In this study, we investigate the potential sales forecasts of unhandled bread products in retail stores based on factory shipment data. An embedding-based forecasting method that uses large-scale information network embedding (LINE) and simultaneously considers first- and second-order proximities is developed to define similar neighboring stores using their product-store relationship and to predict their potential sales volume. LINE is a network-embedding method that transforms network data into a lowdimensional distributed representation and requires a low computation time, even when applied to large networks. The results show that our proposed method outperforms a simple prediction method (Baseline) and t-SNE, a well-known dimensionality reduction method for high-dimensional data, in terms of accurate product sales prediction via simulation experiments. Furthermore, we conduct a sensitivity analysis to verify the applicability of our proposed method when the forecasting target is expanded to products sold in fewer stores and in stores with less product variety.
|Journal of Advanced Computational Intelligence and Intelligent Informatics
|Published - 2022 3月
ASJC Scopus subject areas
- コンピュータ ビジョンおよびパターン認識