Today, it is possible to offer customers a garment to order according to their desires and body figure. The new, more automatic, equipment and software make the process faster and possible to realise. However it is important to have the basic knowledge about how to adapt the patterns according to the many variations of figures that exists. Pattern construction for unique figures is covered in this study. The report begins with a background to the problem concerning finding suitable garments, followed by earlier initiatives. The main body of the study contains three main sections: body figure registration – including measurements, single pattern adaptations, and individual patterns.

Purpose

The purpose of this paper is to use a systematic model for detecting misfit between the garment and the target group.

Design/methodology/approach

Using an empirical–analytical methodology, the systematic model was tested. The input data were run through the model to generate the output data, which were analysed, including basic statistics. The purpose of the analysis was to detect misfit and improve the garment measurement chart. This procedure was repeated until a clear result was reached.

Findings

The result of this study is an optimised garment measurement chart, which considers the garment’s ease, different sizes/proportions in relation to a target group. The results show that it is possible to use a systematic model to define the shortcomings of a garment´s range of sizes and proportions.

Research limitations/implications

Further studies are needed to verify the results of the theoretical garment fit and their values in relation to real garment fit.

Practical implications

If the systematic model is implemented to improve the theoretical garment fit, this may have effects on the available garment sizes and its proportions, resulting in increased theoretical garment fit for the target group.

Originality/value

The paper presents a systematic model for detecting and eliminating theoretical fitting; the model includes both garment ease allowance and defined points of misfit.

Close to 30% of garments bought online are returned, often due to issues of fit. These issues often relate to size selection, which is challenging without physically trying on a garment. Alternative methods need to be explored to select the best size in lieu of physically trying apparel on. To address this issue, we compare the size selections based on primary measurements and size charts, virtual garments, and real garments. A cross-sectional quantitative survey was carried out in an experimental setting. The participants (36, predominantly White females, aged 21–56) made size selections and evaluations based on virtual and real blouses and trousers. Selecting the size based on virtual garments is more accurate than size selection based on primary measurements and size charts, scoring 57% and 42%, respectively. Further research should be used to improve the virtual fitting room, with benefits such as fewer returns and more satisfied customers.