Identifying Popular Products at an Early Stage of Sales Season for Apparel Industry

Jiayun Wang (), Shanshan Wu (), Qingwei Jin (), Yijun Wang () and Can Chen ()
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Jiayun Wang: School of Management, Zhejiang University, Hangzhou 310058, China
Shanshan Wu: School of Management, Zhejiang University, Hangzhou 310058, China; LineZone Data Technology Co. Ltd., Hangzhou 310052, China
Qingwei Jin: School of Management, Zhejiang University, Hangzhou 310058, China
Yijun Wang: LineZone Data Technology Co. Ltd., Hangzhou 310052, China
Can Chen: LineZone Data Technology Co. Ltd., Hangzhou 310052, China

Interfaces, 2024, vol. 54, issue 3, 282-296

Abstract: The early phase of launching a new apparel product is critical for gaining insights of its performance and classifying it into different categories such as fast selling, average selling, and slow selling. This information is crucial for optimizing product management strategies and making decisions regarding inventory planning, pricing, and marketing. Many apparel companies rely on rule-based methods conducted by experienced sales managers, which consume significant time and energy from managers and often result in delayed information and low prediction accuracy. We propose a new ranking-based method to identify the product popularity that predicts regional and national rankings of products based on sales data at an early stage of a sales season. Our method enables companies to efficiently identify popular products within a remarkably short span of two to four weeks. To validate its efficacy, we compare the model’s predictions with actual orders from a fashion company in 2021, showcasing a notable 5.9% increase in sales volume when using our approach to guide order decisions.

Keywords: applications; predictive analytics; ranking-based method (search for similar items in EconPapers)
Date: 2024
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