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The coronavirus disease (COVID-19) pandemic has emerged as an unprecedented health crisis worldwide and heavily disrupted the healthcare supply chain. This study focuses on analysing the different types of disruptions occurring in personal protective equipment (PPE) supply chains during the COVID-19 pandemic and on proposing mitigation strategies that are fit to the global scale and many interdependencies that are characteristic for this pandemic. The authors construct a conceptual system dynamics model (SD) based on the literature and adjusted with the use of empirical data (interviews) to capture the complexity of a global supply chain and identify leverage points (mitigation strategies).

This research follows a mix-methods approach. First, the authors developed a conceptual framework based on four types of disruptions that usually occur during health emergencies (direct effect, policy, supply chain strategy, and behaviourally induced disruptions). Second, the authors collected and analysed data from interviews with experts in the PPE supply chain. Based on the interviews data, the authors developed a conceptual system dynamics (SD) model that allows to capture the complex and dynamic interplay between the elements of the global supply chain system, by highlighting key feedback loops, delays, and the way the mitigation strategies can impact on them. From this analysis, the authors developed four propositions for supply chain risk management (SCRM) in global health emergencies and four recommendations for the policy and decision makers.

The SD model highlights that without a combination of mitigation measures, it is impossible to overcome all disruptions. As such, a co-ordinated effort across the different countries and sectors that experience the disruptions is needed. The SD model also shows that there are important feedback loops, by which initial disruptions create delays and shortages that propagate through the supply chain network. If the co-ordinated mitigation measures are not implemented early at the onset of the pandemic, these disruptions will be persistent, creating potential shortages of PPE and other critical equipment at the onset of a pandemic – when they are most urgently needed.

This research enriches the understanding of the disruptions of PPE supply chains on the systems level and proposes mitigation strategies based on empirical data and the existing literature.

This study explored the experiences of Irish emergency medical services (EMS) first responders during the first nationwide restrictions to curb the spread of COVID-19.

A systematic literature review (SLR) of research into healthcare workers' and first responders' experiences during the COVID-19 and 2003 SARS pandemics was performed. The SLR informed the content of an online questionnaire distributed via the Irish Pre-Hospital Emergency Care Council to 2,092 first responders on its live register. Data analysis used both descriptive and content analysis.

EMS first responders faced many challenges including PPE quality, training on its use, issues with decontamination facilities, and organisational effectiveness. Emotional challenges included the anxiety experienced, the impact on families, and ethical dilemmas confronted related to patient care. Positive findings also emerged, such as first responders' dedication to working through the pandemic, collegiality, and the community goodwill displayed.

While investigations of the impact of the COVID-19 pandemic on healthcare workers have been undertaken globally, studies focussing exclusively on the experiences of EMS first responders have been rare. This study addressed this knowledge gap, providing an insight into the challenges and successes experienced by first responders and identifying opportunities for learning that can be applied to future public health emergencies.

This study investigated how organizations can maintain their supply chain (SC) resilience in situations where high-impact shocks cannot be absorbed and what capabilities are needed. The article is an empirical exploration of a socio-ecological view of resilience in the SC context.

The case under study in this article is that of Médecins sans Frontières (MSF) and MSF's reconfiguration of its supply management processes in response to the supply shocks during the coronavirus disease 2019 (COVID-19) pandemic. In total, 503 internal documents and ERP extractions from six databases from late 2019 to September 2020, 43 semi-structured interviews and a 3-round policy Delphi process were used to investigate this phenomenon.

The authors' results show that throughout the pandemic, MSF adapted its procurement and supply processes to cope with supply shortages at both the international and local levels of the SC. This was possible due to the organization's capacity to use its exploitation and exploration capabilities of the organization at the same time.

This research is based on the single in-depth case study of a medical aid organization. Further research should investigate this phenomenon in commercial companies with similar or different organizational structures.

This study constitutes a first attempt to empirically demonstrate that the four phases of the adaptive cycle put forth in the panarchy theory constitute a suitable representation of the reconfigurations that SCs follow in response to a high-impact shock. The study also adds to the growing body of knowledge on resilience by including ambidexterity as a mechanism to achieve resilience.

The COVID-19 pandemic caused global supply disruptions and shortages that resulted in countries battling over desperately needed (medical) supplies. In this mayhem, additive manufacturing (AM) provided relief to the strained healthcare systems and manufacturing environments by offering an alternative way to rapidly produce desired products. This study sheds light on how AM was used globally in response to the COVID-19 pandemic.

