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The purpose of this paper is to present a model of researching clothing anthropometrics at the Manchester Metropolitan University in the UK (MMU model), to demonstrate steps in devising size charts by analysing raw data, to relate key aspects of size charts to raw data, and to generate debate on such methods that impinge on the disseminated knowledge in this specialised area. Although sizing is important to consumers, retailers and manufacturers, this area has received scarce attention in the literature.

The MMU model presents step‐by‐step processes in generating size charts. Data from 150 women generated descriptive statistics (mean, standard deviation, percentiles); these were utilised to devise seven sizes of a body measurements table. Correlations were used to determine relationships, resulting in size charts with a defined size range and grading increments that are relatable to utilisation by consumers, retailers and manufacturers.

A step‐by ‐step model of analysing raw data is presented. A verifiable size chart, codes, grading increments and size limits relatable to data are generated. The usefulness of size charts is therefore contextualised.

This paper discusses only one model of researching clothing anthropometrics and provides a related conceptual framework; this could be the basis for future research and debate in this area.

For competitiveness, efficient sizing is useful for marketing, especially in creating niches, targeting customers and facilitating consumer satisfaction.

The MMU model provides an initial conceptual framework at one institution, a benchmark for similar practice in academia and industry and subsequent debate in literature.

The purpose of this paper is to identify common issues among commercial body size charts, and to propose a sizing improvement methodology without changing the number of sizes in the range. One goal is to equalize the number of people accommodated by each size within the range, and to propose a way to evaluate the effectiveness of the improvement.

The proposed method consists of three phases: (Phase I) identify target population; (Phase II) analysis of existing size charts; and (Phase III) improvement of the initial size chart. Phase III is the key process, which includes repeated manipulation of intersize intervals of the three primary measurements (chest, waist and hip) for improved consistency of overall and interior accommodate rates among the three measurement categories. A program was developed in RStudio® to generate trials and side-by-side bar plots for visualization of the differences in accommodate rates.

The main issue in commercial body size charts observed is the inconsistency of the interior accommodation rates among measurement categories. Some other issues include: lack of important measurements, failure to provide ranges and gaps between measurement ranges of adjacent sizes.

This paper proposed a complete work flow to improve body size charts to fix the common issues. The method integrates historic size information and new anthropometric information extracted from a national-scale sizing database (e.g. SizeUSA). The study also brought association of the secondary body measurements with primary measurements without using linear regression. Hence, information from body size charts can be more efficiently used in acquiring other size information.

A survey of 100 young women's body measurements was undertaken during 1992/93. The findings are the basis of Part 3, which aims to explain how size charts are developed for garments; to evaluate the measuring equipment used and to compare the size chart body measurements with those proportionally derived by traditional formulae. A size chart is the artificial dividing of a range of measurements which are concise and consistent. There are different types of size charts. Some are of body measurement for specific proportion and shape. Others are for garments including ease allowances which vary according to the garment style and type of fabric. Size charts can be developed in three stages commencing with the raw survey data, which is then rounded to the nearest 1.0 cm or 0.5 cm and finally ease allowance is added for the finished garment. During the survey some measurements were repeated using different measuring equipment so that a comparison could be made to select the most suitable for pattern construction. The use of the anthropometer is limited as it can only take linear measurements. However, it is helpful when analysing body proportion, whereas the tape measures attached to the harness and a metal tape measure can record the contour surface of the body, which is more appropriate for clothing. The adjustable square and angle were a little difficult to position correctly but were useful to check the formulae used for pattern construction. A comparison is made between the survey body measurements and traditional formula to derive body measurements which are difficult to take. The dividing of the height by eight heads is useful for length proportions. The derived neck shape and survey measurements were comparable. Head measurements suitable for hoods were similar for all bust and neck sizes. Only the height showed any progression in size. This concludes the three articles which explain the taking of body measurements, methods of analysing the data and applying it to clothing pattern construction. It is hoped that this will aid those in industry and education who wish to undertake research and to develop new technology.

A computer system for made‐to‐measure pattern production should have the capability of determining default measurements for sets of customer measurements input to the system where one or more of these measurements are missing. This paper recommends the use of default formulae rather than mathematical interpolation of size charts. These default formulae, when applied to a given size chart set, enable measurements to be determined efficiently over wide ranging customer sizes in both stature and girth. The specific default formulae for the German DOB charts are derived for Regular and Outsize charts and also for the full range of Height categories and Bust to Hip relationships, so that all sizes and shapes of customers are catered for. Default formulae have been applied in the MicroFit made‐to‐measure system from Garment Micro Systems and also implemented on this system for checking the validity of measurements entered into the system for each individual customer.

Sizing system is a fundamental topic in garment fitting. The purpose of this study was to assess the fit of existing police uniforms (shirt, jacket, overcoat and trousers) and develop a sizing system for upper and lower body uniforms of Amhara policemen in Ethiopia.

In total, 35 body dimensions of 889 policemen were taken through a manual anthropometric survey following the procedures in ISO 8559:1989 after each subject was interviewed on issues related to garment fit. The anthropometric data were pre-processed, key body dimensions were identified by principal components analysis and body types were clustered by the agglomerative hierarchical clustering algorithm and verified by the XGBoost classifier in a Python programming environment. The developed size charts were validated statistically using aggregate loss and accommodation rate.

About 44% of the subjects encountered fit problems every time they own new readymade uniforms. Lengths and side seams of shirts, and lengths and waist girths of trousers are the most frequently altered garment sites. Analysis of the anthropometric measurements resulted in 13 and 15 sizes for the upper and lower bodies, respectively. Moreover, the comparison of the developed upper garment size chart with the existing size chart for a shirt showed a considerable difference. This indicates that inappropriate size charts create fit problems.

