Modelling intended product demand in fashion retail using IoT and AI

Chi On Chan, Henry Lau and Youqing Fan

International Journal of Business Information Systems, 2025, vol. 48, issue 1, 54-71

Abstract: The fashion industry operates in a fast moving and dynamic environment which requires fashion designers to respond to market trends quickly and continuously. This study investigates potential for application of internet of things (IoT) and artificial intelligence (AI) in fashion retail. The customer product interaction that takes place in retail stores reflects hidden preferences. As information now spreads faster than ever before, sharing product information or product evaluation by different groups can be reported in no time, which can help estimate real demand of products. But detecting these changes in real time has been difficult in the past. However, this paper analyses data collected by using IoT through the application of adaptive neuro-fuzzy inference system to learn demand changes, so as to know the intended product demand in real time.

Keywords: internet of things; IoT; fashion retail; customer product interaction; CPI; adaptive neuro-fuzzy inference system; ANFIS; artificial intelligence; AI. (search for similar items in EconPapers)
Date: 2025
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