role in this balance, identifying risks, analyzing scenarios, and evaluating key performance indicators. However, existing simulation models often overlook suppliers and inventory management, focusing more on production, sales, and distribution. To address this, a simulation model tailored for the furniture industry integrates supplier selection with inventory management strategies, considering geographical complexities. Through a case study, various scenarios are assessed, revealing a trade-off between lead times and costs. Close MTO suppliers decrease lead times but increase costs due to transportation expenses, while distant sourcing minimizes costs but extends lead times, challenging customer expectations. This simulation model offers insights for MTO companies navigating supplier selection and inventory management, enhancing decision-making and customer satisfaction. Future research aims to expand the model into a supply chain Digital Twin, incorporating resilience and risk management to tackle broader MTO supply chain challenges.

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.

Although scholars have developed simulation models and analyzed bankability criteria in numerous papers to enhance the financial outcomes of Public-Private Partnerships (PPPs), the intersection between both fields considering alternatives to assess the financial impact of flexibility in both scope and financing, remains underdeveloped. To address this gap, this paper introduces a financial model that assesses the financial impact of suitable life-cycle flexibility alternatives while coexisting with revenue performance alterations in each project phase within toll road PPPs. This model serves as a tool to gain insight into the planning of strategic actions to implement flexibility in both scope and financing. Based on system dynamics and validated using financial data from two toll road PPPs in India and East Europe, simulation results suggest that two key drivers (capital expenditures and debt repayment period) have the greatest influence on financial performance. Findings support the implementation of flexible scopes to effectively address the most influential exogenous factor impacting the financial performance of PPPs, namely traffic shortfalls. This model provides a suitable tool for assessing the life-cycle economic sustainability of PPPs in uncertain and complex environments.

This paper addresses the evolving warehouse automation scenario in supply chain management, focusing on the design and simulation of a multi-level shuttle system. Unlike existing studies, the proposed system integrates picking stations within the storage rack, optimising space and improving picking efficiency. A discrete event simulation is proposed to model the system design and operation. The model also integrates different strategies for selecting items and serving picking stations. The paper provides insights into efficient warehouse processes from both a theoretical and practical point of view by presenting a structural model, simulation results and 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/)

This paper introduces a conceptual framework for integrating Conversational AI (CAI), specifically conversational agents (CAs), with Decision Support Systems (DSS) to enhance Supply Chain Management (SCM) decision-making processes. In today's complex supply chain environment, characterized by diverse processes and entities operating across different geographic locations, the effective use of AI in DSS is crucial. The proposed framework envisions a Conversationally Enabled Supply Chain (CESC) where decision-makers interact with the DSS using natural language through a CA, facilitating tasks such as data analysis, scenario analysis, and simulation. The choice of a conceptual framework as a research tool provides a systematic approach to collect and organize elements, offering a clear reference structure and a common language. This framework aims to enhance understanding, guide research and analysis, and integrate knowledge from diverse sources, contributing to a holistic understanding of the proposed CA-empowered DSS for SCM. The paper emphasizes the significance of CESC and sets the stage for future research and development in the domain, providing a foundation for ongoing work. 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/)

Project risk management (PRM) involves identifying risks, assessing their impact, and developing a contingency plan. A structured contingency management (CM) approach prevents subjective biases in analyzing risks and developing responses. Previous studies have either focused on improving the accuracy of risk estimates or analyzed, from a qualitative perspective, the relationships between perceived risk and project performance. This study aimed to improve PRM by providing a risk-perception-based contingency management framework (CMF). The CMF guides contingency depletion based on two short- and long-term cost overrun indicators and their respective thresholds. Thresholds and the initial contingency reserve amount are determined by applying the Monte Carlo method to a stochastic, discrete-event, finite-horizon, dynamic project simulation model. The study developed the CMF through a structured approach, validating the simulation model on eight specific project configurations. The results prove that the framework can be applied to any project, shaping the risk response strategy. This study contributes to PRM by explaining the relationships between risk perception and risk responses and providing a prescriptive CM tool.

Automated storage and retrieval systems have become increasingly popular in modern supply chains due to their significant advantages over traditional warehousing systems. Due to the high complexity of these systems, simulation approaches can be used to generate accurate performance measures for a specific system configuration. Simulation models are also the cornerstone of digital twins, one of the latest technological innovations that can further improve warehouse operations. Therefore, the aim of this research is to describe an approach for the development of a discrete event simulation model of an automated storage and retrieval system with a perspective towards the implementation of a digital twin. To be consistent with the objectives of the digital twin, the proposed model represents both the physical system and the overarching information technology architecture, such as the warehouse management system and the warehouse control system. In addition, this paper describes a methodology to validate such a simulation model by setting up an experimental campaign based on the principles of design of experiment. The experiments conducted in a logistics laboratory were used to iteratively calibrate the model until its performance accurately reflected the functioning of the real system. The results obtained demonstrate the effectiveness of the proposed method. Finally, this work contributes to the literature on warehouse digital twins by highlighting new variables to be considered when defining travel time models and their stochastic nature.

Effective project risk management involves identifying potential risks and developing contingency plans to mitigate their impact on the budget. While previous research has primarily focused on improving the accuracy of risk assessments, less attention has been given to understanding how Project Managers' risk perception affects their response actions. To address this gap, this study proposes a novel framework for optimizing project risk management by incorporating Project Managers' risk perception into contingency planning. The framework utilizes an optimization algorithm that employs discrete-event stochastic dynamic simulation to determine the appropriate contingency reserves and response thresholds for addressing short- and long-term cost overruns. The proposed framework is developed through a structured approach that includes verifying and validating it using synthetic project data. The results obtained from pilot runs confirm the framework's ability to optimize contingency management through the proposed cost overrun response plan. By better aligning risk perception with contingency planning, this approach can help project managers make more informed decisions when managing project risks and avoid costly budget overruns. Ultimately, this can lead to improved project outcomes and enhanced organizational performance. Overall, this study contributes to the growing body of research on project risk management and provides a valuable tool for practitioners seeking to enhance their project risk management practices.

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.

The research on technologies and methodologies for (accurate, real-time, spontaneous, three-dimensional…) facial expression recognition is ongoing and has been fostered in the past decades by advances in classification algorithms like deep learning, which makes them part of the Artificial Intelligence literature. Still, despite its upcoming application to contexts such as human–computer interaction, product and service design, and marketing, only a few literature studies have investigated the willingness of end users to share their facial data with the purpose of detecting emotions. This study investigates the level of awareness and interest of 373 potential consumers towards this technology in the car insurance sector, particularly in the contract drafting phase, with a focus on differentiating the respondents between generation Y and Z. Results show that younger people, individuals with higher levels of education, and social network users feel more confident about this innovative technology and are more likely to share their expressive facial data.

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.

Automation and digitization increase the effectiveness and efficiency of logistics processes. In warehousing, Automated Storage and Retrieval Systems (AS/RS) are largely adopted due to their considerable advantages over traditional warehousing, namely high space utilization, shorter cycle times and improved inventory control. To further enhance such advantages, warehouse operations can be digitized via a Digital Twin (DT) which retrieves data from the real-world industrial process, mimics its behaviour and feeds specific inputs back to the real-world process, after elaboration from a simulation-based digital model. This work presents a DT proposal for a real-world AS/RS system, highlighting its current implementations together with its future developments.

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.