Enhancing Apparel Size and Fit Prediction for e-Commerce: A Mixed-Methods Approach with 3D Body Scanning

To improve virtual fit and automated tailoring solutions that deliver both style and function to consumers, a quantification of style and how this in relation to a person’s unique anthropometry is required, building on previous 3D Body Scanning and ergonomic research by the supervisors (Gill et al., 2014, 2016, 2017; Gill and Parker, 2017; Januszkiewicz et al., 2017; Parker, 2016; Parker et al., 2017; Sadat Muhammad Sayem et al., 2018).

The global fashion industry is worth over $212 billion (McCarthy et al., 2017), of which 12% of UK e-Commerce is derived from textiles, clothing and footwear stores (Mintel, 2017). This shows great uncapitalised potential in the fashion e-Commerce sector. While consumers are demanding improved returns and delivery options from retailers (Goody, 2016), the resulting high level of garment returns is one of the industry’s biggest problems (Dover, 2016). As a consequence of this, poor fit in garments has a direct link to industry profitability. Additionally, poor garment fit has been identified driving a reluctance to make online purchases and costly returns for the retailers (Goody, 2017).

The impact of this project shall add crucial theory to the automated bespoke tailoring developments occurring within the Apparel Design Engineering research cluster of The University of Manchester. This is a keystone of virtual fit and virtual tailoring solutions being developed for a disruptive technology in the fashion retail industry by research in the Apparel Design Engineering cluster.

The ideal candidate will have, or expect to achieve, a First Class or 2.1 Honours degree in Design (Industrial or Human Factors), Psychology, Marketing or a closely related subject.