Demand forecasting in the fashion business - an example of customized nearest neighbour and linear mixed model approaches

• Autorzy: Henzel Joanna , Wawrowski Łukasz , Kubina Anna , Sikora Marek , Wróbel Łukasz

Identyfikatory

DOI

10.15439/2022F256

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

The fashion industry is characterised by the need to make demand forecasts in advance and for highly volatile products for which we often have no sales history at the time the forecasts are made. For this reason, it is necessary to propose forecast mechanisms that can cope with the given conditions. Such forecasts can be based on expert predictions for generalized product categories. In this case, the task of machine learning forecasting methods would be to divide the aggregate prediction into forecasts for individual products, in each colour and size. In the paper, we present several approaches to this specific task. We present the use of the naive method, custom nearest neighbour approach, parametric linear mixed model and an ensemble approach. Overall, the best results we obtained for the ensemble method. Our research was based on real data from fashion retail.

Słowa kluczowe

EN

industry; computer science; image color analysis; computational modeling; demand forecasting; machine learning; predictive model

PL

przemysł; informatyka; analiza koloru obrazu; modelowanie obliczeniowe; prognozowanie popytu; uczenie maszynowe; model predykcyjny

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 61--65
• Opis fizyczny: Bibliogr. 11 poz., wykr., il.

Twórcy

autor

Henzel Joanna

• Silesian University of Technology Department of Computer Networks and Systems ul. Akademicka 2A, 44-100 Gliwice, Poland

autor

Wawrowski Łukasz

• Łukasiewicz Research Network – Institute of Innovative Technologies EMAG ul. Leopolda 31, 40-189 Katowice, Poland

autor

Kubina Anna

• Łukasiewicz Research Network – Institute of Innovative Technologies EMAG ul. Leopolda 31, 40-189 Katowice, Poland

autor

Sikora Marek

• Silesian University of Technology Department of Computer Networks and Systems ul. Akademicka 2A, 44-100 Gliwice, Poland

autor

Wróbel Łukasz

• Silesian University of Technology Department of Computer Networks and Systems ul. Akademicka 2A, 44-100 Gliwice, Poland

Bibliografia

• 1. M. Z. Babai, J. E. Boylan, and B. Rostami-Tabar, “Demand forecasting in supply chains: a review of aggregation and hierarchical approaches,” International Journal of Production Research, vol. 60, no. 1, pp. 324–348, 2022.
• 2. E. Hofmann and E. Rutschmann, “Big data analytics and demand forecasting in supply chains: a conceptual analysis,” The International Journal of Logistics Management, 2018.
• 3. M. Seyedan and F. Mafakheri, “Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities,” Journal of Big Data, vol. 7, no. 1, pp. 1–22, 2020.
• 4. A. L. Loureiro, V. L. Miguéis, and L. F. da Silva, “Exploring the use of deep neural networks for sales forecasting in fashion retail,” Decision Support Systems, vol. 114, pp. 81–93, oct 2018. http://dx.doi.org/10.1016/j.dss.2018.08.010
• 5. S. Sajja, N. Aggarwal, S. Mukherjee, K. Manglik, S. Dwivedi, and V. Raykar, “Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail,” in ACM International Conference Proceeding Series, 2020. http://dx.doi.org/10.1145/3430984.3430995. ISBN 9781450388177 pp. 281–289.
• 6. T. M. Choi, C. L. Hui, N. Liu, S. F. Ng, and Y. Yu, “Fast fashion sales forecasting with limited data and time,” Decision Support Systems, vol. 59, no. 1, pp. 84–92, mar 2014. http://dx.doi.org/10.1016/j.dss.2013.10.008
• 7. M. Xia, Y. Zhang, L. Weng, and X. Ye, “Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs,” Knowledge-Based Systems, vol. 36, pp. 253–259, dec 2012. http://dx.doi.org/10.1016/j.knosys.2012.07.002
• 8. “Machine learning in predicting demand for fast-moving consumer goods: An exploratory research,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 737–742, 2019. http://dx.doi.org/10.1016/j.ifacol.2019.11.203
• 9. S. J. Taylor and B. Letham, “Forecasting at scale,” The American Statistician, vol. 72, no. 1, pp. 37–45, 2018.
• 10. J. Fox, Applied regression analysis and generalized linear models. Sage Publications, 2015.
• 11. G. E. Box and D. R. Cox, “An analysis of transformations,” Journal of the Royal Statistical Society: Series B (Methodological), vol. 26, no. 2, pp. 211–243, 1964.

Uwagi

1. The work was carried out in part within the project co-financed by European Funds entitled “Decision Support and Knowledge Management System for the Retail Trade Industry (SensAI)" (POIR.01.01.01-00-0871/17-00). The research leading to these results received funding by Young Researchers funds of Department of Computer Networks and Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland (project no.: 02/120/BKM22/0021). This work was partially supported by the European Union through the European Social Fund (grant POWR.03.05.00-00-Z305).

2. Short article

3. Track 1: 17th International Symposium on Advanced Artificial Intelligence in Applications

4. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).