Xiaoxin Zhu's research while affiliated with Qingdao University of Technology and other places

Supply chain risk management (SCRM) has come into focus in the wake of the COVID-19 pandemic, which saw severe disruption in supply chains (SC) across the globe. So as to secure SCs, insulate them as much as possible from certain risks, and mitigate the severity of the consequences of disruptions, such as those that occur during pandemics, researchers and policy makers need to be abreast of developments in the field of SCRM. This study selected hot and frequently cited papers of WoS from the last 10 years for analysis, performing quantitative visualization analysis so as to establish what the current trends are within the field. Additionally, further attention was paid to those studies that specifically focused on the impact of COVID-19 on SCs. This study used a keyword timeline and clustering analysis map to establish what the main research directions in SCRM were between the years 2012 to 2018, as well as the perspectives from which SC optimization was studied from 2018 to 2021. The key journals and research institutions for SCRM are established, as well as the key categories that the published literature falls under. Cluster analysis shows which areas in the published literature have the most references. Further, the study establishes the direction of current trends within SCRM research, such as those concerning the integration of blockchain technology, and SC designs with low ‘certainty’ requirements, and suggests developing an innovative perspective with regards to SC disruption management. Finally, the study finds that understudied areas of SCRM include the correlations between supply chain resilience and sustainability, the environmental and social dimensions of designing sustainable SC networks, and the lack of focus on the supply chains of low-demand items and SMEs (small and medium-size enterprises).

An adequate provision of medical supplies is critical in the battle against pandemics, such as the ongoing one against COVID-19. This paper proposes a generalized analysis based on the fluctuation period of emergency material demand, constructing a combined forecasting model of time series and support vector machines. The daily demand of specific protective masks donated by the Wuhan COVID-19 Epidemic Prevention and Control Headquarters in the period from February 1 to March 16, 2020 is predicted through the use of data from the Wuhan Red Cross. Compared with traditional linear time series forecasting models, the proposed forecasting model sees its accuracy increased by 37.55%, with the relative errors of MAE, MSE and MAPE being respectively reduced by 37.57%, 60.88% and 37.86%. It transpires that the combined model is able to make full use of the potential information implied in the original data. The decision-making process provides a reference point for the forecast of the demand of medical emergency materials in future major public health emergencies.