1.5.1
Background of sizing systems
The use of anthropometric databases to enhance apparel design and fit has mainly been aimed at defining new sizing systems. A sizing system divides a population into homogeneous subgroups with similar body measurements (size groups), in such a way that all individuals in a size group can wear the same garment [7, 34]. In current sizing systems, the body dimensions used to obtain the size groups are called control dimensions or key dimensions. The primary control dimension separates the set of individuals into major size groups along the anthropometric measurement that is considered the most important dimension for designing a particular garment. Next, these major size groups are divided into subgroups according to a secondary con- trol dimension that is considered the second most important dimension for the same garment (more than one secondary control variable can be used). Each subgroup is described by certain values of the control dimensions se- lected, thus a body of specific proportions, called body shape or body type, is defined. Of course, subgroups can be further split according to a tertiary control dimension, etc. Each further subdivision of the groups identifies the body shape of the size group more closely. The set of values of each control dimension to be covered in the sizing chart is called the size range along that control dimension. The percentage of the population that is accommodated by the sizing system is called the accommodation rate of the sizing system. The size range of each control dimension is divided into a set of sizes known as the size scale. The size scale depends on the increment between adjacent sizes. This increment is called the size interval, size step or size grade and can have a fixed or variable value. Once the size groups are determined, the values of certain other body dimensions needed for garment manufac- turing can be added to the sizing chart. These additional measurements are called secondary dimensions (not to be confused with secondary control di- mensions). Finally, the coding system used to identify the body dimensions for which the garment was designed is called size designation (or better still,
labeling). The combination of the size designation method and the sizing charts constitutes the sizing system, size roll or tariff system of the specified garment. In particular, a sizing system is specified with a table of numbers that details the values of the body dimensions used to define each size group. Table 1.2 summarizes the decision steps to be made when defining a sizing chart.
1. Primary and secondary control dimensions to classify the population.
2. Range of values covered by each control dimension (size range and size scale). 3. Division of the size scale of each variable into segments (size interval). 4. Number of size groups to produce.
5. Additional secondary dimensions that are relevant for creating the garment. 6. Labeling to identify the dimensions of each size group.
Table 1.2: Decisions to be made when defining a sizing system for a specified type of garment, see [7].
Most manufacturers from different apparel companies create and adjust their own size charts by trial and error using the information collected from sales studies, returned goods reports and small-scale customer surveys. Fur- ther changes to the dimensions of garments of specific sizes are made in a stepwise manner, often without adapting the size designation.
The sizing systems resulting from this process are the reason for the lack of fit of the clothes that companies offer, a large amount of unsold and re- turned garments and a less competitive business [30]. Furthermore, because each apparel company creates its own sizing system, garments from one com- pany may fit differently to those of another with the same size-label. All makes for a highly unsatisfactory and confusing clothes shopping experience for customers. In order to remedy this situation, several standardization or- ganizations have arisen, which propose a regulation of the sizing system. In 1968 the Swedish member body of the International Organization for Stan- dardization (ISO) proposed that a Technical Committee (ISO/TC 133, which was established in 1969) should be set up to create a global sizing system for clothing [221, 7]. This committee reached the conclusion that developing a unique sizing system to accommodate the world’s population would be ine- ffective because of the variability inherent to different countries or ethnic groups. However, they concentrated on deciding which the most relevant elements were for defining a sizing system. In 1991, ISO/TC 133 published a report providing the preferred control dimensions, values and intersize in-
tervals for defining a sizing system based on anthropometric data from a particular population [103, 7]. Several countries have revised their size desig- nation systems in accordance with the standards published by ISO. The Eu- ropean Committee for Standardization has developed several standards (EN 13402 Size Designation of Clothes) from the ISO standards. In this report, we will use two of those EN reports to propose the definition of an efficient stan- dard sizing system which may be used for every Spanish apparel company: the Size designation of clothes. Part 2: Primary and secondary dimensions [58] and the Size designation of clothes. Part 3: Measurements and intervals [59].
