How is COVID-19 altering the manufacturing landscape? A literature review of imminent challenges and management interventions
TL;DR: In this article, a systematic literature review reveals the frailty of supply chains and production networks in withstanding the pressures of lockdowns and other safety protocols, including product and workforce shortages.
Abstract: Disruption from the COVID-19 pandemic has caused major upheavals for manufacturing, and has severe implications for production networks, and the demand and supply chains underpinning manufacturing operations. This paper is the first of its kind to pull together research on both—the pandemic-related challenges and the management interventions in a manufacturing context. This systematic literature review reveals the frailty of supply chains and production networks in withstanding the pressures of lockdowns and other safety protocols, including product and workforce shortages. These, altogether, have led to closed facilities, reduced capacities, increased costs, and severe economic uncertainty for manufacturing businesses. In managing these challenges and stabilising their operations, manufacturers are urgently intervening by—investing in digital technologies, undertaking resource redistribution and repurposing, regionalizing and localizing, servitizing, and targeting policies that can help them survive in this altered economy. Based on holistic analysis of these challenges and interventions, this review proposes an extensive research agenda for future studies to pursue.
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TL;DR: In this paper , the authors investigated the impacts of the COVID-19 pandemic and their proactive mediation by adaptive operational decisions in different network design structures in anticipation of and during the pandemic.
Abstract: • Impacts of the COVID-19 pandemic on supply chains are analysed. • Proactive, adaptive operational decisions are examined. • We contribute to the understanding of the preparedness and recovery decisions. • We combine pandemic dynamics, supply chain design, and operational dynamics. This article investigates the impacts of the COVID-19 pandemic and their proactive mediation by adaptive operational decisions in different network design structures in anticipation of and during the pandemic. In generalized terms, we contribute to the understanding of the effect of preparedness and recovery decisions in a pandemic setting on supply chain operations and performance. In particular, we examine the impact of inventory pre-positioning in anticipation of a pandemic and the adaptation of production-ordering policy during the pandemic. Our model combines three levels, which is not often seen jointly in operations management literature, i.e., pandemic dynamics, supply chain design, and operational production-inventory control policies. The analysis is performed for both two- and three-stage supply chains and different scenarios for pandemic dynamics (i.e., uncontrolled propagation or controlled dispersal with lockdowns). Our findings suggest that two-stage supply chains exhibit a higher vulnerability in disruption cases. However, they are exposed to a lower system inertia and show positive effects at the recovery stage. Supply chain adaptation ahead of a pandemic is more advantageous than during the pandemic when specific operational recovery policies are deployed. We show that it is instructive to avoid simultaneous changes in structural network design and operational policies since that can destabilize the production-inventory system and result in higher product shortages.
63 citations
TL;DR: A systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021 is presented in this paper .
Abstract: Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience.
12 citations
TL;DR: In this paper , a modified failure mode and effects analysis (FMEA) is proposed to assess the identified SCRs, which integrates FMEA and best-worst method to provide a double effectiveness.
Abstract: Supply chains have been facing many disruptions due to natural and man-made disasters. Recently, the global pandemic caused by COVID-19 outbreak, has severely hit trade and investment worldwide. Companies around the world faced significant disruption in their supply chains. This study aims to explore the impacts of COVID-19 outbreak on supply chain risks (SCRs). Based on a comprehensive literature review on supply chain risk management, 70 risks are identified and listed in 7 categories including demand, supply, logistics, political, manufacturing, financial and information. Then, a modified failure mode and effects analysis (FMEA) is proposed to assess the identified SCRs, which integrates FMEA and best-worst method to provide a double effectiveness. The results demonstrate the efficiency of the proposed method, and according to the main findings, "insufficient information about demand quantities", "shortages on supply markets", "bullwhip effect", "loss of key suppliers", "transportation breakdowns", "suppliers", "on-time delivery", "government restrictions", "suppliers' temporary closure", "market demand change" and "single supply sourcing" are the top 10 SCRs during the COVID-19 outbreak, respectively. Finally, the practical implications are discussed and useful managerial insights are recommended.
12 citations
TL;DR: In this paper , the authors evaluated the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic.
Abstract: Hesitant attitudes have been a significant issue since the development of the first vaccines—the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population’s decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%.
8 citations
TL;DR: In this paper , the authors proposed a model for sustainable operations management in emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics technologies.
Abstract: Grounded in dynamic capabilities, this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs, it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients, the average daily length of stay (LOS), and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19, and includes data from 238,152 patients. Comparing statistics on daily patient volumes, average LOS, and resource usage, both before and during the COVID-19 pandemic, we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period, these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model, we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158,347, it decreased to 79,805 during-COVID-19. On the other hand, while the average daily LOS was 117.53 min before-COVID-19, this value was calculated to be 165,03 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies, it empirically investigates the impact of different policies on ED operations.
