  From global pandemics to geopolitical turmoil, leaders in logistics, product
allocation, procurement and operations are facing increasing difficulty with
safeguarding their organizations against supply chain vulnerabilities. It is
recommended to opt for forecasting against trending based benchmark because
auditing a future forecast puts more focus on seasonality. The forecasting
models provide with end-to-end, real time oversight of the entire supply chain,
while utilizing predictive analytics and artificial intelligence to identify
potential disruptions before they occur. By combining internal and external
data points, coming up with an AI-enabled modelling engine can greatly reduce
risk by helping retail companies proactively respond to supply and demand
variability. This research paper puts focus on creating an ingenious way to
tackle the impact of COVID19 on Supply chain, product allocation, trending and
seasonality.
  Key words: Supply chain, covid-19, forecasting, coronavirus, manufacturing,
seasonality, trending, retail.
