Abstract
Retail chains without proper demand forecasting tools are susceptible to significant financial losses by missing out on sales due to stocked-out products or having to throw out expired products because they were overstocked. Extensive research has been carried out, comparing different forecasting methodologies and models, examining the influence of different factors, and highlighting the significance of intermittent forecasting. However, these approaches often struggle to scale up and crumble when dealing with larger retail chains. In this paper, we analyze the real case of a big retail chain with 300 stores, 200 product groups per store and over 1 million products in total. We propose an architecture made up of multiple Neural Network models that can generate forecasts in a timely manner, taking into account calendar features, promotions and the interactions between competing products. It produces daily predictions in under 3 h and retrains weekly the models whose performance deteriorates in 12 h, using an AutoML component to explore deeper and larger architectures. It is a critical component of the company’s Order Management System, achieving a Root Mean Squared Error of 4.48 units across each horizon that was defined by the company.
This research was partially funded by MASOUTIS, a private national retail chain company, through the Special Account for Research Funds of the Aristotle University of Thessaloniki (Project No. 73026) under contract 180311/26-07-2021.
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Notes
- 1.
SARIMAX is an improved version of SARIMA that is able to handle external features, such as promotion and weather features in addition to sales data.
References
Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 2623–2631. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3292500.3330701
Böse, J.H., et al.: Probabilistic demand forecasting at scale. Proc. VLDB Endowment 10(12), 1694–1705 (2017)
Cerqueira, V., Torgo, L., Soares, C.: Machine learning vs statistical methods for time series forecasting: Size matters (2019). https://doi.org/10.48550/arxiv.1909.13316
Eisenach, C., Patel, Y., Madeka, D.: Mqtransformer: multi-horizon forecasts with context dependent and feedback-aware attention. arXiv:2009.14799 (2020)
Falatouri, T., Darbanian, F., Brandtner, P., Udokwu, C.: Predictive analytics for demand forecasting-a comparison of sarima and lstm in retail scm. Procedia Comput. Sci. 200, 993–1003 (2022)
Fildes, R., Ma, S., Kolassa, S.: Retail forecasting: research and practice. Int. J. Forecast. (2019). https://doi.org/10.1016/j.ijforecast.2019.06.004
Huber, J., Stuckenschmidt, H.: Daily retail demand forecasting using machine learning with emphasis on calendric special days. Int. J. Forecast. 36(4), 1420–1438 (2020)
Ingle, C., Bakliwal, D., Jain, J., Singh, P., Kale, P., Chhajed, V.: Demand forecasting: literature review on various methodologies. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–7. IEEE (2021)
İşlek, İ., Öğüdücü, Ş.G.: A retail demand forecasting model based on data mining techniques. In: 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE), pp. 55–60. IEEE (2015)
Liao, S., Yin, J., Rao, W.: Towards accurate retail demand forecasting using deep neural networks. In: Nah, Y., Cui, B., Lee, S.-W., Yu, J.X., Moon, Y.-S., Whang, S.E. (eds.) DASFAA 2020. LNCS, vol. 12114, pp. 711–723. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59419-0_44
Lim, B., Arik, S.O., Loeff, N., Pfister, T.: Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int. J. Forecast. 37(4), 1748–1764 (2021). https://doi.org/10.1016/j.ijforecast.2021.03.012
Ma, S., Fildes, R., Huang, T.: Demand forecasting with high dimensional data: the case of sku retail sales forecasting with intra-and inter-category promotional information. Eur. J. Oper. Res. 249(1), 245–257 (2016)
Ulrich, M., Jahnke, H., Langrock, R., Pesch, R., Senge, R.: Classification-based model selection in retail demand forecasting. Int. J. Forecast. 38(1), 209–223 (2022). https://doi.org/10.1016/j.ijforecast.2021.05.010
Wen, R., Torkkola, K., Narayanaswamy, B., Madeka, D.: A multi-horizon quantile recurrent forecaster. In: The 31st Conference on Neural Information Processing Systems (NIPS 2017), Time Series Workshop (2017)
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Pierros, I., Kouloumpris, E., Zaikis, D., Vlahavas, I. (2023). Retail Demand Forecasting for 1 Million Products. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-27440-4_45
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