A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty
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Fast fashion is a timely, influential and well observed business strategy in the fashion retail industry. An effective fast fashion supply chain relies on quick and competent forecasts of highly volatile demand that involves multiple stock keeping units. However, there are multiple sources of uncertainty, such as market situation and rapid changes of the fashion trends, which makes demand forecasting more challenging. Therefore, it is crucial for the fast fashion companies to carefully select the right forecasting models to thrive and to succeed in this ever changing business environment. In this study, we first review a selected set of computational models which can be applied for fast fashion demand forecasting. We then perform a real sale data based computation analysis and discuss the strengths and weaknesses of these versatile models. Finally, we conduct a survey to learn about the perceived importance of different demand forecasting systems’ features from the fashion industry. Finally, we rank the fast fashion demand forecasting systems using the AHP analysis and supplement with important insights on the preferences on the demand forecasting systems of different groups of fashion industry experts and supply chain practitioners.
KeywordsIndustrial applications Uncertainty demand forecasting systems Computational models AHP analysis Fast fashion RFID
- Box, G. E., Jenkins, G. M., & Reinsel, G. C. (2011). Time series analysis: Forecasting and control. New York: Wiley.Google Scholar
- Chan, H. L. (2016). Using radio frequency identification (RFID) technologies to improve decision-making in apparel supply chains. In T. M. Choi (Ed.), Information systems for the fashion and apparel industry. Amsterdam: Elsevier.Google Scholar
- Choi, T. M. (2013a). Fast fashion systems: Theories and applications. Boca Raton: CRC Press.Google Scholar
- Choi, T. M. (2013c). Multi-period risk minimization purchasing models for fashion products with interest rate, budget, and profit target considerations. Annals of Operations Research, 237(1), 77–98.Google Scholar
- Choi, T. M., Hui, C. L., & Yu, Y. (2011). Intelligent time series fast forecasting for fashion sales: A research agenda. In 2011 IEEE international conference on machine learning and cybernetics (ICMLC) (Vol. 3, pp. 1010–1014).Google Scholar
- Deng, J. L. (1989). Introduction to grey system theory. The Journal of Grey System, 1(1), 1–24.Google Scholar
- Ghemawat, P., Nueno, J. L., & Dailey, M. (2003). ZARA: Fast fashion (pp. 1–35). Boston, MA: Harvard Business School.Google Scholar
- Ho, D. K. Y., & Choi, T. M. (2014). Collaborative planning forecasting replenishment schemes in apparel supply chain systems: Cases and research opportunities. In T. M. Choi, C. L. Hui, & Y. Yong (Eds.), Intelligent fashion forecasting systems: Models and applications (pp. 29–40). Berlin: Springer.CrossRefGoogle Scholar
- Li, X., Kong, F., Liu, Y., & Qin, Y. (2011). Applying GM (1, 1) model in China’s apparel export forecasting. In 2011 IEEE fourth international symposium on computational intelligence and design (ISCID) (Vol. 2, pp. 245–247).Google Scholar
- Li, W., & Xie, H. (2014). Geometrical variable weights buffer GM (1, 1) model and its application in forecasting of China’s energy consumption. Journal of Applied Mathematics, 2014, Article ID 131432.Google Scholar
- 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, Article ID 738675.Google Scholar
- Nenni, M. E., Giustiniano, L., & Pirolo, L. (2013). Demand forecasting in the fashion industry: A review. International Journal of Engineering Business Management, 5(37), 1–6.Google Scholar
- Saaty, T. L. (1994). Fundamentals of decision making and priority theory with the analytic hierarchy process. Pittsburgh: RWS Publications.Google Scholar
- Serel, D. A. (2013). Flexible procurement models for fast fashion retailers. In T. M. Choi (Ed.), Fast fashion systems: Theories and applications (pp. 59–75). Boca Raton: CRC Press.Google Scholar
- Wang, Z. X. (2014). Nonlinear grey prediction model with convolution integral NGMC and its application to the forecasting of China’s industrial emissions. Journal of Applied Mathematics, 2014, Article ID 580161.Google Scholar
- Wang, K., Gou, Q., Yang, L., & Shan, S. (2013). Coordination of a fast fashion supply chain with profit-loss sharing contract. Fast Fashion Systems: Theories and Applications, 4, 77–92.Google Scholar
- Xue, W., Zuo, J., & Xu, X. (2015). Analysis of market competition and information asymmetry on selling strategies. Annals of Operations Research. doi: 10.1007/s10479-015-1809-5.
- Yang, D., Xiao, T., Choi, T. M., & Cheng, T. C. E. (2015). Optimal reservation pricing strategy for a fashion supply chain with forecast update and asymmetric cost information. International Journal of Production Research. doi: 10.1080/00207543.2014.998789.