The study undertakes a systematic and content-centric review of 289 additively manufactured products made in response to the COVID-19 pandemic. Additionally, quantitative frequency-based text mining and various descriptive analyses were applied that support the investigation of the subject under regard.

Results show that AM was primarily used in the medical domain for the production of standard medical items, such as personal protective equipment (PPE) but also for non-obvious and new applications (e.g. swab simulator, rapid diagnostic kits, etc.). Also, certain paradigm shifts were observed, as the effective move to mass production and the mitigation of problems related to certification and standardization emerged as prominent management prospects. Nevertheless, various obstacles arose and remained in the path of lasting AM success, especially with respect to print quality, raw material supply and technological versatility.

Due to the actuality of the topic under investigation, no comparable study has so far been conducted. The systematic review provides a conclusive and precise foundation for further analysis and subsequent discussions. Additionally, no comparable study mapping such a wide array of different AM products exists today.

In response to shortages in personal protective equipment (PPE) during the COVID-19 pandemic, makers, community groups and manufacturers around the world utilised 3D printing to fabricate items, including face shields and face masks for healthcare workers and the broader community. In reaction to both local and global needs, numerous designs emerged and were shared online. In this paper, 37 face shields and 31 face masks suitable for fused filament fabrication were analysed from a fabrication perspective, documenting factors such as filament use, time to print and geometric qualities. 3D print times for similar designs varied by several hours, meaning some designs could be produced in higher volumes. Overall, the results show that face shields were approximately twice as fast to 3D print compared to face masks and used approximately half as much filament. Additionally, a face shield typically required 1.5 parts to be 3D printed, whereas face masks required five 3D printed parts. However, by quantifying the print times, filament use, 3D printing costs, part dimensions, number of parts and total volume of each design, the wide variations within each product category could be tracked and evaluated. This data and objective analysis will help makers, manufacturers, regulatory bodies and researchers consolidate the 3D printing response to COVID-19 and optimise the ongoing strategy to combat supply chain shortages now and in future healthcare crises.

The COVID-19 pandemic significantly increased demand for medical and protective equipment by frontline health workers, as well as the general community, causing the supply chain to stretch beyond capacity, an issue further heightened by geographical and political lockdowns. Various 3D printing technologies were quickly utilised by businesses, institutions and individuals to manufacture a range of products on-demand, close to where they were needed. This study gathered data about 91 3D printed projects initiated prior to April 1, 2020, as the virus spread globally. It found that 60% of products were for personal protective equipment, of which 62% were 3D printed face shields. Fused filament fabrication was the most common 3D print technology used, and websites were the most popular means of centralising project information. The project data provides objective, quantitative insight balanced with qualitative critical review of the broad trends, opportunities and challenges that could be used by governments, health and medical bodies, manufacturing organisations and the 3D printing community to streamline the current response, as well as plan for future crises using a distributed, flexible manufacturing approach.

Artificial intelligence (AI) has started to receive attention in the field of digital entrepreneurship. However, few studies propose AI-based models aimed at assisting entrepreneurs in their day-to-day operations. In addition, extant models from the product design literature, while technically promising, fail to propose methods suitable for opportunity development with high level of uncertainty. This study develops and tests a predictive model that provides entrepreneurs with a digital infrastructure for automated testing. Such an approach aims at harnessing AI-based predictive technologies while keeping the ability to respond to the unexpected.

Based on effectuation theory, this study identifies an AI-based, predictive phase in the “build-measure-learn” loop of Lean startup. The predictive component, based on recommendation algorithm techniques, is integrated into a framework that considers both prediction (causal) and controlled (effectual) logics of action. The performance of the so-called active learning build-measure-predict-learn algorithm is evaluated on a data set collected from a case study.

The results show that the algorithm can predict the desirability level of newly implemented product design decisions (PDDs) in the context of a digital product. The main advantages, in addition to the prediction performance, are the ability to detect cases where predictions are likely to be less precise and an easy-to-assess indicator for product design desirability. The model is found to deal with uncertainty in a threefold way: epistemological expansion through accelerated data gathering, ontological reduction of uncertainty by revealing prior “unknown unknowns” and methodological scaffolding, as the framework accommodates both predictive (causal) and controlled (effectual) practices.

Research about using AI in entrepreneurship is still in a nascent stage. This paper can serve as a starting point for new research on predictive techniques and AI-based infrastructures aiming to support digital entrepreneurs in their day-to-day operations. This work can also encourage theoretical developments, building on effectuation and causation, to better understand Lean startup practices, especially when supported by digital infrastructures accelerating the entrepreneurial process.