A Study on Clothes Sales Forecast System using Weather Information: Focused on S/S Clothes

Abstract

: This study aims to develop clothing sales forecast system using weather information. As the annual temperature variation affects changes in daily sales of seasonal clothes, sales period can be predicted growth, peak and decline period by changes of temperature. From this perspective, we analyzed the correlation between temperature and sales. Moving average method was applied in order to indicate long-term trend of temperature and sales changes. 7-day moving average temperature at the start/end points of the growth, peak, and decline period of S/S clothing sales was calculated as a reference temperature for sales forecast. According to the 2013 data analysis results, when 7-day moving average temperature value becomes 4 o C or higher, the growth period of S/S clothing sales starts. The peak period of S/S clothing sales starts at 17 o C, up to the highest temperature. When temperature drops below 21 o C after the peak temperature, the decline period of S/S clothing sales is over. The reference temperature was applied to 2014 temperature data to forecast sales period. Through comparing the forecasted sales periods with the actual sales data, validity of the sales forecast system has been verified. Finally this study proposes 'clothing sales forecast system using weather information' as the method of clothing sales forecast.

Use of Weather Factors in Clothing Studies in Korea and its Implications: a Review

“…Because most clothing items are seasonal, we expected warmer temperatures to increase sales of S/S products and decrease sales of F/W products. Most previous studies have demonstrated this relationship (Back et al 2019 ; Han 2021 ; Hong 2019 ; Hong and Lee 2013 ; Hong et al 2012 ; Hwangbo et al 2017 ; Jang and Lee 2002 ; Jang and Lim 2003 ; Kim et al 2017 ; Lee et al 2010 ; Lee et al 2011 ; Lim and Lho 2018 ; Oh et al 2017 ), but temperature changes were found to be insignificant within a certain period. Lim and Lho ( 2018 ) found that sales increase with temperature differences, but the more significant the temperature change to some extent, the less impact they have on sales.…”

“…Few studies mention lags between weather changes and sales (Agnew and Palutikof 1999 ; Arunraj and Ahrens 2016 ), but these studies did not provide empirical results. Instead of daily weather changes and sales, Oh et al ( 2017 ) considered the tendency of temperature change due to the influence of longitudinal weather events such as low pressure and high pressure by averaging the temperature change according to the effect of longitudinal weather events. Oh et al ( 2021 ) studied the effect of anomalous weather on the seasonal clothing market in New York as a place-specific case study.…”

“…In addition, they showed that an increase in consumer purchases tended to complete purchases before the seasons changed. Oh et al ( 2017 ) used the 7-day moving average of temperature data instead of daily average temperature data for analysis. From this data, they posit that rather than daily temperature change, low pressure, high pressure, cold waves, heat waves, and similar climate factors, changes in temperature over a period make consumers feel the need to purchase seasonal clothing.…”