Demand Forecasting for Multichannel Fashion Retailers by Integrating Clustering and Machine Learning Algorithms
學年 110
學期 1
出版（發表）日期 2021-09-03
作品名稱 Demand Forecasting for Multichannel Fashion Retailers by Integrating Clustering and Machine Learning Algorithms
作品名稱（其他語言）
著者 I-Fei Chen; Chi-Jie Lu
單位
出版者
著錄名稱、卷期、頁數 Processes 9(9), 1578
摘要 In today’s rapidly changing and highly competitive industrial environment, a new and emerging business model—fast fashion—has started a revolution in the apparel industry. Due to the lack of historical data, constantly changing fashion trends, and product demand uncertainty, accurate demand forecasting is an important and challenging task in the fashion industry. This study integrates k-means clustering (KM), extreme learning machines (ELMs), and support vector regression (SVR) to construct cluster-based KM-ELM and KM-SVR models for demand forecasting in the fashion industry using empirical demand data of physical and virtual channels of a case company to examine the applicability of proposed forecasting models. The research results showed that both the KM-ELM and KM-SVR models are superior to the simple ELM and SVR models. They have higher prediction accuracy, indicating that the integration of clustering analysis can help improve predictions. In addition, the KM-ELM model produces satisfactory results when performing demand forecasting on retailers both with and without physical stores. Compared with other prediction models, it can be the most suitable demand forecasting method for the fashion industry.
關鍵字 demand forecasting;multichannel retailing;fashion retailing;machine learning;clustering;multichannel retailing
語言 en
ISSN 2227-9717
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Chi-Jie Lu
審稿制度 是
國別 CHE
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121950 )