Applying deep learning to forecast the demand of a Vietnamese FMCG company

Lê Đức Đạo Lê Đức Đạo; Lê Nguyên Khôi Lê Nguyên Khôi

doi:10.59294/HIUJS.VOL.5.2023.552

Từ khóa:

dự báo nhu cầu khách hàng, ARIMA, học sâu, mạng bộ nhớ ngắn hạn dài, FMCG

Tóm tắt

In the realm of Fast-Moving Consumer Goods (FMCG) companies, the precision of demand forecasting is essential. The FMCG sector operates in a highly uncertain environment marked by rapid market shifts and changing consumer preferences. To address these challenges, the application of deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, has emerged as a vital solution for enhancing forecast accuracy. This research paper focuses on the critical role of demand forecasting in FMCG, emphasizing the need for LSTM-based deep learning models to deal with demand uncertainty and improve predictive outcomes. Through this exploration, we aim to illuminate the link between demand forecasting and advanced deep learning, enabling FMCG companies to thrive in a highly dynamic business landscape.

Abstract

In the realm of Fast-Moving Consumer Goods (FMCG) companies, the precision of demand forecasting is essential. The FMCG sector operates in a highly uncertain environment marked by rapid market shifts and changing consumer preferences. To address these challenges, the application of deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, has emerged as a vital solution for enhancing forecast accuracy. This research paper focuses on the critical role of demand forecasting in FMCG, emphasizing the need for LSTM-based deep learning models to deal with demand uncertainty and improve predictive outcomes. Through this exploration, we aim to illuminate the link between demand forecasting and advanced deep learning, enabling FMCG companies to thrive in a highly dynamic business landscape.

Tài liệu tham khảo

[1] Gruen, T. W., Corsten, D. S., & Bharadwaj, S., “Retail out-of-stocks: A worldwide examination of extent, causes and consumer responses,” Washington, DC: Grocery Manufacturers of America, 2002.

[2] Khashei, M., & Bijari, M., “An artificial neural network (p, d, q) model for timeseries forecasting”, Expert Systems with applications, 37(1), 479-489, 2010.

DOI: https://doi.org/10.1016/j.eswa.2009.05.044

[3] Hewamalage, H., Bergmeir, C., & Bandara, K., “Recurrent neural networks for time series forecasting: Current status and future directions”, International Journal of Forecasting, 37(1), 388-427, 2021.

DOI: https://doi.org/10.1016/j.ijforecast.2020.06.008

[4] Seo, S., A review and comparison of methods for detecting outliers in univariate data sets (Doctoral dissertation, University of Pittsburgh), 2006.

[5] Williams, B. M., & Hoel, L. A., “Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results”, Journal of transportation engineering, 129(6), 664-672, 2003.

DOI: https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664)

[6] Ediger, V. Ş., & Akar, S., “ARIMA forecasting of primary energy demand by fuel in Turkey”, Energy policy, 35(3), 1701-1708, 2007.

DOI: https://doi.org/10.1016/j.enpol.2006.05.009

[7] Abbasimehr, H., Shabani, M., & Yousefi, M., “An optimized model using LSTM network for demand forecasting”, Computers & industrial engineering, 143, 106435, 2020.

DOI: https://doi.org/10.1016/j.cie.2020.106435

[8] Jiang, Q., Tang, C., Chen, C., Wang, X., & Huang, Q., “Stock price forecast based on LSTM neural network. In Proceedings of the Twelfth International Conference on Management Science and Engineering Management (pp. 393-408)”, Springer International Publishing, 2019.

DOI: https://doi.org/10.1007/978-3-319-93351-1_32

[9] Zhang, G. P., “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing, 50, 159-175, 2003.

DOI: https://doi.org/10.1016/S0925-2312(01)00702-0

[10] Dave, E., Leonardo, A., Jeanice, M., & Hanafiah, N., “Forecasting Indonesia exports using a hybrid model ARIMA-LSTM”, Procedia Computer Science, 179, 480-487, 2021.

DOI: https://doi.org/10.1016/j.procs.2021.01.031

[11] Jain, G., & Mallick, B., “A study of time series models ARIMA and ETS”, Available at SSRN, 2898968, 2017.

DOI: https://doi.org/10.2139/ssrn.2898968

[12] Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J., “LSTM: A search space odyssey”, IEEE transactions on neural networks and learning systems, 28(10), 2222-2232, 2016.

DOI: https://doi.org/10.1109/TNNLS.2016.2582924

[13] Hyndman, R. J., & Koehler, A. B., “Another look at measures of forecast accuracy”, International journal of forecasting, 22(4), 679-688, 2006.

DOI: https://doi.org/10.1016/j.ijforecast.2006.03.001

[14] Bergmeir, C., Hyndman, R. J., & Koo, B., “A note on the validity of cross-validation for evaluating autoregressive time series prediction”, Computational Statistics & Data Analysis, 120, 70-83, 2018.

DOI: https://doi.org/10.1016/j.csda.2017.11.003

[15] Taieb, S. B., Taylor, J. W., & Hyndman, R. J., “Coherent probabilistic forecasts for hierarchical time series”, In International Conference on Machine Learning, pp. 3348-3357, PMLR, 2017.