Machine Learning for Demand Prediction of Seasonal Second-hand Fashion Items Based on Prior and Fine-tuning Prediction Models

Fuyu Saito, Haruka Yamashita, Hokuto Sasaki, Masayuki Goto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the rapid development of information technology in recent years, it has become common for consumers to purchase various products via electric commerce (EC) sites. As a case study, this study focuses on ZOZOUSED, which is engaged in the business of buying used clothes from users, and reselling them as second-hand goods. From the perspective of inventory and management costs, it is desirable for items to be sold as soon as possible after they are listed on EC sites, and the number of listed items has been conventionally controlled, depending on the experience of item managers. However, owing to the subjective assessment of item managers, unnecessary price reductions for sales promotion of items, or opportunity losses triggered by an excessive number of listed items, may occur. For reasonable item management, the demand prediction for items by customers is a crucial task required to develop the optimal listing plan that balances supply and demand. Therefore, this study proposes a forecasting method of sales figures for the actual operation of listing second-hand goods, which comprises two-stage models: the first model is a prior seasonal long-term prediction of sales figures for each item group based on seasonal similarity, and the second model is a short-term fine-tuning prediction for daily operation via residual predictions with recent data. Furthermore, we apply the proposed model to the actual data of past sales figures accumulated in ZOZOUSED, and analyze the obtained results to demonstrate the usefulness of the proposed method. In addition, we empirically demonstrate the effectiveness of the proposed method by designing and performing an empirical experiment on an actual business by applying the output of the proposed method as a new index for determining the number of new items to be listed.

Original languageEnglish
Title of host publication2021 IEEE 12th International Workshop on Computational Intelligence and Applications, IWCIA 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665444255
DOIs
Publication statusPublished - 2021
Event12th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2021 - Virtual, Online, Japan
Duration: 2021 Nov 62021 Nov 7

Publication series

Name2021 IEEE 12th International Workshop on Computational Intelligence and Applications, IWCIA 2021 - Proceedings

Conference

Conference12th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2021
Country/TerritoryJapan
CityVirtual, Online
Period21/11/621/11/7

Keywords

  • EC site
  • machine learning
  • sale prediction
  • sales history data
  • second-hand fashion item

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

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