COVID-19 Effect on Supply and Demand of Essential Commodities using Unsupervised Learning Method

The affliction caused by the COVID-19 Pandemic is diverse from other disasters seen so far. Supply chain industries are facing unique challenges in fulfilling the essential needs of the people. The objective of the paper is to analyze the supply and demand of essentials during pre-pandemic and post-pandemic lockdowns using machine learning algorithms. This helps for supply chain industries in forecasting and managing the supply and demand of essential stocks for the future. Data are analyzed using prediction algorithms to check the actual and predicted values. The clustering algorithm along with rolling mean is used for half-yearly data of 2019 and 2020 to identify the sales of different categories of essential commodities. This paper aims at applying intelligence in predicting various categories of sales by providing timely information for B2B Industries during the time of disasters.