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From global pandemics to geopolitical turmoil, leaders in logistics, productallocation, procurement and operations are facing increasing difficulty withsafeguarding their organizations against supply chain vulnerabilities. It isrecommended to opt for forecasting against trending based benchmark becauseauditing a future forecast puts more focus on seasonality. The forecastingmodels provide with end-to-end, real time oversight of the entire supply chain,while utilizing predictive analytics and artificial intelligence to identifypotential disruptions before they occur. By combining internal and externaldata points, coming up with an AI-enabled modelling engine can greatly reducerisk by helping retail companies proactively respond to supply and demandvariability. This research paper puts focus on creating an ingenious way totackle the impact of COVID19 on Supply chain, product allocation, trending andseasonality.  Key words: Supply chain, covid-19, forecasting, coronavirus, manufacturing,seasonality, trending, retail.

The challenges and realities of retailing in a COVID-19 world:  Identifying trending and Vital During Crisis keywords during Covid-19 using  Machine Learning (Austria as a case study)