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http://dx.doi.org/10.5805/SFTI.2021.23.4.480

Sales Forecasting Model for Apparel Products Using Machine Learning Technique - A Case Study on Forecasting Outerwear Items -  

Chae, Jin Mie (School of Global Fashion Industry, Hansung University)
Kim, Eun Hie (Oracle Korea)
Publication Information
Fashion & Textile Research Journal / v.23, no.4, 2021 , pp. 480-490 More about this Journal
Abstract
Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.
Keywords
sales forecasting; k-means clustering; decision tree classifier; sales pattern; outerwear item attributes;
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1 Lee, E. J. (2008). A comparative analysis of time series forecasting models for fashion products. Unpublished master's thesis, Pukyong National University, Busan.
2 Lee, K. C., & Oh, S. B. (1996). An intelligent approach to time series identification by a neural network-driven decision tree classifier. Decision Support Systems, 17(3), 183-197. doi:10.1016/0167-9236(95)00031-3   DOI
3 Lee, S., Kang, J. H., Lee, H., Joo, T. W., Oh, S., Park, S., & Kim, S. B. (2014). Prediction of product life cycle using data mining algorithms - A case study of clothing industry. Journal of the Korean Institute of Industrial Engineers, 40(3), 291-298. doi:10.7232/JKIIE.2014.40.3.291   DOI
4 Lee, Y. (2012). A development study for fashion market forecasting models. Unpublished doctoral dissertation, Ewha Womans University, Seoul.
5 Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain - The bullwhip effect. Management Science, 43(4), 546-558. doi:10.1287/mnsc.43.4.546   DOI
6 Makridakis, S. G., & Wheelwright, S. C. (1978). Forecasting - Methods and applications. Santa Barbara, CA: Wiley.
7 Mostard, J., Teunter, R., & de Koster, R. (2011). Forecasting demand for single-period products - A case study in the apparel industry. European Journal of Operational Research, 211(1), 139-147. doi: 10.1016/j.ejor.2010.11.001   DOI
8 Kim, G. S., Kim, J. H., Kim, H. T., Suh, C. J., Erh, Y. Y., Yoo, S. J., Yoo, H. J., & Hwang B. J. (2011). Production & operations management system. Paju: Bobmunsa.
9 Thomassey, S., & Happiette, M. (2007). A neural clustering and classification system for sales forecasting of new apparel items. Applied Soft Computing, 7(4), 1177-1187. doi:10.1016/j.asoc.2006.01.005   DOI
10 Thomassey, S., Happiette, M., & Castelain, J.-M. (2003). Mean-term textile sales forecasting using families and items classification. Studies in Informatics and Control, 12(1), 41-52.
11 Tsujino, K., & Nishida, S. (1995). Implementation and refinement of decision trees using neural networks for hybrid knowledge acquisition. Artificial Intelligence in Engineering, 9(4), 265-276. doi:10.1016/0954-1810(95)00005-4   DOI
12 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(738675), 1-9. doi: 10.1155/2013/738675   DOI
13 Koo, H., & Min, D. (2013). Forecasting renewable energy using delphi survey and the economic evaluation of long-term generation mix. Journal of the Korean Institute of Industrial Engineers, 39(3), 183-191. doi:10.7232/JKIIE.2013.39.3.183   DOI
14 Sung, H. Y. (2006). Study of the price elasticity about the merchandises on selling in supermarkets. Unpublished master's thesis, ChungAng University, Seoul.
15 Vashishtha, R. K., Burman, V., Kumar, R., Sethuraman, S., Sekar, A. R., & Ramanan, S. (2020, August). Product age based demand forecast model for fashion retail. Oral presented at the fifth international workshop on fashion and KDD. San Diego, CA.
16 Thomassey, S., & Fiordaliso, A. (2006). A hybrid sales forecasting system based on clustering and decision trees. Decision Support Systems, 42(1), 408-421. doi:10.1016/j.dss.2005.01.008   DOI
17 Nam, S. M. (2006). A study on the anomaly in retailing market - Focused on the day of the week effect of sales volume in fashion apparel products retail store. Journal of Global Academy of Marketing Science, 16(1), 117-141. doi:10.1080/12297119.2006.9707360   DOI
18 Ni, Y., & Fan, F. (2011). A two-stage dynamic sales forecasting model for the fashion retail. Expert Systems with Applications, 38(3), 1529-1536. doi:10.1016/j.eswa.2010.07.065   DOI
19 Saaksvuori, A., & Immonen, A. (2005). Product lifecycle management. New York: Springer
20 Sun, Z. L., Choi, T. M., Au, K. F., & Yu, Y. (2008). Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems, 46(1), 411-419. doi:10.1016/j.dss.2008.07.009   DOI
21 Sztandera, L. M., Frank, C., & Vemulapali, B. (2004). Predicting women's apparel sales by soft computing. In L. Rutkowski, J. H. Siekmann, R. Tadeusiewicz, & L. A. Zadeh (Eds.), Artificial Intelligence and Soft Computing - ICAISC 2004: 7th International Conference, Zakopane, Poland, June 7-11, 2004. Proceedings (pp. 1193-1198). Berlin and Heidelberg: Springer-Verlag Berlin Heidelberg.
