An effective disaster recovery model in supply chain management at times of pandemic

Abstract

An automated model representing communicating states for a given moment in actual time is theorized in an Automated Supply Chain Dual (SC). When handling instability threats in SCs, we look at the circumstances surrounding the architecture and deployment of the digital twins. Combining models with data-driven methods show interrelationships between data risk, modeling disturbances, and performance evaluation. Digital network networking maps are distinctly visually illustrated by the SC blow and modifications amidst the COVID-19 pandemic, along with the after-event recovery method. The findings of this research complement SC risk management’s science and experience by enriching forecasting and corrective findings to exploit the merits of SC modeling, quantitative predictive data, and evidence of disruptions in real-time. The supply chains have been severely impacted by the recent coronavirus pandemic, known as the COVID-19 outbreak. Due to supply failure, demand for certain items has increased significantly, while raw materials supply required to produce those items has decreased; therefore, to address these issues, this paper proposes some strategies for improving service levels for the most sought-after products, such as toilet paper, during a severe pandemic, such as COVID-19.