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NTIS 바로가기한국의류학회지 = Journal of the Korean Society of Clothing and Textiles, v.44 no.6, 2020년, pp.1053 - 1069
채진미 (한성대학교 글로벌패션산업학부) , 김은희 (한국 오라클)
This study examined the sales pattern relationship with respect to product attributes to propose sales forecasting for fashion products. We analyzed 537 SKU sales data of T-shirts in the domestic sports brand using SAS program. The sales pattern of fashion products fluctuated and were influenced by ...
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/IJRDM-07-2015-0101
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
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
Bertrand, J.-L., Brusset, X., & Fortin, M. (2015). Assessing and hedging the cost of unseasonal weather: Case of the apparel sector. European Journal of Operational Research, 244(1), 261-276. doi:10.1016/j.ejor.2015.01.012
Choi, T.-M., Hui, C.-L., Liu, N., Ng, S.-F., & Yu, Y. (2014). Fast fashion sales forecasting with limited data and time. Decision Support Systems, 59, 84-92. doi:10.1016/j.dss.2013.10.008
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
Fam, K.-S., Merrilees, B., Richard, J. E., Jozsa, L., Li, Y., & Krisjanous, J. (2011). In-store marketing: a strategic perspective. Asia Pacific Journal of Marketing and Logistics, 23(2), 165-176. doi:10.1108/13555851111120470
Fiordaliso, A. (1998). A nonlinear forecasts combination method based on Takagi-Sugeno fuzzy systems. International Journal of Forecasting, 14(3), 367-379. doi:10.1016/S0169-2070(98)00010-7
Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer.
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
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.
Kim, D. H., Park, H. J., Choi, M. G., Kim, W., & Choi, D. G. (2019). 판매 데이터를 통한 Recurrent Neural Networks (RNN) 기반 리테일 매장 수요 예측 및 재고 관리 모델 개발 연구 [A study of retail store demand forecast and inventory management model development based on Recurrent Neural Networks (RNN) through sales data]. Proceedings of Korean Institute of Industrial Engineers, the Korean Operations Research and Management Science Society, and the Korea Society for Simulation, Spring Joint Conference, Korea, 2039-2051.
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
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.
Kim, S.-H., & Rhee, E.-Y. (2000). 의류상품 소비자의 판매촉진 반응유형과 쇼핑성향 [Sales promotion response and shopping orientation of apparel consumers]. Distribution Business Review, 5(1), 33-46.
Kotler, P., & Keller, K. L. (2009). Marketing management (13th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
Lee, E. J. (2008). A comparative analysis of time series forecasting models for fashion products (Unpublished master's thesis). Pukyong National University, Busan.
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
Lee, Y. (2012). A development study for fashion market forecasting models (Unpublished doctoral dissertation). Ewha Womans University, Seoul.
Lee, Y. J. (2014). An educational proposal to enhance analytic ability of the managers of Korean apparel industry: Focusing on market demand forecast. The Journal of Business Education, 28(6), 187-216.
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. doi:10.1155/2013/738675
Loureiro, A. L. D., Migueis, V. L., & 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. doi:10.1016/j.dss.2018.08.010
Makridakis, S. G., & Wheelwright, S. C. (1978). Forecasting: Methods and applications. Santa Barbara, CA: Wiley.
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
Muller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: A guide for data scientists. Sebastopol, CA: O'Reilly Media, Inc.
Nenni, M. E., Giustiniano, L., & Pirolo, L. (2013). Demand forecasting in the fashion industry: A review. International Journal of Engineering Business Management, 5. doi:10.5772/56840
Papalexopoulos, A. D., & Hesterberg, T. C. (1990). A regression-based approach to short-term system load forecasting. IEEE Transactions on Power Systems, 5(4), 1535-1547. doi:10.1109/59.99410
Park, J. H., Park, Y. M., & Lee, K. Y. (1991). Composite modeling for adaptive short-term load forecasting. IEEE Transactions on Power Systems, 6(2), 450-457. doi:10.1109/59.76686
Parsons, A. G. (2001). The association between daily weather and daily shopping patterns. Australasian Marketing Journal (AMJ), 9(2), 78-84. doi:10.1016/S1441-3582(01)70177-2
Sung, h.-y. (2006). Study of the price elasticity about the merchandises on selling in supermarkets (Unpublished master's thesis). Chung-Ang University, Seoul.
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.
Thomassey, S. (2014). Sales forecasting in apparel and fashion industry: A review. In T.-M. Choi, C.-L. Hui, & Y. Yu (Eds.), Intelligent fashion forecasting systems: Models and applications (pp. 9-27). Heidelberg and New York: Springer.
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
Thomassey, S., Happiette, M., & Castelain, J. M. (2005). A short and mean-term automatic forecasting system-application to textile logistics. European Journal of Operational Research, 161(1), 275-284. doi:10.1016/j.ejor.2002.09.001
Timm, N. H. (2002). Applied multivariate analysis. New York, NY: Springer.
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
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.
Yoo, H., & Pimmel, R. L. (1999). Short term load forecasting using a self-supervised adaptive neural network. IEEE Transactions on Power Systems, 14(2), 779-784. doi:10.1109/59.761912
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