Supply Chain Recovery Strategies for High Demand Products Based on the Impact of Capacity and Transportation Disruptions

The outbreak of the COVID-19 has had a huge impact on the manufacturing supply chain, especially the supply chain of high-demand products, and is mainly reflected in the double interruption of production capacity and transportation. The research aims to use system dynamics to explore how government subsidies can play a role in supply chain recovery when government subsidies are limited, which provides a new idea for improving supply chain management. In order to explore the impact of government subsidy strategies on supply chain recovery in the context of supply chain disruptions, this paper takes high-demand products during the epidemic as the research object, and takes the government's subsidy choices under the impact of production capacity and transportation disruptions as the entry point for recovery strategies. The cumulative total profit of chain members is used as a judgment indicator, and systems dynamics is used to conduct modeling and simulation to build a secondary supply chain for manufacturers and distribution centers and simulate eight scenarios of different levels of production capacity and transportation interruptions, clarifying the impact of government subsidies on supply the impact of chain recovery. The research results show that, for secondary supply chains, whether in the scenario of partial or complete transportation interruption, government subsidies to manufacturers make supply chain recovery more effective, government subsidies do not have an immediate recovery effect during production capacity and transportation interruptions, and that under the complete interruption of production capacity, the cumulative total value of the supply chain after increasing government subsidies has rebounded in a spiral.

Online Rumor Diffusion Model Based on Variation and Silence Phenomenon in the Context of COVID-19.

In the era of mobile internet, information dissemination has made a new leap in speed and in breadth. With the outbreak of the coronavirus disease 2019 (COVID-19), the COVID-19 rumor diffusion that is not limited by time and by space often becomes extremely complex and fickle. It is also normal that a piece of unsubstantiated news about COVID-19 could develop to many versions. We focus on the stagnant role and information variants in the process of rumor diffusion about COVID-19, and through the study of variability and silence in the dissemination, which combines the effects of stagnation phenomenon and information variation on the whole communication system in the circulation of rumors about COVID-19, based on the classic rumor SIR (Susceptible Infected Recovered) model, we introduce a new concept of "variation" and "oyster". The stability of the new model is analyzed by the mean field equation, and the threshold of COVID-19 rumor propagation is obtained later. According to the results of the simulation experiment, whether in the small world network or in the scale-free network, the increase of the immure and the silent probability of the variation can effectively reduce the speed of rumor diffusion about COVID-19 and is conducive to the dissemination of the truth in the whole population. Studies have also shown that increasing the silence rate of variation can reduce COVID-19 rumor transmission more quickly than the immunization rate. The interesting discovery is that at the same time, a higher rumor infection rate can bring more rumors about COVID-19 but does not always maintain a high number of the variation which could reduce variant tendency of rumors. The more information diffuses in the social group, the more consistent the version and content of the information will be, which proves that the more adequate each individual information is, the slower and less likely rumors about COVID-19 spread. This consequence tells us that the government needs to guide the public to the truth. Announcing the true information publicly could instantly contain the COVID-19 rumor diffusion well rather than making them hidden or voiceless.

Data-Driven Dynamic Adjustment and Optimization Model of Emergency Logistics Network in Public Health.

BACKGROUND AND AIM: In the long-term prevention of the COVID-19 pandemic, parameters may change frequently for various reasons, such as the emergence of mutant strains and changes in government policies. These changes will affect the efficiency of the current emergency logistics network. Public health emergencies have typical unstructured characteristics such as blurred transmission boundaries and dynamic time-varying scenarios, thus requiring continuous adjustment of emergency logistics network to adapt to the actual situation and make a better rescue. PRACTICAL SIGNIFICANCE: The infectivity of public health emergencies has shown a tendency that it first increased and then decreased in the initial decision-making cycle, and finally reached the lowest point in a certain decision-making cycle. This suggests that the number of patients will peak at some point in the cycle, after which the public health emergency will then be brought under control and be resolved. Therefore, in the design of emergency logistics network, the infectious ability of public health emergencies should be fully considered (ie, the prediction of the number of susceptible population should be based on the real-time change of the infectious ability of public health emergencies), so as to make the emergency logistics network more reasonable. METHODS: In this paper, we build a data-driven dynamic adjustment and optimization model for the decision-making framework with an innovative emergency logistics network in this paper. The proposed model divides the response time to emergency into several consecutive decision-making cycles, and each of them contains four repetitive steps: (1) analysis of public health emergency transmission; (2) design of emergency logistics network; (3) data collection and processing; (4) adjustment and update of parameters. RESULTS: The result of the experiment shows that dynamic adjustment and update of parameters help to improve the accuracy of describing the evolution of public health emergency transmission. The model successively transforms the public health emergency response into the co-evolution of data learning and optimal allocation of resources. CONCLUSION: Based on the above results, it is concluded that the model we designed in this paper can provide multiple real-time and effective suggestions for policy adjustment in public health emergency management. When responding to other emergencies, our model can offer helpful decision-making references.