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The purpose of this paper is to compare the three‐dimensional (3D) shapes of a sample of 18‐30 year old Australian women, to the 3D shapes assumed by the Standards Australia (SA) garment sizing system, using the newly developed L‐statistic, to suggest methods of improving current garment sizing systems.

The L‐statistic is a new method for quantifying the lack of fit between two sets of dimensions defining 3D shapes. Using the L‐value, 28 measurements were calculated from 3D scans on 294 18‐30 year old Australian women and compared to the SA sizing system. Each participant was assigned to a best‐fit size, which was the size yielding the lowest L‐value.

The results showed that there is a significant mismatch between the real 3D shapes of young Australian women and the shapes assumed by the SA sizing system, and that there is a difference of more than two sizes when comparing reported dress sizes and best‐fit clothing sizes.

Results suggest a need for revision of the current system, or development of a new system. Revision of current clothing sizes, or development of new sizes would be optimised by using 3D body scanning technology.

This paper outlines the use of the L‐statistic, a new method of comparing the shape of Australian women to the SA sizing system. The L‐statistic can also be used to compare any individual to any sizing system and to assess the lack‐of‐fit between any sizing system and a sample of scanned bodies.

Size fit and economic efficiency are two crucial aspects that need to be considered in designing a sizing system. However, there could exist a trade-off between those aspects in order to establish a practical sizing system. The purpose of this paper is to develop a sequential hybrid method of grid and optimization to generate a practical sizing system using anthropometric data.

The proposed sequential hybrid method consisted of two sequential steps, which employs grid method and optimization method. In the initial step, the grid method creates primary grids that accommodate a designated percentage (e.g. 90%) of users with best size fit. In the subsequent step, the optimization method generated additional grids to provide acceptable fit, with minimum fit penalty scores for users unaccommodated by the primary grids. Our method was applied to the development of a sizing system for men's military jackets. The proposed method performances were evaluated in terms of accommodation percentage, size fit and number of sizing categories.

Our proposed method resulted in 26 primary grids during the initial step, which cover 90% of users. Next, we generated six additional grids during the subsequent step that provide minimum fit penalty scores for the rest (10%) users.

The main contributions of this paper are as follows: consider accommodation percentage, size fit and number of sizing categories in the design of sizing system; combine the grid and optimization methods and evaluate a sizing system for men's military jackets. The proposed method is applicable to develop optimal sizing systems for multiple-size products.

Most apparel sizing systems are based on one or two body dimensions and rely on assumptions about proportional body relationships to project other dimensions necessary to design the garment pattern. Garments from these systems will not fit a population with large variations in body proportions. Using nonlinear optimization methods and anthropometric data of US Army women three multidimensional sizing systems were derived that are designed to provide improved fit for women from the USA, a population with much variation. These systems range from an optimized linear system with a regular grade to an unconstrained optimized system with a grade break at each size. The optimized sizing systems compare favorably with D5585‐94 in their ability to accommodate variation in the population based on a mathematical test of the aggregate loss of each system. Issues related to pattern grading, size selection, and calculation of stock keeping units are discussed.

Since anthropometric dimensions vary during a lifetime, it is difficult to provide adequate sizing for all, especially growing, children. This paper aims to review children's sizing provision for girls aged 4‐8 years among four UK retailers (Adams, Debenhams, Marks & Spencer and Mothercare), an area of limited research.

Two research strategies were utilised: observational visits to retail stores (on sizing systems and environment) and face‐to‐face interviews with ten parents and five childrenswear garment technologists (on sizing of children's garments and fit issues).

UK retailers utilised various numerical size coding systems based on height, age and weight. Next, Asda George and Adams were the favourite shopping stores for children's wear. Parents were co‐shoppers with their children. While parents' key criteria for purchase were durability, fit, quality, price and washability, children's choice was based on colour, fashion and peer influence. Variation in sizing designation caused confusion. Parents have suggested varying lengths as a solution to accommodating different sizes; preferring a common system with age as key size code. Sizing inconsistency between brands and incomprehensible size codes are major factors in the creation of customer dissatisfaction with children's clothing. Providing ambient facilities for co‐shopping is vital.

The children's fashion sector is important to children, parents and retailers. Parents are co‐shoppers with their children and have key criteria for selecting to shop in a store. Marketers should be aware of core needs: sizing provision, shopping environment and the dynamics of co‐shopping. Sizing systems should be relevant to avoid dissatisfaction and confusion. More research is needed focusing on larger and other samples; target markets and psychological needs for shopping.

The area of children's shopping for clothing in the UK has a limited literature. Key issues revolve around garment sizing, garment fit, co‐shopping and consumer satisfaction. This study contributes to filling the gap in knowledge.

Reviews the sizing systems developed in several countries — the USA, Austria, England, Germany, Hungary, Japan and South Korea. A comparison of these systems shows that: the way of labelling garment sizes has varied from one sizing system to another; most of the sizing systems classified figure types by height and drop value (the difference between hip girth and bust girth measurements), and the way of classifying garments and the key dimensions of garment types in each system was slightly different. Recognizing the need for greater uniformity, the International Organization for Standardization (ISO) developed an international size labelling system. Many countries, including England, Japan, South Korea and Hungary, revised their size labelling systems by adopting the ISO system.