22 Vroman, P., Happiette, M., & Rabenasolo, B. (1998). Fuzzy adaptation of the holt-winter model for textile sales-forecasting. Journal of the Textile Institute, 89(1), 78-89. doi:10.1080/00405009808658668   DOI
23 Vroman, P., Happiette, M., & Vasseur, C. (2001). A hybrid neural model for mean-term sales forecasting of textile items. Studies in Informatics and Control, 10(2), 149-168.
24 Witten, I. H., & Frank, E. (1999). Data mining - Practical machine learning tools and techniques with Java implementations. San Francisco, CA: Morgan Kaufmann.
25 Yelland, P. M., & Dong, X. (2014). Forecasting demand for fashion goods - A hierarchical Bayesian approach. In T. M. Choi, C. L. Hui, & Y. Yu (Eds.), Intelligent fashion forecasting systems - Models and applications (pp. 71-94). Heidelberg and New York: Springer.
26 Yesil, E., Kaya, M., & Siradag, S. (2012). Fuzzy forecast combiner design for fast fashion demand forecasting. Proceedings of 2012 International Symposium on Innovations in Intelligent Systems and Applications (pp. 1-5). Trabzon, Turkey: IEEE. doi:10.1109/INISTA.2012.6247034   DOI
27 Yu, Y., Choi, T. M., & Hui, C. L. (2012). An intelligent quick prediction algorithm with applications in industrial control and loading problems. IEEE Transactions on Automation Science and Engineering, 9(2), 276-287. doi:10.1109/TASE.2011.2173800   DOI
28 Muller, A. C., & Guido, S. (2016). Introduction to machine learning with Python - A guide for data scientists. Sebastopol, CA: O'Reilly Media, Inc.
29 Craparotta, G., Thomassey, S., & Biolatti, A. (2019). A siamese neural network application for sales forecasting of new fashion products using heterogeneous data. International Journal of Computational Intelligence Systems, 12(2), 1537-1546. doi:10.2991/ijcis.d.191122.002   DOI
30 Aksoy, A., Ozturk, N., & Sucky, E. (2012). A decision support system for demand forecasting in the clothing industry. International Journal of Clothing Science and Technology, 24(4), 221-236. doi:10.1108/09556221211232829   DOI
31 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. doi:10.1016/j.ijpe.2007.06.013   DOI
32 Bahng, Y., & Kincade, D. H. (2012). The relationship between temperature and sales - Sales data analysis of a retailer of branded women's business wear. International Journal of Retail & Distribution Management, 40(6), 410-426. doi:10.1108/09590551211230232   DOI
33 Bhavsar, P., Safro, I., Bouaynaya, N., Polikar, R., & Dera, D. (2017). Machine learning in transportation data analytics. In M. Chowdhury, A. Apon, & K. Dey (Eds.), Data analytics for intelligent transportation systems (pp. 283-307). Amsterdam: Elsevier.
34 Choi, T. M., Hui, C. L., Ng, S. F., & Yu, Y. (2012). Color trend forecasting of fashionable products with very few historical data. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1003-1010. doi:10.1109/TSMCC.2011.2176725   DOI
35 Curram, S. P., & Mingers, J. (1994). Neural networks, decision tree induction and discriminant analysis - An empirical comparison. Journal of the Operational Research Society, 45(4), 440-450. doi:10.1057/jors.1994.62   DOI
36 Hui, C. L., Lau, T. W., Ng, S. F., & Chan, C. C. (2005). Learning-based fuzzy colour prediction system for more effective apparel design. International Journal of Clothing Science and Technology, 17(5), 335-348. doi:10.1108/09556220510616192   DOI
37 Frank, C., Garg, A., Sztandera, L., & Raheja, A. (2003). Forecasting women's apparel sales using mathematical modeling. International Journal of Clothing Science and Technology, 15(2), 107-125. doi: 10.1108/09556220310470097   DOI
38 Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning - Data mining, inference, and prediction. New York, NY: Springer.
39 Arunraj, N. S., & Ahrens, D. (2016). Estimation of non-catastrophic weather impacts for retail industry. International Journal of Retail & Distribution Management, 44(7), 731-753. doi:10.1108/IJRDM07-2015-0101   DOI
40 Hwangbo, H., Kim, E. H., & Chae, J. M. (2017). The influences of meteorological factors, discount rate, and weekend effect on the sales volume of apparel products. Fashion & Textile Research Journal, 19(4), 434-447. doi:10.5805/SFTI.2017.19.4.434   DOI
41 Jang, E. Y., & Lee, S. J. (2002). The effects of meteorological factors on sales of apparel products - Focused on apparel sales in the department store. Journal of the Korean Society of Costume, 52(2), 139-150.
42 Jang, E. Y., & Lim, B. H. (2003). An exploratory study on the effect of weather factors on sales of fashion apparel products in department stores. Journal of Global Academy of Marketing Science, 12(1), 121-134. doi:10.1080/12297119.2003.9707207   DOI
43 Kim, J. J. (2009). Development of the sales forecast models of fashion products - Focusing on the case of a development stores. Unpublished master's thesis, Hanyang University, Seoul.
44 Joo, Y. S., & Cho, G. Y. (2016). Outlier detection and treatment in industrial sampling survey. Journal of the Korean Data & Information Science Society, 27(1), 131-142. doi:10.7465/jkdi.2016.27.1.131   DOI
45 Kim, J., & Hwangbo, H. (2017). Online and offline price elasticities of demand: Evidence from the apparel industry. The E-Business Studies, 18(5), 51-65. doi:10.20462/tebs.2017.10.18.5.51   DOI