• 한국의류산업학회지
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• 제22권5호
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• pp.660-675
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• 2020

Since Industry 4.0, there is a growing interest in smart manufacturing across all industries. However, there are few studies on this topic in the garment industry despite the growing interest in implementing smart manufacturing. This paper presents the feasibility and essential considerations for implementing smart manufacturing in the garment industry. A systematic review analysis was conducted. Studies on garment manufacturing and smart manufacturing were searched separately in the Scopus database. Key technologies for each manufacturing were derived by keyword analysis. Studies on key technologies in each manufacturing were selected; in addition, bibliographic analysis and cluster analysis were conducted to understand the progress of technological development in the garment industry. In garment manufacturing, technology studies are rare as well as locally biased. In addition, there are technological gaps compared to other manufacturing. However, smart manufacturing studies are still in their infancy and the direction of garment manufacturing studies are toward smart manufacturing. More studies are needed to apply the key technologies of smart manufacturing to garment manufacturing. In this case, the progress of technology development, the difference in the industrial environment, and the level of implementation should be considered. Human components should be integrated into smart manufacturing systems in a labor-intensive garment manufacturing process.

• 한국경영공학회지
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• 제23권4호
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• pp.165-177
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• 2018

Smart technology has been utilized in various fields of all kinds of industries. Manufacturing industry has built its smart technology environment appropriate for its manufacturing fields in order to strengthen its manufacturing performance and competitiveness. The advance of smart technology for manufacturing industry needs to efficiently produce products, and response customer's demands and services in a global industrial environment. The smart technology capability of manufacturing fields is very crucial for the innovative production and efficient operation activities, and for efficient advancement of the manufacturing performance. We have necessitated a scientific and objective method that can gauge a smart technology ability in order to manage and strengthen the smart technology ability of manufacturing fields. This research provides a comprehensive framework that can rationally gauge the smart technology capability of manufacturing fields for effectively managing and advancing their smart technology capabilities. In this research, we especially develop a structural framework that can gauge the smart technology capability for a smart factory of manufacturing fields, with verifying by reliability analysis and factor analysis based on previous literature. This study presents a 13-item framework that can measure the smart technology capability for a smart factory of manufacturing fields in a smart technology perspective.

• Journal of Information Technology Applications and Management
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• 제26권2호
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• pp.13-26
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• 2019

The purpose of this study is to examine the effect of each of the two technology acceptance factors(employees' attitude toward smart factory technology, and ease of smart factory technology use) on the introduction level of each of the three smart factory technologies (manufacturing big data technology, automation technology, and supply chain integration technology), and in turn, the effect of each of the three smart factory technologies on manufacturing performance. This study employed PLS statistics software package to empirically validate a structural equation model with survey data from 100 domestic small-and medium-sized manufacturing firms (SMMFs). The analysis results revealed the followings. First, it is founded that employees' attitude toward smart factory technology influenced all of the three smart factory technology introduction levels in a positive manner. In particular, SMMFs of which employees had more favorable attitude toward smart factory technology tended to increase introduction levels of both automation technology and supply chain integration technology more than in the case of manufacturing big data technology. Second, ease of smart factory technology use also had a positive impact on each of the three smart factory technology introduction levels, respectively. A noteworthy finding is this : SMMFs which perceived smart factory technology as easier to use would like to elevate the introduction level of manufacturing big data technology more than in the cases of either automation technology or supply chain integration technology. Third, smart factory technologies such as automation technology and supply chain integration technology had affirmative impacts on manufacturing performance of SMMFs. These results shed some valuable insights on the introduction of smart factory technology : The success of smart factory heavily depends on organization-and people-related factors such as employees' attitude toward smart factory technology and employees' perceived ease of smart factory technology use.

• 산업경영시스템학회지
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• 제42권4호
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• pp.76-83
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• 2019

The South Korean government is actively assisting the supply of the smart factory solutions to SMEs (Small & Medium-sized Enterprises) according to its manufacturing innovation 3.0 policy for the smart manufacturing as the 4th industrial revolution era unfolds. This study analyzed the impacts of the smart factory solutions, which have been supplied by the government, on the companies performances. The effects of the level of smart factory and the operation capabilities for the smart factory solutions on company performances, and the mediating effects of manufacturing capabilities have been analyzed using SPSS and AMOS. The data for this survey-based study were collected from the SMEs which implemented the smart factory solutions since 2015. The results show that the level of smart factory solutions adopted and operation capabilities for the smart factories do not have direct effects on the company performances, but their mediating effects on the manufacturing capabilities matter and the manufacturing capabilities effect directly on the company performances. In addition significant factors boosting the operation capability for the smart factory and the levels of the smart factory solutions are identified. Finally, the policy direction for enhancing the smart factory effects is presented, and the future research directions along with the limitations are suggested.