3 citations
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TL;DR: In this article, the authors evaluate the process of systematic review used in the medical sciences to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research and highlight the challenges in developing an appropriate methodology.
Abstract: Undertaking a review of the literature is an important part of any research project. The researcher both maps and assesses the relevant intellectual territory in order to specify a research question which will further develop the knowledge hase. However, traditional 'narrative' reviews frequently lack thoroughness, and in many cases are not undertaken as genuine pieces of investigatory science. Consequently they can lack a means for making sense of what the collection of studies is saying. These reviews can he hiased by the researcher and often lack rigour. Furthermore, the use of reviews of the available evidence to provide insights and guidance for intervention into operational needs of practitioners and policymakers has largely been of secondary importance. For practitioners, making sense of a mass of often-contrad ictory evidence has hecome progressively harder. The quality of evidence underpinning decision-making and action has heen questioned, for inadequate or incomplete evidence seriously impedes policy formulation and implementation. In exploring ways in which evidence-informed management reviews might be achieved, the authors evaluate the process of systematic review used in the medical sciences. Over the last fifteen years, medical science has attempted to improve the review process hy synthesizing research in a systematic, transparent, and reproducihie manner with the twin aims of enhancing the knowledge hase and informing policymaking and practice. This paper evaluates the extent to which the process of systematic review can be applied to the management field in order to produce a reliable knowledge stock and enhanced practice by developing context-sensitive research. The paper highlights the challenges in developing an appropriate methodology.
7,020 citations
TL;DR: An intertwined supply network (ISN) is an entirety of interconnected supply chains (SC) which, in their integrity secure the provision of society and markets with goods and services.
Abstract: An intertwined supply network (ISN) is an entirety of interconnected supply chains (SC) which, in their integrity secure the provision of society and markets with goods and services. The ISNs are o...
863 citations
TL;DR: The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic and can be of value for decision-makers to design SCs that can react adaptively to both positive changes and negative changes.
Abstract: Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. In this paper, we theorize a new notion-the viable supply chain (VSC). In our approach, viability is considered as an underlying SC property spanning three perspectives, i.e., agility, resilience, and sustainability. The principal ideas of the VSC model are adaptable structural SC designs for supply-demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs. Further, we demonstrate how the VSC components can be categorized across organizational, informational, process-functional, technological, and financial structures. Moreover, our study offers a VSC framework within an SC ecosystem. We discuss the relations between resilience and viability. Through the lens and guidance of dynamic systems theory, we illustrate the VSC model at the technical level. The VSC model can be of value for decision-makers to design SCs that can react adaptively to both positive changes (i.e., the agility angle) and be able to absorb negative disturbances, recover and survive during short-term disruptions and long-term, global shocks with societal and economical transformations (i.e., the resilience and sustainability angles). The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic. We emphasize that resilience is the central perspective in the VSC guaranteeing viability of the SCs of the future. Emerging directions in VSC research are discussed.
545 citations
Swansea University1, Loughborough University2, Copenhagen Business School3, University of Bedfordshire4, Aston University5, California State University, Monterey Bay6, University of Bradford7, Government of Tamil Nadu8, Symbiosis International University9, Indian Institute of Management Tiruchirappalli10, Goa Institute of Management11
TL;DR: This study offers a collective insight to many of the key issues and underlying complexities affecting organisations and society from COVID-19, through an information systems and technological perspective.
537 citations
TL;DR: Ten major technologies of Industry 4.0 can fulfil the requirements of customised face masks, gloves, and collect information for healthcare systems for proper controlling and treating of COVID-19 patients.
Abstract: Background and aims COVID 19 (Coronavirus) pandemic has created surge demand for essential healthcare equipment, medicines along with the requirement for advance information technologies applications. Industry 4.0 is known as the fourth industrial revolution, which has the potential to fulfil customised requirement during COVID-19 crisis. This revolution has started with the applications of advance manufacturing and digital information technologies. Methods A detailed review of the literature is done on the technologies of Industry 4.0 and their applications in the COVID-19 pandemic, using appropriate search words on the databases of PubMed, SCOPUS, Google Scholar and Research Gate. Results We found several useful technologies of Industry 4.0 which help for proper control and management of COVID-19 pandemic and these have been discussed in this paper. The available technologies of Industry 4.0 could also help the detection and diagnosis of COVID-19 and other related problems and symptoms. Conclusions Industry 4.0 can fulfil the requirements of customised face masks, gloves, and collect information for healthcare systems for proper controlling and treating of COVID-19 patients. We have discussed ten major technologies of Industry 4.0 which help to solve the problems of this virus. It is useful to provide day to day update of an infected patient, area-wise, age-wise and state-wise with proper surveillance systems. We also believe that the proper implementation of these technologies would help to enhance education and communication regarding public health. These Industry 4.0 technologies could provide a lot of innovative ideas and solution for fighting local and global medical emergencies.
482 citations