The challenges posed by unsustainable practices in today\'s economy underscore the urgent need for a transition toward a circular economy (CE) and a holistic supply chain (SC) perspective. Benchmarking plays a pivotal role in managing circular SCs, offering a metric to gauge progress. However, the lack of consensus on the optimal benchmarking approach hampers effective implementation of circular business practices. To address this gap, we conducted a systematic review of the literature, identifying 29 pertinent publications. The analysis revealed 30 unique attributes and sub-attributes for benchmarking circularity, which were clustered into five main attributes. The main attributes are goals, subjects, key performance indicators (KPIs), data sources, and evaluation methods, while the sub-attributes are organised as features of the main attributes and depicted as a feature model. Drawing from selected publications, we illustrated each feature with examples. Our model offers a comprehensive benchmarking reference for circularity and will be a valuable tool for managers in the transition toward circularity. Supply chains seeking to benchmark their transition to circularity can apply the reference model to ensure that their benchmarking strategy is consistent with state-of-the-art knowledge. By providing a generic circularity benchmarking approach that is valid for diverse economic sectors, our findings contribute to theoretical efforts to address the lack of generic frameworks for CE.

The global community is actively pursuing alternative energy sources to mitigate environmental concerns and decrease dependence on fossil fuels. Biodiesel, recognized as a clean and eco-friendly fuel with advantages over petroleum-based alternatives, has been identified as a viable substitute. However, its commercialization encounters challenges due to costly production processes. Establishing a more efficient supply chain for mass production and distribution could surmount these obstacles, rendering biodiesel a cost-effective solution. Despite numerous review articles across various renewable energy supply chain domains, there remains a gap in the literature specifically addressing the biodiesel supply chain network design. This research entails a comprehensive systematic literature review (SLR) focusing on the design of biodiesel supply chain networks. The primary objective is to formulate an economically, environmentally, and socially optimized supply chain framework. The review also seeks to offer a holistic overview of pertinent technical terms and key activities involved in these supply chains. Through this SLR, a thorough examination and synthesis of existing literature will yield valuable insights into the design and optimization of biodiesel supply chains. Additionally, it will identify critical research gaps in the field, proposing the exploration of fourth-generation feedstocks, integration of multi-channel chains, and the incorporation of sustainability and resilience aspects into the supply chain network design. These proposed areas aim to address existing knowledge gaps and enhance the overall effectiveness of biodiesel supply chain networks.

The supply chain management (SCM) environment is rapidly evolving as a result of the critical role industry 4.0 enablers are playing. Consequently, and to leverage the power of industry 4.0 enablers (I4Es) including; artificial intelligence (AI), machine learning (ML), internet of things (IoT) and big data (BD), researchers and industry practitioners have employed these I4Es to resolve several pain points in supply chain management at all levels, improve operational efficiency, manage demand volatility, tackle cost fluctuations, and make data-driven decisions. Thus, I4Es are working as an evolutionary catalyst for supply chain management in myriads of ways. As such, the application of I4Es in supply chain management (I4Es-in-SCM) research has witnessed tremendous growth over the past years. This study conducted a scientometric analysis and critical review of the I4Es-in-SCM research to monitor trends, visualize the structure of knowledge, identify gaps, and highlight future research avenues. The paper recruited and analysed bibliographic data of 786 papers from Scopus on the application of I4Es-in-SCM research. Analysis showed that the last two decades witnessed a phenomenal growth in research on the application of I4Es-in-SCM, with at least 42 % of all countries making contributions. The analysis showed wider collaboration between countries and noticed a rather significant collaboration among researchers within a given continent. The study also identified the most influential researchers, journals, and countries as well as trending themes and topics in the application of I4Es-in-SCM research. After delineating boundaries of scientific knowledge, the study proffered areas that require further research. The novelty of this study lies in providing a more holistic statistical and visualized analysis of the structure of knowledge, productivity, and scientific collaborations of researchers, journals and countries in the application of I4Es-in-SCM management research. Accordingly, the study outcomes may serve as a useful reference to supply chain academics, early-stage researchers, practitioners, policymakers, and organizations in understanding the structure of knowledge on the application of I4Es-in-SCM research and may constitute a basis for future research.

Supply chain quality management practices are necessary to improve processes, meet consumer quality needs, and enhance supply chain quality management performance in sustainable food networks. Food supply chain quality management and associated practices are considerably studied in global food systems, less so for alternative food networks. There are salient differences between global food systems and alternative food networks, which may reflect on the applicable supply chain quality management practices in the food systems and networks. This paper reviews the literature on supply chain quality management practices, with a focus on alternative food networks. A systematic literature review methodology is adopted, resulting in the analysis of seventy-eight papers, identifying a total of one hundred and three supply chain quality management practices. The identified supply chain quality management practices were analysed in relation to their link to a) place, production, and producer and b) link to (bio)processes. Emerging themes from the analysis are discussed, and some areas of future research were put forward.

The worldwide spread of the COVID-19 disease has had a catastrophic effect on healthcare supply chains. The current manuscript systematically analyzes existing studies mitigating strategies for disruption management in the healthcare supply chain during COVID-19. Using a systematic approach, we recognized 35 related papers. Artificial intelligence (AI), block chain, big data analytics, and simulation are the most important technologies employed in supply chain management in healthcare. The findings reveal that the published research has concentrated mainly on generating resilience plans for the management of COVID-19 impacts. Furthermore, the vulnerability of healthcare supply chains and the necessity of establishing better resilience methods are emphasized in most of the research. However, the practical application of these emerging tools for managing disturbance and warranting resilience in the supply chain has been examined only rarely. This article provides directions for additional research, which can guide researchers to develop and conduct impressive studies related to the healthcare supply chain for different disasters.

