Reliable sales forecasts are important to the garment industry. In recent
years, the global climate is warming, the weather changes frequently, and
clothing sales are affected by weather fluctuations. The purpose of this study
is to investigate whether weather data can improve the accuracy of product
sales and to establish a corresponding clothing sales forecasting model. This
model uses the basic attributes of clothing product data, historical sales
data, and weather data. It is based on a random forest, XGB, and GBDT adopting
a stacking strategy. We found that weather information is not useful for basic
clothing sales forecasts, but it did improve the accuracy of seasonal clothing
sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced
by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the
stacking strategy model outperformed the voting strategy model, with an average
MSE reduction of 49.28%. Clothing managers can use this model to forecast their
sales when they make sales plans based on weather information.
References
[1]
Aversa, J., Hernandez, T. and Doherty, S. (2021) Incorporating Big Data within Retail Organizations: A Case Study approach. Journal of Retailing and Consumer Services, 60, Article ID: 102447. https://doi.org/10.1016/j.jretconser.2021.102447
[2]
Caro, F. and Martínez-de-Albéniz, V. (2015) Fast Fashion: Business Model Overview and Research Opportunities. In: Agrawal, N. and Smith, S., Eds., Retail Supply Chain Management. International Series in Operations Research & Management Science, Vol. 223, Springer, Boston, 237-264. https://doi.org/10.1007/978-1-4899-7562-1_9
[3]
Martinez-de-Albeniz, V. and Belkaid, A. (2021) Here Comes the Sun: Fashion Goods Retailing under Weather Fluctuations. European Journal of Operational Research, 294, 820-830. https://doi.org/10.1016/j.ejor.2020.01.064
[4]
Tian, X., Cao, S. and Song, Y. (2021) The Impact of Weather on Consumer Behavior and Retail Performance: Evidence from a Convenience Store Chain in China. Journal of Retailing and Consumer Services, 62, Article ID: 102583. https://doi.org/10.1016/j.jretconser.2021.102583
[5]
Parsons, A.G. (2001) The Association between Daily Weather and Daily Shopping Patterns. Australasian Marketing Journal, 9, 78-84. https://doi.org/10.1016/S1441-3582(01)70177-2
[6]
Steinker, S., Hoberg, K. and Thonemann, U.W. (2017) The Value of Weather Information for E-Commerce Operations. Production and Operations Management, 26, 1854-1874. https://doi.org/10.1111/poms.12721
[7]
Oh, J., Ha, K.-J. and Jo, Y.-H. (2022) A Predictive Model of Seasonal Clothing Demand with Weather Factors. Asia-Pacific Journal of Atmospheric Sciences, 58, 667-678. https://doi.org/10.1007/s13143-022-00284-3
[8]
Babongo, F., Appelqvist, P., Chavez-Demoulin, V., Hameri, A.-P. and Niemi, T. (2018) Using Weather Data to Improve Demand Forecasting for Seasonal Products. International Journal of Services and Operations Management, 31, 53-76. https://doi.org/10.1504/IJSOM.2018.094183
[9]
Rose, N. and Dolega, L. (2022) It’s the Weather: Quantifying the Impact of Weather on Retail Sales. Applied Spatial Analysis and Policy, 15, 189-214. https://doi.org/10.1007/s12061-021-09397-0
[10]
Choi, C., Kim, E. and Kim, C. (2011) A Way of Managing Weather Risks Considering Apparel Consumer Behaviors. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1909185
[11]
Boada-Collado, P. and Martinez-de-Albeniz, V. (2020) Estimating and Optimizing the Impact of Inventory on Consumer Choices in a Fashion Retail Setting. Manufacturing & Service Operations Management, 22, 582-597. https://doi.org/10.1287/msom.2018.0764
[12]
Giri, C., Jain, S., Zeng, X. and Bruniaux, P. (2019) A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry. IEEE Access, 7, 95376-95396. https://doi.org/10.1109/ACCESS.2019.2928979
[13]
Wang, C., Sen, M.R., Yao, B., Certik, M. and Randrianarivony, K.A. (2021) Harnessing Machine Learning Emerging Technology in Financial Investment Industry: Machine Learning Credit Rating Model Implementation. Journal of Financial Risk Management, 10, 317-341. https://doi.org/10.4236/jfrm.2021.103019
[14]
