Forecasting Potential Sales of Bread Products at Stores by Network Embedding

Kohei Takahashi, Yusuke Goto

研究成果

抄録

In this work, we study the forecast of the potential sales of out-of-stock products in retail stores using factory shipment data. A precise prediction of the potential sales of out-of-stock products in retail stores is beneficial for both baking factories and retail stores because it optimizes the supply chain by introducing a new product in proper quantity at retail stores, and it also creates new opportunities for baking factories to sell their products to retail stores. This study uses high-dimensional and sparse baking factory shipment data, which are unsuitable for prediction using conventional methods because the data have a high computation time and missing values. We employ a network embedding method, LINE, to derive similar stores based on their sales and predict their potential sales. We confirmed that our proposed method outperforms a simple prediction method (Baseline) and t-SNE for accurate product sales prediction via simulation experiments. We also verified our proposed method's applicability when the forecasting target is expanded to products sold in fewer stores and with lower volume.

本文言語English
ホスト出版物のタイトル2021 5th IEEE International Conference on Cybernetics, CYBCONF 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ114-119
ページ数6
ISBN(電子版)9781665403207
DOI
出版ステータスPublished - 2021 6 8
イベント5th IEEE International Conference on Cybernetics, CYBCONF 2021 - Virtual, Sendai, Japan
継続期間: 2021 6 82021 6 10

出版物シリーズ

名前2021 5th IEEE International Conference on Cybernetics, CYBCONF 2021

Conference

Conference5th IEEE International Conference on Cybernetics, CYBCONF 2021
国/地域Japan
CityVirtual, Sendai
Period21/6/821/6/10

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識

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