In recent years, companies have been under increasing pressure from consumers, grassroots and community organizations, governments, and shareholders to develop and practice sustainable business practices. Academic and corporate interest in sustainable supply chain management has risen considerably in recent years. This can be seen in the number of papers published. This paper aims to systematically investigate the discipline of supply chain management (SCM) within the context of sustainability. The two concepts are increasingly aligned, and sustainable supply chain management (SSCM) represents an evolving field where they explicitly interact. The study proposes a conceptual framework to classify various factors along the triple bottom-line pillars of sustainability issues in the context of supply chains. The findings indicate that the existing literature is primarily focused on individual sustainability and supply chain dimensions rather than taking a more integrated approach. Also, the economic benefits of developing a sustainable supply chain for an organization are discussed in addition to specific features of sustainable supply chains and limitations of existing research; this should stimulate further research. Our analysis revealed trends and gaps, allowing us to create a solid agenda for additional SSCM research.

The COVID-19 pandemic was an unexpected and disruptive event that significantly affected the performance of manufacturing systems and supply chains in various sectors. In this paper, a literature review is provided, which investigates the role that Industry 4.0 technologies and simulation tools have played in addressing the effects of the pandemic crisis. Specifically, a bibliometric analysis provides an overview of the most influential technologies through a study of the most used keywords. While a document analysis, conducted on critical papers that concern real case studies, shows that so far simulation provided support in four main areas: energy consumption, healthcare supply chain & contact tracing, food supply chain, and in general supply chain management. The main outcome of this research work is that Industry 4.0 technologies and simulation models were particularly important during the pandemic crisis and their properties deserve to be deeply exploited in the near future.

In today\'s complex and ever-changing world, Supply Chain Management (SCM) is increasingly becoming a cornerstone to any company to reckon with in this global era for all industries. The rapidly growing interest in the application of Deep Learning (a class of machine learning algorithms) in SCM, has urged the need for an up-to-date systematic review on the research development. The main purpose of this study is to provide a comprehensive vision by reviewing a set of 43 papers about applications of Deep Learning (DL) methods to the SCM, as well as the trends, perspectives, and potential research gaps. This review uses content analysis to answer three research questions namely: 1- What SCM problems have been solved by the use of DL techniques? 2- What DL algorithms have been used to solve these problems? 3- What alternative algorithms have been used to tackle the same problems? And do DL outperform these methods and through which evaluation metrics? This review also responds to this call by developing a conceptual framework in a value-adding perspective that provides a full picture of areas on where and how DL can be applied within the SCM context. This makes it easier to identify potential applications to corporations, in addition to potential future research areas to science. It might also provide businesses a competitive advantage over their competitors by allowing them to add value to their data by analyzing it quickly and precisely.

Although most digital twin (DT) applications for health care have emerged in precision medicine, DTs can potentially support the overall health care process. DTs (twinned systems, processes, and products) can be used to optimize flows, improve performance, improve health outcomes, and improve the experiences of patients, doctors, and other stakeholders with minimal risk.
This paper aims to review applications of DT systems, products, and processes as well as analyze the potential of these applications for improving health care management and the challenges associated with this emerging technology.
We performed a rapid review of the literature and reported available studies on DTs and their applications in health care management. We searched 5 databases for studies published between January 2002 and January 2022 and included peer-reviewed studies written in English. We excluded studies reporting DT usage to support health care practice (organ transplant, precision medicine, etc). Studies were analyzed based on their contribution toward DT technology to improve user experience in health care from human factors and systems engineering perspectives, accounting for the type of impact (product, process, or performance/system level). Challenges related to the adoption of DTs were also summarized.
The DT-related studies aimed at managing health care systems have been growing over time from 0 studies in 2002 to 17 in 2022, with 7 published in 2021 (N=17 studies). The findings reported on applications categorized by DT type (system: n=8; process: n=5; product: n=4) and their contributions or functions. We identified 4 main functions of DTs in health care management including safety management (n=3), information management (n=2), health management and well-being promotion (n=3), and operational control (n=9). DTs used in health care systems management have the potential to avoid unintended or unexpected harm to people during the provision of health care processes. They also can help identify crisis-related threats to a system and control the impacts. In addition, DTs ensure privacy, security, and real-time information access to all stakeholders. Furthermore, they are beneficial in empowering self-care abilities by enabling health management practices and providing high system efficiency levels by ensuring that health care facilities run smoothly and offer high-quality care to every patient.
The use of DTs for health care systems management is an emerging topic. This can be seen in the limited literature supporting this technology. However, DTs are increasingly being used to ensure patient safety and well-being in an organized system. Thus, further studies aiming to address the challenges of health care systems challenges and improve their performance should investigate the potential of DT technology. In addition, such technologies should embed human factors and ergonomics principles to ensure better design and more successful impact on patient and doctor experiences.

The year 2020 can be earmarked as the year of global supply chain disruption owing to the outbreak of the coronavirus (COVID-19). It is however not only because of the pandemic that supply chain risk assessment (SCRA) has become more critical today than it has ever been. With the number of supply chain risks having increased significantly over the last decade, particularly during the last 5 years, there has been a flurry of literature on supply chain risk management (SCRM), illustrating the need for further classification so as to guide researchers to the most promising avenues and opportunities. We therefore conduct a bibliometric and network analysis of SCRA publications to identify research areas and underlying themes, leading to the identification of three major research clusters for which we provide interpretation and guidance for future work. In doing so we focus in particular on the variety of parameters, analytical approaches, and characteristics of multi-criteria decision-making techniques for assessing supply chain risks. This offers an invaluable synthesis of the SCRA literature, providing recommendations for future research opportunities. As such, this paper is a formidable starting point for operations researchers delving into this domain, which is expected to increase significantly also due to the current pandemic.