Rationing could be an effective measure for governments to introduce in future pandemics, new research from Durham University Business School finds, alongside a number of recommendations revealed by a pioneering forecasting model.
Dr Kostas Nikolopoulos, Professor in Business Information Systems & Analytics, and Christos Tsinopoulos, Professor in Operations & Project Management, alongside colleagues compared statistical, machine learning and deep learning forecasting models to predict the growth of COVID-19 cases and the subsequent disruptions this would cause across the supply chain at country level.
Using data from the USA, India, UK, Germany, and Singapore up to mid-April 2020, they provide predictive analytics tools and results that can immediately help policymakers and planners make better decisions during the ongoing and future pandemics.
Their findings confirm an excess demand for groceries and electronics, and a reduced demand for automotive, during pandemics – but the model proves that the earlier a lockdown is imposed, the higher the excess demand will be for groceries.
Therefore, the researchers recommend that governments would need to secure high volumes of key products before the lockdown; and, when this is not possible, seriously consider more radical interventions such as rationing.
“We cannot ignore that the progression of COVID-19 across countries drives changes in immediate needs and consumer behaviour, for example, panic buying and overstocking at home. Such changes put an enormous strain to the respective supply chains. For instance, when consumers start panic buying dry pasta, eventually, the whole supply chain involving eggs, flour, wheat, is affected. Therefore, forecasting becomes essential for effective governmental decision making, for managing supply chain resources, and for informing very difficult political decisions as, for example, imposing a lockdown or curfews. Yet forecasting the evolution of the pandemic i.e. the growth in the number of cases per country, is a complex task, partly because of the limited history of pandemic data,” Nikolopoulos says.
Yet this research, conducted during the pandemic, provides a methodology for predicting the reaction of certain critical markets – such as groceries, electronics, automotive, and clothing – based on forecasts of growth of coronavirus and governmental interventions such as lockdown.
Since policymakers during COVID-19 operate in uncharted territory and make tough decisions, this research plays a vital role in supporting decision-making processes.
The research has been accepted for publication at the European Journal of Operational Research.