1.5.2
Background of fit models
The final evaluation of garment fit requires models to test every new design before the production phase. These models are the dress form, the human fit model and the virtual fit model. Of these three, the human fit model plays the most important role. Companies try to enhance the quality of fit by scanning their fit models and deriving dress forms from those scans [7, 193]. The fit model represents the commercial measurements established by each company to define the proportional relationships needed to achieve the company’s fit [225]. Beyond merely wearing the garment for examination, a fit model is a person who provides objective feedback about fit, movement, comfort and visual appearance of a garment in place of the consumer. A fit model therefore acts as a live mannequin.
The current practice in apparel fit analysis is based on using expert panels [8]. An expert panel is an experienced working team that judges the fit of a garment. In the apparel industry, fit analysis is tested with a live fit model. Almost every apparel company develops its own sizing system by using a different fit model which covers their whole target market [224]. This means that apparel companies only attempt to fit one body type, generating base patterns and grade rules that match the proportions of their fit model [6]. However, there might be many shapes and body types within a size and this single idealized fit model may not adequately address the differences between them [7, page 133]. Furthermore, there is little information available to help choose a fit model whose body size and shape are consistent with the body characteristics of the target market [6].
During recent years, research has been done to examine the reliability of using virtual 3D scan models instead of fit models to improve garment fit
[8, 24]. These virtual 3D models come from scanned live fit models. They can be used similarly to fit models but offer many benefits in different areas of apparel design and manufacture [157, 8]. Indeed, the tailoring procedure followed by fashion designers and manufacturers needs real individuals to be scanned to generate 3D clothes from 2D patterns [142, 217]. Consequently, a representative fit model of the target population, whether a live fit model or a 3D scan model, is critical for improving garment fit and has become an integral part of the design process. Good fit models are basic for defining an accurate sizing system.
1.5.3
Literature review and our statistical proposals
Three types of approaches can be distinguished for creating a sizing system: traditional step-wise sizing, multivariate methods and optimization methods. The main difference between the traditional approach regarding multivariate and optimization methods is that the size groups that it defines form a fixed regular pattern along each control dimension, while the other approaches de- fine size groups that are spaced randomly (without constraints) in the space defined by the key dimensions. Traditional methods use bivariate distribu- tions to define a sizing chart and cross tabulation to select the sizes gradually, covering the highest percentage of population. The size interval is set accor- ding to common practice or fit and style considerations of the designers. This approach is too simplistic. It is not possible to cover the different body types of the population because other relevant anthropometric dimensions are not considered.
More recently, more advanced mathematical methods have been develo- ped. From the statistical point of view, Principal component analysis (PCA) and clustering methods have been widely used. PCA has been used as a dimensionality reduction technique. The usual procedure consists of selecting the first two principal components that explain the bulk of the data variance and generating the bivariate distribution in which to define the sizing chart [85, 97, 138, 179]. Partitioning clustering methods, especially the k-means algorithm, have been used to classify the target population into different morphologies by using every anthropometric measurement available as an input [96, 34, 230, 155, 9]. Other alternatives combining data mining and decision trees have also been proposed [98].
The first proposal using an optimization method was put forward by Peter Tryfos in [204]. He developed an integer programming procedure to
optimize the number of sizes in such a way garment sales were maximized. An alternative to Tryfos’proposal was introduced in [147], where a nonlinear optimization technique was used to maximize the quality of fit instead of sales. More recently, a linear programming approach to divide the population into homogenous size groups has been proposed in [86].
In this PhD work, we propose several methodologies to divide the popu- lation into efficient sizes from a central case in each size. They are based on clustering, statistical shape analysis and the statistical concept of data depth. On the other hand, to the best of our knowledge, no statistical method has been developed for the purpose of defining representative fit models. A clustering methodology is developed with this goal in mind.