A decision support model to manage demand disruptions of fast-moving consumer goods during a pandemic in Sri Lanka

Abstract

Decision support models play a crucial role within an organization’s demand planning process when emerging pandemics cause disturbances in demand. The increasing trend of pandemics and the long-lasting struggle it create with unpredicted consumer demand and behaviors necessitate the identification of solutions for sudden demand fluctuations during a disruption. The study addresses the absence of quantitative models in the Sri Lankan context to mitigate disruptions in the demand for fast-moving consumer goods caused by pandemics. The results highlight a substantial difference between the aggregate consumption of "Personal Care" and "Home Care" commodities before and after the pandemic. A literature review identified 23 factors that influence demand disruption during a pandemic globally. Then, validated factors for the Sri Lankan context and assessed using Grey relational analysis. The results highlight inflation, consumer wages, prices, and government regulations have a significant impact on disrupting demand during a pandemic in Sri Lanka. The Grey model with 2-AGO is the most suitable model to manage demand disruptions of ‘Personal Care’ and ‘Home Care’ commodities during a pandemic when compared to traditional time series models. The results will assist companies in managing demand disruptions with rapid demand forecasts and taking precautionary actions against fluctuating influencing factors.