Zhou, X., Meng, J., Wang, G. and Qin, X. (2021) A Demand Forecasting Model Based on the Improved Bass Model for Fast Fashion Clothing. International Journal of Clothing Science and Technology, 33, 106-121. https://doi.org/10.1108/IJCST-08-2019-0114
[15]
Falatouri, T., Darbanian, F., Brandtner, P. and Udokwu, C. (2022) Predictive Analytics for Demand Forecasting—A Comparison of SARIMA and LSTM in Retail SCM. Procedia Computer Science, 200, 993-1003. https://doi.org/10.1016/j.procs.2022.01.298
[16]
Hu, W. and Zhang, X. (2020) Commodity Sales Forecast Based on ARIMA Model Residual Optimization. 2020 5th International Conference on Communication, Image and Signal Processing (CCISP), Chengdu, 13-15 November 2020, 229-233. https://doi.org/10.1109/CCISP51026.2020.9273506
[17]
Van Calster, T., Baesens, B. and Lemahieu, W. (2017) A Profit-Driven Order Identification Algorithm for ARIMA Models in Sales Forecasting. Applied Soft Computing, 60, 775-785. https://doi.org/10.1016/j.asoc.2017.02.011
[18]
Ren, S., Chan, H.-L. and Ram, P. (2017) A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty. Annals of Operations Research, 257, 335-355. https://doi.org/10.1007/s10479-016-2204-6
[19]
Ge, N., Sun, L., Shi, X. and Zhao, P. (2019) Research on Sales Forecast of Prophet-LSTM Combination Model. Computer Science, 46, 446-451. (In Chinese)
[20]
Han, Y. (2020) A Forecasting Method of Pharmaceutical Sales Based on ARIMA-LSTM Model. 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), Shenyang, 13-15 November 2020, 336-339. https://doi.org/10.1109/ISCTT51595.2020.00064
[21]
Li, M., Ji, S. and Liu, G. (2018) Forecasting of Chinese E-Commerce Sales: An Empirical Comparison of ARIMA, Nonlinear Autoregressive Neural Network, and a Combined ARIMA-NARNN Model. Mathematical Problems in Engineering, 2018, Article ID: 6924960. https://doi.org/10.1155/2018/6924960
[22]
Huang, H. and Liu, Q. (2017) Intelligent Retail Forecasting System for New Clothing Products Considering Stock-out. Fibres & Textiles in Eastern Europe, 25, 10-16. https://doi.org/10.5604/01.3001.0010.1704
[23]
Loureiro, A.L.D., Migueis, V.L. and da Silva, L.F.M. (2018) Exploring the Use of Deep Neural Networks for Sales Forecasting in Fashion Retail. Decision Support Systems, 114, 81-93. https://doi.org/10.1016/j.dss.2018.08.010
[24]
Fallah Tehrani, A. and Ahrens, D. (2016) Enhanced Predictive Models for Purchasing in the Fashion Field by Using Kernel Machine Regression Equipped with Ordinal Logistic Regression. Journal of Retailing and Consumer Services, 32, 131-138. https://doi.org/10.1016/j.jretconser.2016.05.008
[25]
Tichy, T., Nguyen, L., Holcapek, M., Kresta, A., and Dvorácková, H. (2022) Quarterly Sales Analysis Using Linguistic Fuzzy Logic with Weather data. Expert Systems with Applications, 203, Article ID: 117345. https://doi.org/10.1016/j.eswa.2022.117345
[26]
Arora, A., Srivastava, A. and Bansal, S. (2020) Business Competitive Analysis Using Promoted Post Detection on Social Media. Journal of Retailing and Consumer Services, 54, Article ID: 101941. https://doi.org/10.1016/j.jretconser.2019.101941
[27]
Pavlyshenko, B. (2018) Using Stacking Approaches for Machine Learning Models. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, 21-25 August 2018, 255-258. https://doi.org/10.1109/DSMP.2018.8478522
[28]
van Steenbergen, R.M. and Mes, M.R.K. (2020) Forecasting Demand Profiles of New Products. Decision Support Systems, 139, Article ID: 113401. https://doi.org/10.1016/j.dss.2020.113401
[29]
Stuke, A., Rinke, P. and Todorovic, M. (2021) Efficient Hyperparameter Tuning for Kernel Ridge Regression with Bayesian Optimization. Machine Learning-Science and Technology, 2, Article ID: 035022. https://doi.org/10.1088/2632-2153/abee59
[30]
Verstraete, G., Aghezzaf, E.-H. and Desmet, B. (2019) A Data-Driven Framework for Predicting Weather Impact on High-Volume Low-Margin Retail Products. Journal of Retailing and Consumer Services, 48, 169-177. https://doi.org/10.1016/j.jretconser.2019.02.019
[31]
Orrell, D., Smith, L., Barkmeijer, J. and Palmer, T.N. (2001) Model Error in Weather Forecasting. Nonlinear Processes in Geophysics, 8, 357-371. https://doi.org/10.5194/npg-8-357-2001
