Following the development of the Industrial Revolution 4.0, many new types of systems are being designed, introduced, or attempted, even in almost every traditional industry. The clothing industry is no exception. The use of continuously developing production equipment and Information and Communication Technology (ICT) has a single objective, providing a customized service to all customers. Thus, in this study, the primary research task was to identify ill-balanced aspects or disadvantages of the services previously analyzed to construct a more complete online customized service. This was accomplished by analyzing an automated Computer-Aided Design (CAD) output file containing customer requirements regarding individual clothing items. The secondary research task was to plan and design a clothing manufacturing process to which a one-person one-item mass production system has been applied to achieve a customized service. As a result, for the primary research task, the customers’ requirements for each dress were reflected in attributes, such as color, pattern, or size, and it was possible to obtain an automated CAD output file for each element. Such CAD output files can be used in the production process directly. To find the possibility of upgrading the existing dressmaking process and implement the one-person one-item system, the entire manufacturing process was simulated for the test.

Research was conducted in this study to design data-based size recommendation and size coding systems specifically for online shopping malls, expecting to lighten the burden of holding excessive inventories often caused by the high return rate in these online malls. The recommendation system has been implemented focusing mainly on size extraction and recommendation functions along with a UI (user interface). For the former function, data are necessary to extract customers’ sizes and, for instance, the system to be used in China adopts their Chinese standard body size GB/T (Chinese national standard) considering that there are a variety of body types in their substantial population. The system shows the most similar size dataset among the body size GB/T dataset to the customer once he/she inputs his/her height and weight. Each GB/T data was entered after categorizing it according to the proportion between height and weight. For the latter function, size recommendation, size coding was performed first for all the clothes by the shop owner by entering individual size data. The clothes providing the most suitable fit for the customer are recommended by the selection of that which has the smallest deviation between coded clothes size and the customer body data after performing a series of comparative calculations. To validate the effectiveness of the extraction, a method that checks whether the difference between extracted size and the body size that has been measured remains within the error range of 4cm was used. The result showed there to be an approximate 88% matching rate for women and a slightly lower accuracy of 80% for men. Moreover, the error rate was relatively smaller for the upper half clothing such as shirts, jackets, and blouses or one-piece dresses. Such a result may have been generated since the GB/T data were actually the average data entered 10 years prior without categorizing nationalities, ages, and body types in detail. This research emphasized the necessity of a database containing a more segmented human body size data, which can be effective for extracting and recommending sizes more accurately as the latest ones continue to accumulate.