The study considers the analysis of fit problems and sizing system development in a less researched country. Moreover, the proposed data mining procedure and its application for size chart development is unique and workable.

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

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.

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.

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

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.

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

To obtain sizing systems a specialist knowledge is required to analyse statistically body measurements from surveys. Control measurements, size ranges, body proportions and size intervals have to be calculated. A survey of 100 young women was undertaken in 1993/93 at Manchester Metropolitan University and 10 body measurements were taken. This is used to illustrate the statistical analysis of body measurements, the formulation of sizing systems and body measurement tables. A review of previous surveys and their methods of analysis was undertaken. The main control measurements of height, bust, waist and hips denoting the size of the wearer were obtained by correlation. Size ranges and intervals were obtained by normalising the data and comparing the sizes young women bought and previous size charts. Sizing systems for five sizes 8 to 16 were suggested for three heights, short, medium and tall and bust fittings medium, small and very small. This retained the same proportion for the five sizes within each of the nine systems. A further system of changing proportion in girth measurements was developed from the survey of young women based on percentiles and bust fittings. The neck girth which did not correlate strongly with any measurement was analysed separately. Two examples of body measurement tables covering 30 measurements were formulated to illustrate the procedure. Analysing body measurements statistically is problematic especially in small surveys. It is hoped the suggested guidelines will clarify this area. The coding of sizes is still not uniform. A comparison was made with previous tables. It was concluded that the body proportion had changed and the young women were taller and broader in the waist and hips. Part 3 of this study will cover comparing the problems of taking accurate body measurements with different equipment, formulating size charts for different garments and fabrics and relating these to different systems of pattern construction, and finally, testing prototype garments for size and fit.

The purpose of this paper is to introduce a new design of the X¯ chart to catch smaller shifts in the process average as well as to maintain the simplicity like the Shewhart X¯ chart so that it may be applied at shopfloor level.

In this paper, a new X¯ chart with two strategies is proposed which can overcome the limitations of Shewhart, CUSUM and EWMA charts. The Shewhart chart uses only two control limits to arrive at a decision to accept the Null Hypothesis (H₀) or Alternative Hypothesis (H₁), but in the new X¯ chart, two more limits at “K” times sample standard deviation on both sides from center line have been introduced. These limits are termed warning limits. The first strategy is based on chi‐square distribution (CSQ), while the second strategy is based on the average of sample means (ASM).

The proposed X¯ chart with “strategy ASM” shows lower average run length (ARL) values than ARLs of variable parameter (VP) X¯ chart for most of the cases. The VP chart shows little better performance than the new chart; but at large sample sizes (n) of 12 and 16. The VSS X¯ chart also shows lower ARLs but at very large sample size, which should not be used because, as far as possible, samples should be taken from a lot produced under identical conditions. The inherent feature of the new chart is its simplicity, so that it can be used without difficulty at shopfloor level as it uses only a fixed sample size and fixed sampling interval but it is very difficult to set the various chart parameters in VP and VSS X¯ charts.

A lot of effort has been expended to develop the new strategies for monitoring the process mean. Various assumptions and factors affecting the performance of the X¯ chart have been identified and taken into account. In the proposed design, the observations have been assumed independent of one another but the observations may also be assumed to be auto‐correlated with previous observations and performance of the proposed chart may be studied.

The research findings could be applied to various manufacturing and service industries as it is more effective than the Shewhart chart and simpler than the VP, VSS and CUSUM charts.

The purpose of this paper is to counter autocorrelation by designing the chart, using warning limits. Various optimal schemes of modified chart are proposed for various sample sizes (n) at levels of correlation (Φ) of 0.00, 0.475 and 0.95. These optimal schemes of modified chart are compared with the double sampling (DS) chart, suggested by Costa and Claro (2008).

The performance of the chart is measured in terms of the average run length (ARL) that is the average number of samples before getting an out-of-control signal. Ultimately, due to the effect of autocorrelation among the data, the performance of the chart is suspected. The ARLs at various sets of parameters of the chart are computed by simulation, using MATLAB. The suggested optimal schemes are simpler schemes with limited number of parameters and smaller sample size (n=4) and this simplicity makes them very helpful in quality control.

The suggested optimal schemes of modified chart are compared with the DS chart, suggested by Costa and Claro (2008). It is concluded that the modified chart outperforms the DS chart at various levels of correlation (Φ) and shifts in the process mean. The simplicity in the design of modified chart, makes it versatile for many industries.

Both the schemes are optimized by assuming the normal distribution. But this assumption may also be relaxed to design theses schemes for autocorrelated data. The optimal schemes for chart can be developed for variable sample size and for variable sampling intervals. The optimal schemes can also be explored for cumulative sum and exponentially weighted moving average charts.

The correlation among the process outputs of any industry can be find out and corresponding to that level of correlation the suggested control chart parameters can be applied. The understandable and robust design of modified chart makes it usable for industrial quality control.

The rejection level of products in the industries can be reduced by designing the better control chart schemes which will also reduce the loss to the society, as suggested by Taguchi (1985).

Although it is the extension of previous work but it can be applied to various manufacturing industries as well as service industries, where the data are positively correlated and normally distributed.

The aim of this research was to develop a conceptual framework of the role of garment sizing in the marketing strategy of clothing manufacturing firms in Kenya. From the review of literature, salient components of marketing principles and practices that had been analysed by preceding authors provided the theoretical basis for this qualitative study. The procedures for the in‐depth interviews with 13 clothing manufacturers and ten retailers are explained. Based on grounded theory techniques, the data from the interviews were analysed, resulting in a conceptual framework for determining marketing strategy in this clothing market. Results showed that these firms are still market production oriented, and may face severe competition from inward‐bound competitors who are marketing oriented. This exploratory study contextualised the role of size charts in the marketing strategy of clothing firms.