ABSTRACT
Following the COVID-19 outbreak, a wide range of scholars and practitioners have come to recognize the potential of Additive Manufacturing (AM) technology in building supply chain resilience and efficiency. However, it remains unclear how AM technology might be able to simultaneously build supply chain efficiency and resilience, given the often conflicting nature of these qualities. This paper employs an ambidextrous perspective on dynamic capability theory to investigate the potential of AM technology to solve this resilience-efficiency dilemma at the supply chain level. The research design involves a hybrid approach, combining focus groups and multiple case studies, with particular attention paid to the African supply chain context. The findings indicate that AM technology presents the potential to develop ambidextrous dynamic capabilities, leading to the reconciliation of resilience and efficiency at the supply chain level. Some determinants, such as data-driven systems, supply chain collaboration, innovation agility and knowledge are found to be critical to enable the development of those capabilities around AM-enabled manufacturing systems. The study contributes to the preparation of the global supply chain for the post-COVID era, where digital technologies such as AM will be fundamental for both building resilience and efficiency simultaneously. Practitioners in emerging economies may benefit directly from the outcomes of this study. Furthermore, managers and policy-makers in developed countries may be made aware of the significance of using AM technology in emerging countries to enhance the performance of the global supply chain.
ABSTRACT
There has been an increased interest among scholars to investigate supply chain resilience (SCRes) in manufacturing and service operations during emerging situations. Grounded in the SCRes theory, this study provides insights into the impact of the COVID-19 outbreak on the automobile and airline supply chain. Both the short and long-term response strategies adopted by the two supply chains are assessed, using a combination of qualitative and quantitative techniques in three distinct phases. In phase one, we use a sequential mixed-method for resilience evaluation, integrating Time-to-Recovery (TTR) and Financial Impact (FI) analysis. In phase two, we conduct an empirical survey involving 145 firms to evaluate the short-term SCRes response strategies. In the third phase, we conduct semi-structured interviews with supply chain executives both from the automobile and airline industries to understand the long-term SCRes response strategies. Our findings indicate that: (i) the automobile industry perceived that the best strategies to mitigate risks related to COVID-19, were to develop localized supply sources and use advanced industry 4.0 (I4.0) technologies. (ii) The airline industry on the other hand, perceived that the immediate need was to get ready for business continuity challenges posed by COVID-19, by defining their operations both at the airports and within the flights. (iii) Importantly, both the sectors perceived Big Data Analytics (BDA) to play a significant role by providing real-time information on various supply chain activities to overcome the challenges posed by COVID-19. (iv) Cooperation among supply chain stakeholders is perceived, as needed to overcome the challenges of the pandemic, and to accelerate the use of digital technologies.
ABSTRACT
For diagnosis of COVID-19, a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to two days to complete, serial testing may be required to rule out the possibility of false negative results, and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of COVID-19 patients. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiologic findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history, and laboratory testing to rapidly diagnose COVID-19 positive patients. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an AUC of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of RT-PCR positive COVID-19 patients who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.