Purpose Apparel CAD simulations use two input parameter types: objective parameters measured from source materials, such as patterns and fabrics, and subjective parameters arising from manufacturing processes, such as stitching or steam pressing. The latter rely on designer estimates to a lack of measurement protocols. The resulting subjectivity causes digital twins to deviate from physical garments. This study aims to introduce a metric to quantify this problem and prioritise parameters to improve adequacy of virtual clothing. Design/methodology/approach This study addresses this gap in the context of historical clothing by introducing a four-stage analytical framework based on a novel degree of objective control (DOC) metric, defined as the proportion of a virtual garment’s surface governed by objectively measurable parameters. Analysing 20 costumes and 320 parameter-adequacy pairs, this study correlated DOC with adequacy validated using an AI-driven computer vision workflow across three fidelity characteristics, construction, proportions and shape. Findings Results revealed statistically significant correlations, with surface parameters (e.g. Bond Strength) dominating shape accuracy (R = 0.89), seam parameters (e.g. Tension Ratio) governing structural integrity (R = 0.82) and edge parameters (e.g. Elastic Strength) influencing proportional fidelity (R = 0.76). Surface optimisation yielded 3× greater return-on-effort in adequacy gains than other parameter groups. Originality/value The insights enabled a parameter prioritisation framework for resource-efficient optimisation of CAD workflows, a standardised adequacy audit protocol compliant with FAIR data principles and the first empirical foundation for mitigating subjectivity-induced errors. This helps designers focus efforts where it matters most, saving time and resources in all digital garment domains including virtual prototyping, sustainable design and heritage preservation.
Moskvin et al. (Tue,) studied this question.