The effect of anomalous weather on the seasonal clothing market in New York

Abstract

The occurrences of losing the merchandising calendar for seasonal clothes have become more frequent due to anomalous weather changes. In order to develop accurate sales forecasts for clothing retailers, weather changes should be incorporated. The purpose of the study was to find the timing of consumers' seasonal clothing demands and the relationship between the timing of consumers' searches for seasonal clothes and temperature changes. Specifically, drawing upon the United States' seasonal clothing demand from winter jacket Google Trends (GT) and air temperature data from empirical evidence, the study provided a methodology to discover the time lag for seasonal clothing demand timing based on temperature changes. Using the past five years (2014-2019) of GT and temperature data for the US state of New York, abnormal weather due to a significant El Niño year (2015-2016) and a weak La Niña year (2017-2018) was analysed. It suggested that consumers' search activities start when the temperature decreased rapidly continuously for at least six days. Furthermore, a plausible index was employed to determine the timing of peak demand with cumulative sums (CUSUMs). Based on the CUSUMs of air temperature for 18 days, consumers started to search for winter jackets at about 118-128 CUSUMs for a La Niña year, 217-248 CUSUMs for an El Niño year and 281 CUSUMs for a normal year. The results give a guideline to meet consumers' seasonal needs in a timely manner.

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

“…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. They provided a methodology to discover the time lag for seasonal clothing demand timing based on temperature changes.…”