Past research in examining the performance of alternative lot‐sizing rules has focused on the total cost of inventory carrying cost and set‐up cost. Although this cost‐related performance measure is significant for evaluating the overall efficiency of production systems, there are other variations such as frequent rescheduling, generally referred to as system nervousness, occurring that would affect the production scheduling and subsequently the system performance. Expands the performance criteria to re‐evaluate the effectiveness of using several commonly tested lot‐sizing rules in a multi‐level MRP system under stochastic operating environments by means of a simulation study. Results indicate that the Silver‐Meal algorithm seem to perform very well under most operating environments tested. Also, the operating environments play a significant role in the relative performance of lot‐sizing rules tested.

The purpose of this paper is to analyze petite women’s body size and figure and investigate whether current petite sizing charts accurately reflect actual petit size women’s bodies. This study also categorizes petite women’s body shapes and suggests primary body measurements as a base size for each shape. The ultimate goal is to suggest fundamental body measurements for apparel companies to modify and improve their sizing.

This study used data from SizeUSA data to compare body measurements of 18-35-year-old petite women to regular women. The authors compared the results to measurement differences between petite and regular sizing charts of 14 apparel companies. Then, using the principal component analysis and cluster analysis, the authors classified petite women’s body shapes. Body measurements for each body type are contrasted with the current petite sizing charts, and then, the authors present differences as suggestions for modification and improvement of petite sizing.

Industry sizing system do not generally represent average petite size women preciously except for stature. Within the petite women, four body types were identified (top petite: 30.0 percent, bottom petite: 30.8 percent, regular petite: 23.6 percent, and plus size: 15.4 percent). Of the four groups, the ASTM D7878 generally represented the “top petite” sizing.

It is the first to analyze the industry petite sizing system utilizing population data and focus petite sizing for women aged 18-35. The authors believe this study could draw attention of the apparel industry, providing companies with ideas of how to improve their petite sizing for young women.

In shopping, for selecting the appropriate garments, people have to try on multiple garments. This problem is due to lack of a sizing system based on updated anthropometric data and the classification system that introduces the appropriate size from the sizing chart to each person. To solve this problem, as a first study in the literature, a hybrid intelligent classification model as a size recommendation expert system is proposed. The paper aims to discuss these issues.

Three stages for developing a hybrid intelligent classification system based on data clustering and probabilistic neural network (PNN) are proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference for developing a new intelligent classification system by using a PNN. At the last stage, the accuracy of the proposed model is evaluated by using the Iranian male's body type data set.

Experimental results show that the proposed model has a good accuracy and can be used as a size recommendation expert system to specify the right size for the customers. By using the proposed model and designing an interface for it, a decision support system was developed as a size recommendation expert system that was used by an apparel sales store. The results were time saving and more satisfying for the customers by selecting the appropriate apparel size for them.

In this paper, as a first study in literature, a hybrid intelligent model for developing a size recommendation expert system based on data clustering and a PNN to enable the salesperson to help the consumer in choosing the right size is proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference to develop a new intelligent classification system by using a PNN. In the last stage, the accuracy of the proposed model is evaluated by using testing data. The proposed model achieved an 87.2 percent accuracy rate that is very promising.

The purpose of this paper is to derive a new method for developing sizing systems for the mass customization of garments.

A range of recently published works has been studied. A new method is derived by following a basic statistical analysis on anthropometric data which are supported by an iterative mass customization model and introduced “satisfaction performance” indices. The derived method is applied successfully to an anthropometric data consisting of 12,810 Greek men.

With the proposed method, it is possible to control the degree of mass customization and the corresponding number of garment sizes. Under this way, a balance between the number of sizes (in other words: production cost) and the percentage satisfaction of consumers can be achieved. The proposed method consists of six subsequent tasks which are applied to the target population data for the development of mass customization models for male shirts, coats and trousers.

Future work could be focused on the development of methods for the automatic garments grading with respect to the proposed mass customization models and practise.

The methodology presented in this paper can be applied to the development of mass customization models for other categories of garments and target population.

This study aims to provide a new method for precisely sizing photovoltaic (PV) arrays for standalone, direct pumping PV Water Pumping (PVWP) systems for irrigation purposes.

The method uses historical weather data and considers daily variability in regional temperatures and rainfall, crop evapotranspiration rates and seasonality effects, all within a nonparametric bootstrapping approach to synthetically generate daily rainfall and crop irrigation needs. These needs define the required daily supply of pumped water to achieve a user-specified level of reliability, which provides the input to an intuitive approach for PV array sizing. An economic comparison of the costs for the PVWP versus a comparably powered diesel generator system is provided.

Pumping 22.8646 m³/day of water would meet the pasture crop irrigation needs on a one-acre (4046.78 m²) tract of land in South Florida, with 99.9% reliability. Given the specified assumptions, an 8.4834 m² PV array, having a peak power of 1.1877 (kW), could provide the 1.2347 (kWh/day) of hydraulic energy needed to supply this volume over a total head of 20 meters. The PVWP system is the low-cost option when diesel prices are above $0.90/liter and total installed PV array costs are fixed at $2.00/Watt peak power or total installed PV array costs are below $1.50/Watt peak power and diesel prices are fixed at $0.65/liter.

Because the approach is not dependent on the shapes of the sampling distributions for regional climate factors and can be adapted to consider different types of crops, it is highly portable and applicable for precisely determining array sizes for standalone, direct pumping PVWP systems for irrigating diverse crop types in diverse regions.