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머신 러닝을 활용한 의류제품의 판매량 예측 모델 - 아우터웨어 품목을 중심으로 -

Sales Forecasting Model for Apparel Products Using Machine Learning Technique - A Case Study on Forecasting Outerwear Items -

김은희|채진미
한국의류산업학회 (The Society of Fashion and Textile Industry)|2021.08|한국의류산업학회지 (The Korean Fashion and Textile Research Journal)|제23권 제4호|pp. 480-490

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참고문헌

키워드

의사결정나무k-means clustering판매예측k-평균 군집분석판매패턴아우터웨어 속성sales forecastingdecision tree classifiesales patternouterwear item attributes
주제분류생활과학

초록

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.

Abstract

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.

참고문헌 (45)

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