Forecasting Offline Retail Sales in the COVID-19 Pandemic Period: A Case Study of a Complex Shopping Mall in South Korea

Abstract

This study examines the case of a shopping mall in Seoul, South Korea, based on its offline retail sales data during the period of the enforcement of the COVID-19 pandemic social distancing policy. South Korea implemented strict social distancing, especially in retail categories where people eat out, due to the danger of spreading infectious disease. A total of 55 retail shops’ sales data were analyzed and classified into five categories: fashion, food and beverage (f&b), entertainment, cosmetics and sport. Autoregressive integrated moving average (ARIMA) and exponential smoothing (ETS) models were employed, and the autocorrelation (ACF) and partial autocorrelation (PACF) of each retail category’s sales data were analyzed. The mean absolute percentage error (MAPE) was used to determine the most suitable forecasting model for each retail category. In this way, the f&b and entertainment retail categories, in which people eat out, were found to have been significantly impacted, with their 2022 sales forecasted to be less than 80% of their 2018 and 2019 sales. The fashion retail category was also significantly impacted, slowly recovering sales in 2022. The cosmetics and sport retail categories were little impacted by the COVID-19 outbreak, with their retail sales having already recovered by 2022.

A Comprehensive Approach to Capturing the Impact and Identifying Countermeasures of the COVID-19 Pandemic at Construction Sites in the Republic of Korea

“…For example, many overseas manufacturing plants were shut down in response to the pandemic, disrupting the supply chain. Additionally, delays in delivering materials occurred because some truckers had to quarantine after being exposed to the virus [7,16]. Finally, the supply chain collapse and difficulties obtaining construction materials eventually led to higher prices [18,21,23,31].…”

“…However, measures such as social distancing and a two-week quarantine period for confirmed cases were implemented. Efforts were made to maintain a certain level of productivity by implementing systematic and industry-specific measures to ensure worker safety and productivity [16]. By examining the Korean context and suggesting guidelines for advanced management measures, the findings can provide knowledge for developing effective and ongoing countermeasures against similar infectious diseases and reduce the conceptual gap.…”

Evaluating and Predicting the Long-Term Impact of the COVID-19 Pandemic on Manufacturing Sales within South Africa

“…Kim et al [18] predicted offline retail sales in South Korea during the COVID-19 pandemic, recognising the importance of accurate forecasts in understanding the pandemic's impact on the retail sector. There were two models used: an exponential smoothing (ETS) model and an ARIMA model.…”

Forecasting the Fuel Consumption and Price for a Future Pandemic Outbreak: A Case Study in the USA under COVID-19

“…Exogenous variables allow ARIMAX models to capture the influence of outside variables on the time-series behavior, in addition to the autocorrelation and moving average features of the data. ARIMAX models are particularly suitable for predicting irregular behavior because they can account for the influence of external factors that may contribute to the irregularity or unpredictability in the time series [14]. These external factors could include seasonality, weather conditions, economic indicators, or other relevant variables that may affect the time-series behavior.…”

“…These external factors could include seasonality, weather conditions, economic indicators, or other relevant variables that may affect the time-series behavior. By incorporating these exogenous variables, ARIMAX models can better capture and explain irregular patterns, leading to more accurate predictions [14]. Therefore, several exogeneous variables like temperature, price and mileage traveled have been incorporated, upon which petroleum fuel consumption is likely to depend.…”