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Article

Demand Forecasting for Multichannel Fashion Retailers by Integrating Clustering and Machine Learning Algorithms

by 1 and 2,3,4,*
1
Department of Management Sciences, Tamkang University, New Taipei City 251301, Taiwan
2
Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
3
Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan
4
Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Tsai-Chi Kuo
Processes 2021, 9(9), 1578; https://doi.org/10.3390/pr9091578
Received: 27 July 2021 / Revised: 25 August 2021 / Accepted: 31 August 2021 / Published: 3 September 2021
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. View Full-Text
Keywords: demand forecasting; multichannel retailing; fashion retailing; machine learning; clustering; multichannel retailing demand forecasting; multichannel retailing; fashion retailing; machine learning; clustering; multichannel retailing
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MDPI and ACS Style

Chen, I.-F.; Lu, C.-J. Demand Forecasting for Multichannel Fashion Retailers by Integrating Clustering and Machine Learning Algorithms. Processes 2021, 9, 1578. https://doi.org/10.3390/pr9091578

AMA Style

Chen I-F, Lu C-J. Demand Forecasting for Multichannel Fashion Retailers by Integrating Clustering and Machine Learning Algorithms. Processes. 2021; 9(9):1578. https://doi.org/10.3390/pr9091578

Chicago/Turabian Style

Chen, I-Fei, and Chi-Jie Lu. 2021. "Demand Forecasting for Multichannel Fashion Retailers by Integrating Clustering and Machine Learning Algorithms" Processes 9, no. 9: 1578. https://doi.org/10.3390/pr9091578

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