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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jiayun Wang & Shanshan Wu & Qingwei Jin & Yijun Wang & Can Chen, 2024. "Identifying Popular Products at an Early Stage of Sales Season for Apparel Industry," Interfaces, INFORMS, vol. 54(3), pages 282-296, May.
    • Handle: RePEc:inm:orinte:v:54:y:2024:i:3:p:282-296
    DOI: 10.1287/inte.2023.0022
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    References listed on IDEAS

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