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Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry
University of Borås, Faculty of Textiles, Engineering and Business. College of Textile and Clothing Engineering, Soochow University, Suzhou 215168, China.ORCID iD: 0000-0002-1194-597x
College of Textile and Clothing Engineering, Soochow University, Suzhou 215168, China.ORCID iD: 0000-0003-2218-1693
2022 (English)In: Forecasting, E-ISSN 2571-9394, Vol. 4, no 2, p. 565-581Article in journal (Refereed) Published
Abstract [en]

Compared to other industries, fashion apparel retail faces many challenges in predicting future demand for its products with a high degree of precision. Fashion products’ short life cycle, insufficient historical information, highly uncertain market demand, and periodic seasonal trends necessitate the use of models that can contribute to the efficient forecasting of products’ sales and demand. Many researchers have tried to address this problem using conventional forecasting models that predict future demands using historical sales information. While these models predict product demand with fair to moderate accuracy based on previously sold stock, they cannot fully be used for predicting future demands due to the transient behaviour of the fashion industry. This paper proposes an intelligent forecasting system that combines image feature attributes of clothes along with its sales data to predict future demands. The data used for this empirical study is from a European fashion retailer, and it mainly contains sales information on apparel items and their images. The proposed forecast model is built using machine learning and deep learning techniques, which extract essential features of the product images. The model predicts weekly sales of new fashion apparel by finding its best match in the clusters of products that we created using machine learning clustering based on products’ sales profiles and image similarity. The results demonstrated that the performance of our proposed forecast model on the tested or test items is promising, and this model could be effectively used to solve forecasting problems.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 4, no 2, p. 565-581
Keywords [en]
sales forecasting, deep learning, fashion and apparel industry, machine learning
National Category
Other Computer and Information Science
Research subject
Textiles and Fashion (General)
Identifiers
URN: urn:nbn:se:hb:diva-28240DOI: 10.3390/forecast4020031ISI: 000818326000001Scopus ID: 2-s2.0-85143127022OAI: oai:DiVA.org:hb-28240DiVA, id: diva2:1682132
Available from: 2022-07-08 Created: 2022-07-08 Last updated: 2023-02-06Bibliographically approved

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Giri, Chandadevi

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