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Annals of Operations Research

, Volume 257, Issue 1–2, pp 335–355 | Cite as

A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty

  • Shuyun Ren
  • Hau-Ling Chan
  • Pratibha Ram
S.I.: Innovative Supply Chain Optimization

Abstract

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.

Keywords

Industrial applications Uncertainty demand forecasting systems  Computational models AHP analysis Fast fashion RFID 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Business Division, Institute of Textiles and ClothingThe Hong Kong Polytechnic UniversityHung Hom, KowloonHong Kong

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