Demand Forecasting in Supply Chains during Promotional Seasons: Differences between Judgmental and Data-Driven Approaches
DOI:
https://doi.org/10.54691/bk3z9r40Keywords:
Supply chain management, demand forecasting, judgmental forecasting, ARIMA, promotional events.Abstract
In recent years, promotional events on e-commerce platforms such as the annual “Double 11” and “Black Friday” have become major shopping festivals and crucial periods for boosting sales. During these promotions, demand surges sharply in a short time, creating challenges for supply chains. Inaccurate forecasting may cause shortages or overstocking, leading to financial pressure and high return rates. Traditionally, managers relied on personal experience to predict sales, but in the era of big data, many firms are turning to statistical models and machine learning for greater accuracy. This study focuses on clothing and 3C (computer, communication, and consumer electronics) products, comparing experience-based forecasts with ARIMA time-series predictions. Results show that experience-based forecasting is flexible but less precise for clothing, while data-driven models perform better for 3C products. The research aims to guide enterprises in choosing forecasting methods suited to their product characteristics.
Downloads
References
[1] Trapero, J. R., Fildes, R., & Davydenko, A. (2013). Analysis of judgmental adjustments in the presence of promotions. International Journal of Forecasting, 29(2), 234–245. https://doi.org/10.1016/j.ijforecast.2012.10.005
[2] Goodwin, P., & Fildes, R. (1999). Judgmental forecasts of time series affected by special events: Does providing a statistical forecast improve accuracy? Journal of Behavioral Decision Making, 12(1), 37–53. https://doi.org/10.1002/(SICI)1099-0771(199903)12:1<37::AID-BDM311>3.0.CO;2-W
[3] Petropoulos, F., Makridakis, S., Spiliotis, E., Assimakopoulos, V., & Nikolopoulos, K. (2022). Forecasting for the future: The value of judgmental interventions. European Journal of Operational Research, 303(1), 1–14. https://doi.org/10.1016/j.ejor.2022.02.006
[4] Chen, H., Li, Q., & Sun, Y. (2020). Demand forecasting using XGBoost: Evidence from online retail. Expert Systems with Applications, 149, 113251. https://doi.org/10.1016/j.eswa.2020.113251
[5] Wang, Y., Zhang, Y., & Zhao, X. (2021). E-commerce demand forecasting with LSTM networks. Electronic Commerce Research and Applications, 46, 101034. https://doi.org/10.1016/j.elerap.2021.101034
[6] Kourentzes, N., Crone, S. F., & Fildes, R. (2014). Forecasting with neural networks: An application to intermittent demand. International Journal of Production Economics, 146(1), 193–201. https://doi.org/10.1016/j.ijpe.2013.12.003
[7] Flores, B. E., & White, E. M. (1988). A hybrid approach to forecasting. Journal of the Operational Research Society, 39(8), 797–805. https://doi.org/10.1057/jors.1988.141
[8] Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). The M4 competition: Results, findings, conclusion, and way forward. International Journal of Forecasting, 34(4), 802–808. https://doi.org/10.1016/j.ijforecast.2018.06.00
[9] Breugelmans, E., & Campo, K. (2016). Effectiveness of in-store displays in relation to promotion type and the moderating role of online promotion support. Journal of Retailing, 92(3), 294–309. https://doi.org/10.1016/j.jretai.2016.02.002
[10] Chen, L., & Lee, H. L. (2020). Dynamic demand forecasting for promotions in e-commerce. Production and Operations Management, 29(7), 1608–1627. https://doi.org/10.1111/poms.13213
[11] Ramanathan, R. (2012). Retail supply chain management: Understanding the challenges. International Journal of Production Research, 50(22), 6573–6579. https://doi.org/10.1080/00207543.2011.613862
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Scientific Journal of Economics and Management Research
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
