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

Shuyun Ren, Hau-Ling Chan () and Pratibha Ram
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Shuyun Ren: The Hong Kong Polytechnic University
Hau-Ling Chan: The Hong Kong Polytechnic University
Pratibha Ram: The Hong Kong Polytechnic University

Annals of Operations Research, 2017, vol. 257, issue 1, No 14, 335-355

Abstract: 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 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10479-016-2204-6

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