COVID-19 Outbreaks Challenges to Global Supply Chain Management and Demand Forecasting on SCM Using Autoregressive Models

ABSTRACT

Global-SCM (Global-supply-chain-management) is characterised as the distribution of goods and services to maximise profit and reduce waste within the global network of a transnational business. The goal of this chapter is to provide information on the challenges faced by global supply chain management as a result of the Covid-19 pandemic, and to suggest solutions to these challenges through machine learning and artificial intelligence. The work presented in this research constitutes a contribution to modelling and forecasting the demand in a retail sales company, by using time series approach. Our work shows how the historical data of retail items could be utilized to forecast future demands and how these forecasts affect the supply chain management. The historical demand information was used for forecasting future demands using several autoregressive integrated moving average models like AR, MA, ARMA, ARIMA, and SARIMA. By comparing their Akaike's Information Criterion (AIC) values we get to know that ARIMA model is best suited for demand forecasting of the current retail item sale data. The result from the proposed work so obtained proves that the proposed model could be utilized for demand forecasting to meet the future demands in the retail sales item company. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.