In this study, a new knowledge discovery and data mining-based technique has been proposed for garment size selection. It could be split into four sequential parts principally, involving data preparation, data preprocessing, fit models setting, and size selection. Two cases of mass customization, representing the top and bottom garments respectively, were utilized to expounding the implementation of the presented approach. After data preprocessing, key body dimensions were identified using the hierarchical clustering algorithm. Next, the enumeration algorithm was utilized by listing all the possible values while computing the distance between the target population and the fit models. Afterwards, an improved K-means clustering algorithm and support vector machine (SVM) method were utilized to size selection, respectively. Eventually, the SVM-based solution was considered as the optimal solution after being evaluated by the aggregate loss of fit, number of poor fit, accommodation rate of ideal fit, and number of sizes employed. The experimental results demonstrate that the present approach is a low-cost and high-effective improvement for size selection by exploiting the potentials of the existing sizing system, without creating new sizing systems. Moreover, the proposed approach can easily be applied to any type of garment in a flexible manner.

Limited availablilty of personal protective equipment (PPE) during the current COVID-19 pandemic has led to frequent unsafe reuse of personal protective clothing by healthcare workers and the public. The application of ozone gas to sterilize PPEs for reuse has been proposed. However, the potential damage inflicted on the fabrics has not been reported in the scientific literature. A study was conducted to investigate the changes in fabric elasticity that may be associated with a two-hour exposure to ozone gas at a concentration of 17 ppm. No significant material degradation was found. The results suggest that use of ozone gas to sterilize PPEs for reuse against COVID-19 virus can be effective.

In the last few decades, there has been a surge of interest in the development of fashion business models to assist fashion companies in reducing the cost and to efficiently manage business processes. These business models are developed to manage the internal operations within the company through adopting complex formulae and algorithms to reduce the waste at each procedure. However in today’s fashion business market, global sourcing and global corporation are much more important than before. The relationship between the fashion company and its suppliers, the relationship between the fashion company and its customers, and the management of these relationships. All of them are crucial parts in the business strategy. The competition between fashion companies is no longer in company level but instead is supply chain versus supply chain. Trying to take the massive information into consideration by using traditional digital technology is not a wise decision when developing business strategy. Further thinking, the information flow through the supply chain has the same characteristics “5Vs” as big data: Volume, Velocity, Variety, Value and Veracity. Put another word: the management of information flow in supply chain is the management of big data. There is no doubt that the digital technology under big data environment will fundamentally change the whole supply chain.
The first objective of this paper is to identify the key weakness of lean and agile logistic supply chain models in literature. The second objective is to point out the technology challenges to develop the Tomorrow’s models which build an agile response upon a lean platform: How to set up virtual networks from the early designing stage to the last consuming and feedback stage? How to set up the information standardization and synchronisation process in the system? How to specify the consumer requirements in fitting effects and functional performance of garments? The last objective is to discover the digital technology under big data environment which enable the information efficiently to flow through the whole supply chain.

Line balancing problems are common in the various departments of the garment industry. Different kinds of line balancing algorithms have been applied, for many years, in the apparel industry to solve these problems. Many variables and constraints must be taken into account when designing a manufacturing facility, such as the assembly line balance. For this purpose, lots of data have been collected from actual production systems, as well as alternative ones, in order to find the best line balancing algorithm. Studying the factors affecting line balancing, to identify the bottlenecks and enhance production system performance, is of utmost importance.
Garment manufacturing firms, particularly those working on a small-lot and order basis, must respond rapidly to changes in clothing styles. For flexibility and fast response, the line manger must be aware of the current situation in his production system, in order to process orders on time. Furthermore, in order to increase productivity, it is essential for line managers to be able to understand the behavior of the production system and to generate alternative systems. Thus, the simulation model approach, developed for the garment industries, enables predictability and helps to increase total productivity.
The aim of this research is to simulate a production line producing several models at the same time, in order to find the maximum number of products in a product mix that can be manufactured, at high quality and without wasting time. Results show that it is possible to produce up to five different products simultaneously on the same line.

The human body shape is the foundation of clothing structure design, it is important to analyze body shape accurately to satisfy people’s demand for the fitness of clothing. With the improvement of people’s requirements for the fit of clothing, the classification of body type is gradually refined. As a large group consumers of clothing products, middle-aged female body shapes are different from young and older women. This paper was based on the 3D human body measurement technology. 211 females’ body data aged from 50 to 59 in central China were used as experiment samples. There are 26 body part sizes in total, including length, width and girth. Through the data analysis software SPSS, five types of body shape are obtained based on cluster analysis. Then the models of various types of middle-aged women were reconstructed, the specific characteristics of various types of body types were explained. Finally, the influence of body shape difference on the prototype version was studied by comparing the corresponding upper prototype of each body type. The research results of this paper can provide reference for the development of middle-aged women’s upper prototype.