A Review of Fast Fashion Demand Forecasting Methods

Main Article Content

Beiji Melissa Jin
Business school from the University of Bristol, Bristol, United Kingdom

DOI: https://doi.org/10.70267/aitia.2026490497

Keywords

fast fashion, demand forecasting, machine learning, retail analytics, supply chain management

Abstract

Fast-fashion retail operates in an environment characterized by short product life cycles, high demand volatility, and rapidly changing consumer preferences. Accurate demand forecasting is therefore essential for reducing inventory risk and improving suppl y chain responsiveness. This paper provides a comprehensive review of demand forecasting methods applied in fast fashion. The objective of this review is to analyse the existing traditional forecasting approaches, machine learning techniques, and hybrid forecasting systems. Different specific models were chosen based on the three categories, such as ARIMA, Regression-based for traditional models, LSTM, social media-driven methods for artificial intelligence-based models and hybrid models which combined elements of both traditional and AI-based. The paper concludes that no single method dominates across all fast-fashion forecasting; however, different types of models are used based on specific characteristics. Hybrid methods are considered more promising than traditional forecasting methods due to the reviewed literature showing overall best performance.

Abstract 9 | PDF Downloads 4

References

  • [1] Yu, X., & Lin, L. (2022). Fast Fashion Demand Forecasting Models: A Comparative Study. In H.-L. Chan, S. Ren, & N. Liu (Eds.), Operations Management in the Era of Fast Fashion (pp. 49–69). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-1177-4_4
  • [2] Swaminathan, K., & Venkitasubramony, R. (2024). Demand forecasting for fashion products: A systematic review. International Journal of Forecasting, 40(1), 247–267. https://doi.org/10.1016/j.ijforecast.2023.02.005
  • [3] Christopher, M., Lowson, R., & Peck, H. (2004). Creating agile supply chains in the fashion industry. International Journal of Retail & Distribution Management, 32(8), 367–376. https://doi.org/10.1108/09590550410546188
  • [4] Olamide Raimat Amosu, Praveen Kumar, Yewande Mariam Ogunsuji, Segun Oni, & Oladapo Faworaja. (2024). AI-driven demand forecasting: Enhancing inventory management and customer satisfaction. World Journal of Advanced Research and Reviews, 23(2), 708–719. https://doi.org/10.30574/wjarr.2024.23.2.2394
  • [5] Arrow, K. J., Harris, T., & Marschak, J. (1951). Optimal Inventory Policy. Econometrica, 19(3), 250. https://doi.org/10.2307/1906813
  • [6] Scarf, H. E. (2002). Inventory Theory. Operations Research, 50(1), 186–191. https://doi.org/10.1287/opre.50.1.186.17773
  • [7] Liu, N., Ren, S., Choi, T. -M., Hui, C. -L., & Ng, S. -F. (2013). Sales Forecasting for Fashion Retailing Service Industry: A Review. Mathematical Problems in Engineering, 2013, 1–9. https://doi.org/10.1155/2013/738675
  • [8] Vashishtha, R. K., Burman, V., Kumar, R., Sethuraman, S., Sekar, A. R., & Ramanan, S. (2020). Product age based demand forecast model for fashion retail (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2007.05278
  • [9] Cachon, G. P., & Swinney, R. (2011). The Value of Fast Fashion: Quick Response, Enhanced Design, and Strategic Consumer Behavior. Management Science, 57(4), 778–795. JSTOR.
  • [10] Choi, T.-M. (2018). Impacts of retailer’s risk averse behaviors on quick response fashion supply chain systems. Annals of Operations Research, 268(1–2), 239–257. https://doi.org/10.1007/s10479-016-2257-
  • [11] Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25(1), 3–23. https://doi.org/10.1016/j.ijforecast.2008.11.010
  • [12] Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3), e0194889. https://doi.org/10.1371/journal.pone.0194889
  • [13] Wilson, G. T. (2016). Time Series Analysis: Forecasting and Control, 5th Edition, by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel and Greta M. Ljung, 2015. Published by John Wiley and Sons Inc., Hoboken, New Jersey, pp. 712. ISBN: 978‐1‐118‐675 02‐1. Journal of Time Series Analysis, 37(5), 709–711. https://doi.org/10.1111/jtsa.12194
  • [14] Aburto, L., & Weber, R. (2007). Improved supply chain management based on hybrid demand forecasts. Applied Soft Computing, 7(1), 136–144. https://doi.org/10.1016/j.asoc.2005.06.001
  • [15] Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140–1154. https://doi.org/10.1016/j.ejor.2006.12.004
  • [16] Fildes, R. (2003). New-Product Diffusion Models. International Journal of Forecasting, 19(2), 327–328. https://doi.org/10.1016/S0169-2070(03)00016-5
  • [17] Kharfan, M., Chan, V. W. K., & Firdolas Efendigil, T. (2021). A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches. Annals of Operations Research, 303(1–2), 159–174. https://doi.org/10.1007/s10479-020-03666-w
  • [18] Au, K.-F., Choi, T. -M., & Yu, Y. (2008). Fashion retail forecasting by evolutionary neural networks. International Journal of Production Economics, 114(2), 615–630. https://doi.org/10.1016/j.ijpe.2007.06.013
  • [19] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • [20] Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to Forget: Continual Prediction with LSTM. Neural Computation, 12(10), 2451–2471. https://doi.org/10.1162/089976600300015015
  • [21] Abbasimehr, H., Shabani, M., & Yousefi, M. (2020). An optimized model using LSTM network for demand forecasting. Computers & Industrial Engineering, 143, 106435. https://doi.org/10.1016/j.cie.2020.106435
  • [22] Silva, E. S., Hassani, H., Madsen, D. Ø., & Gee, L. (2019). Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends. Social Sciences, 8(4), 111. https://doi.org/10.3390/socsci8040111
  • [23] Cui, R., Gallino, S., Moreno, A., & Zhang, D. J. (2018). The Operational Value of Social Media Information. Production and Operations Management, 27(10), 1749–1769. https://doi.org/10.1111/poms.12707
  • [24] Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting, 38(4), 1346–1364. https://doi.org/10.1016/j.ijforecast.2021.11.013
  • [25] Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175.
  • [26] Wang, C., & Wang, J. (2025). Research on E-Commerce Inventory Sales Forecasting Model Based on ARIMA and LSTM Algorithm. Mathematics, 13(11), 1838. https://doi.org/10.3390/math13111838
  • [27] Beheshti-Kashi, S., Karimi, H. R., Thoben, K. -D., Lü tjen, M., & Teucke, M. (2015). A survey on retail sales forecasting and prediction in fashion markets. Systems Science & Control Engineering, 3(1), 154–161. https://doi.org/10.1080/21642583.2014.999389
  • [28] 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%3C37::AID-BDM319%3E3.0.CO;2-8
  • [29] Adel, M., Mohammed, A. M., Elsaid, A. H., & Abdelatey, A. (2026). Deep learning for new fashion product demand prediction: Integrating visual similarity and demand correction in cold-start scenarios. International Journal of Data Science and Analytics, 22(1), 9. https://doi.org/10.1007/s41060-025-00888-

Article Sidebar

PDF
Published
Sep 13, 2026
Conference Proceedings Volume
Vol. 16 (2026): Proceedings of the 2026 International Conference on AI and Technological Innovation Applications (AITIA 2026)
Section
Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Jin, Beiji Melissa. “A Review of Fast Fashion Demand Forecasting Methods”. Exploring Science Academic Conference Series, vol. 16, Sept. 2026, pp. 490-7, https://doi.org/10.70267/aitia.2026490497.