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)

TL;DR

This paper explores how machine learning can be used to identify trending and vital keywords during the COVID-19 crisis to improve supply chain resilience and forecasting in retail, using Austria as a case study.

Contribution

It introduces an AI-enabled modeling approach to detect trends and seasonality in supply chain data during COVID-19, enhancing proactive decision-making.

Findings

AI models effectively identify COVID-19 related trending keywords.

Forecasting based on trending benchmarks improves supply chain resilience.

Proactive analytics reduce supply chain risks during crises.

Abstract

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.