ABSTRACT
European Norm 13402-3 sets standards for clothes sizing designation. Depending on the kind of garment, between one to up to four anthropometric dimensions need to be considered in defining the clothing size system. According to the distribution of the anthropometric dimensions of a specific population, the quantity needed or proportion for the various clothes and garment size categories varies greatly. In order to develop a process that enables clothing distribution managers to foresee the relative proportion of each category size that is expected to be consumed, assuming a homogeneous appeal of the garments irrespective of their size, across the population, a set of considerations supported by mathematical calculations, need to be established. To this end, it must be noted that the combination of anthropometric dimensions, described by their Gaussian statistical parameters (mean and standard deviation) is mathematically feasible, as long as correlation factors between anthropometric dimensions are known for the population at hand (these may only be extracted from original data sets). The chapter proposes deploying correlation factors between anthropometric dimensions involved in the European clothes sizing standard, and presents a method for garments lot sizing for point of sale application, informed by the correlation factors, which may be retrieved from literature, and by the statistical parameters of the population (actual or inferred). The chapter demonstrates the approach presented for various clothing item types, including men shorts (sizing based on one anthropometric dimension) and women brassieres (sizing based on two anthropometric dimensions). The mathematical formulation presented and demonstrated in the chapter is systematized in examples solved by spreadsheet calculations and is intended to support the management of orders of garments by size at their point of sale considering the reduced cycle of fashion (about 3 months). The chapter also emphasizes that reporting of anthropometric data should consider not only the mean and standard deviation of individual 326dimensions as has been the common practice in the field of human factors and ergonomics, but should also be accompanied by correlation charts between the anthropometric dimensions, given reduced availability of this kind of data.
