Title: Modelling intended product demand in fashion retail using IoT and AI

Authors: Chi On Chan; Henry Lau; Youqing Fan

Addresses: Department of Business Administration, Hong Kong Shue Yan University, Hong Kong, China; School of Business, Western Sydney University, Sydney, Australia ' School of Business, Western Sydney University, Sydney, Australia ' School of Business, Western Sydney University, Sydney, Australia

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.

DOI: 10.1504/IJBIS.2025.144080

International Journal of Business Information Systems, 2025 Vol.48 No.1, pp.54 - 71

Received: 12 Mar 2021
Accepted: 10 Jun 2021
Published online: 24 Jan 2025 *