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JACIII Vol.26 No.2 pp. 236-246
doi: 10.20965/jaciii.2022.p0236
(2022)

Paper:

Embedding-Based Potential Sales Forecasting of Bread Product

Kohei Takahashi* and Yusuke Goto**

*Graduate School of Software and Information Science, Iwate Prefectural University
152-52 Sugo, Takizawa, Iwate 020-0693, Japan

**College of Systems Engineering and Science, Shibaura Institute of Technology
307 Fukasaku, Minuma-ku, Saitama, Saitama 337-8570, Japan

Received:
July 30, 2021
Accepted:
February 7, 2022
Published:
March 20, 2022
Keywords:
network embedding, forecasting, bread, bipartite graph
Abstract
Embedding-Based Potential Sales Forecasting of Bread Product

Flow of forecasting simulation experiment

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 low-dimensional 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.

Cite this article as:
K. Takahashi and Y. Goto, “Embedding-Based Potential Sales Forecasting of Bread Product,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.2, pp. 236-246, 2022.
Data files:
References
  1. [1] K. Takahashi and Y. Goto, “Forecasting Potential Sales of Bread Products at Stores by Network Embedding,” Proc. of the 2021 5th IEEE Int. Conf. on Cybernetics (CYBCONF 2021), pp. 114-119, 2021.
  2. [2] W. Elmenreich and I. J. Rudas, “Special Issue on Computational Cybernetics,” J. Adv. Comput. Intell. Intell. Inform., Vol.9, No.4, p. 345, doi: 10.20965/jaciii.2005.p0345, 2005.
  3. [3] S. Beer, “What Has Cybernetics to Do With Operational Research,” J. of the Operational Research Society, Vol.10, No.1, pp. 1-21, 1959.
  4. [4] R. Fildes, K. Nikolopoulos, S. F. Crone, and A. A. Syntetos, “Forecasting and Operational Research: A Review,” J. of the Operational Research Society, Vol.59, No.9, pp. 1150-1172, 2008.
  5. [5] R. Carbonneau, K. Laframboise, and R. Vahidov, “Application of Machine Learning Techniques for Supply Chain Demand Forecasting,” European J. of Operational Research, Vol.184, No.3, pp. 1140-1154, 2008.
  6. [6] M. Seyedan and F. Mafakheri, “Predictive Big Data Analytics for Supply Chain Demand Forecasting: Methods, Applications, and Research Opportunities,” J. of Big Data, Vol.7, Article No.53, 2020.
  7. [7] T. Asai, H. Kusakari, and T. Matsuda, “An Analysis of Demand for Bread Focusing on Bread Varieties,” J. of Rural Economics, Vol.90, No.4, pp. 397-400, 2019 (in Japanese).
  8. [8] K. H. Jho and M. Yoshida, “Factors of food expenditure in Household: The Effect Analysis of Householder’s Age, Birth Cohort, Income and Price,” J. of Rural Problems, Vol.33, No.1, pp. 10-17, 1997 (in Japanese).
  9. [9] H. Takeyasu, Y. Higuchi, and K. Takeyasu, “A Hybrid Method to Improve Forecasting Accuracy in the Case of Bread,” Int. J. of Information and Communication Technology Research, Vol.2, No.11, pp. 804-812, 2012.
  10. [10] J. Huber, A. Gossmann, and H. Stuckenschmidt, “Cluster-based Hierarchical Demand Forecasting for Perishable Goods,” Expert Systems With Applications, Vol.76, pp. 140-151, 2017.
  11. [11] C. Yang and H. Sutrisno, “Short-Term Sales Forecast of Perishable Goods for Franchise Business,” Proc. of 2018 10th Int. Conf. on Knowledge and Smart Technology, pp. 101-105, 2018.
  12. [12] R. Chen, Q. Hua, Y. Chang, B. Wang, L. Zhang, and X. Kong, “A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks,” IEEE Access, Vol.6, pp. 64301-64320, 2018.
  13. [13] Y. Takama, H. Shibata, and Y. Shiraishi, “Matrix-Based Collaborative Filtering Employing Personal Values-Based Modeling and Model Relationship Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.6, pp. 719-727, doi: 10.20965/jaciii.2020.p0719, 2020.
  14. [14] P. Cui, X. Wang, J. Pei, and W. Zhu, “A Survey on Network Embedding,” IEEE Trans. on Knowledge and Data Engineering, Vol.31, No.5, pp. 833-852, 2019.
  15. [15] J. T. Huang, A. Sharma, S. Sun, L. Xia, D. Zhang, P. Pronin, J. Padmanabhan, G. Ottaviano, and L. Yang, “Embedding-based Retrieval in Facebook Search,” Proc. of the 26th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 2553-2561, 2020.
  16. [16] J. Zhao, J. Wang, M. Sigdel, B. Zhang, P. Hoang, M. Liu, and M. Korayem, “Embedding-based Recommender System for Job to Candidate Matching on Scale,” Proc. of the 27th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’21 IRS Workshop), 8pp., 2021.
  17. [17] B. Perozzi, R. Al-Rfou, and S. Skiena, “DeepWalk: Online Learning of Social Representations,” Proc. of the 20th AMC SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 701-710, 2014.
  18. [18] A. Grover and J. Leskovec, “node2vec: Scalable Feature Learning for Networks,” Proc. of the 22th AMC SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 855-864, 2016.
  19. [19] D. Gunning, M. Stefik, J. Choi, T. Miller, S. Stumpf, and G.Z. Yang, “XAI: Explainable Artificial Intelligence,” Science Robotics, Vol.4, No.37, doi: 10.1126/scirobotics.aay7120, 2019.
  20. [20] J. Tang, M. Qu, M. Wang, M. Zhang, J. Tan, and Q. Mei, “LINE: Large-Scale Information Network Embedding,” Proc. of the 24th Int. Conf. on World Wide Web, pp. 1067-1077, 2015.
  21. [21] M. Nishiguchi, H. Morita, Y. Shirai, and Y. Goto, “Readable Contrast Mining Method for Heterogeneous Bipartite Networks with Class Label,” Proc. of the Pacific Asia Conf. on Information Systems 2020, 2020.
  22. [22] H. Hotelling, “The Generalization of Student’s Ratio,” The Annals of Mathematical Statistics, Vol.2, No.3, pp. 360-378, 1931.
  23. [23] L. van der Maaten and G. Hinton, “Visualizing Data Using t-SNE,” J. of Machine Learning Research, Vol.9, No.86, pp. 2579-2605, 2008.
  24. [24] J. Huber and H. Stuckenschmidt, “Daily Retail Demand Forecasting Using Machine Learning with Emphasis on Calendric Special Days,” Int. J. of Forecasting, Vol.36, pp. 1420-1438, 2020.

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Last updated on Sep. 26, 2022