• 한국전자통신학회논문지
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• 제16권5호
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• pp.783-788
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• 2021

스마트제조는 제품기획, 설계, 생산, 품질, 재고, 조달 등 스마트공장 내 제조 프로세스의 정보화, 최적화 및 생산 시스템의 자동화를 ICT기반 스마트 기술로 실현하는 제조 공정으로 정의된다. 본 논문에서는 스마트공장과 스마트제조 시스템의 운영시 발생하는 제조 데이터와 관련된 국내 표준화 동향에 대해 소개한다. 표준화의 범위가 매우 넓기 때문에 스마트공장 및 제조 ICT시스템과 관련된 일반적인 표준화 내용 및 제조 데이터를 거래시 필요한 표준에 관련된 사항을 다룬다. 이를 기반으로 하여 제조 데이터의 활용을 위해 필요한 사항에 대해 논의한다.

• Journal of Information Processing Systems
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• 제15권1호
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• pp.181-188
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• 2019

A production system is a management system that supports all activities to perform production operations at the manufacturing site. From the point-of-view of a smart factory, smart manufacturing systems redesigned the concept of onsite production systems to fit the entire system and its necessary functional composition. In this study, we select the key functions needed to build a smart factory for a PCB line and propose a new six-step model for the deployment of a smart manufacturing system by integrating essential functions. The smart manufacturing system newly classified the production and operation tasks of PCB manufacturing and selected necessary functions through requirement analysis and benchmarking of advanced companies. The selected production operation tasks are mapped to the functions of the system and configured into seven modules, and the optimal deployment model is presented to allow flexible responses to the characteristics of the tasks. These methodologies are first presented in this study, and the proposed model was applied to the PCB line to confirm that they had significant changes in the work method, qualitative effects, and quantitative effects. Typically, lead time and WIP have reduced by about 50%.

• 한국IT서비스학회지
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• 제20권3호
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• pp.13-25
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• 2021

The article presents the results of how employee's hierarchical job roles differently recognize a SM(smart manufacturing) and evaluate comprehensively on the SM employees training. The research was focus on small and medium size manufacturing corporation in Banwol·Siwha industrial complex, where is carried out Smart Complex National Policy. The Results from 205 participants working for a manufacturing firms in the Banwol·Siwha industrial complex. The results of study show that managers (vs workers) group is higher recognition of smart manufacturing and more intention to participate a SM employee training and utilize a SM equipments for test a manufacturing process. and these variables were mediated by SM cognition. These results will help SM manpower training center strategically design their training programs to maximize the training effectiveness.

• International Journal of Internet, Broadcasting and Communication
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• 제14권3호
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• pp.91-100
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• 2022

Major developed countries are seriously considering smart factories to increase their manufacturing competitiveness. Smart factory is a customized factory that incorporates ICT in the entire process from product planning to design, distribution and sales. This can reduce production costs and respond flexibly to the consumer market. The smart factory converts physical signals into digital signals, connects machines, parts, factories, manufacturing processes, people, and supply chain partners in the factory to each other, and uses the collected data to enable the smart factory platform to operate intelligently. Enhancing personalized value is the key. Therefore, it can be said that the success or failure of a smart factory depends on whether big data is secured and utilized. Standardized communication and collaboration are required to smoothly acquire big data inside and outside the factory in the smart factory, and the use of big data can be maximized through big data analysis. This study examines big data analysis and standardization in smart factory. Manufacturing innovation by country, smart factory construction framework, smart factory implementation key elements, big data analysis and visualization, etc. will be reviewed first. Through this, we propose services such as big data infrastructure construction process, big data platform components, big data modeling, big data quality management components, big data standardization, and big data implementation consulting that can be suggested when building big data infrastructure in smart factories. It is expected that this proposal can be a guide for building big data infrastructure for companies that want to introduce a smart factory.

• 반도체디스플레이기술학회지
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• 제21권4호
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• pp.168-173
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• 2022

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.

• 대한임베디드공학회논문지
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• 제14권4호
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• pp.187-195
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• 2019

In the fourth industrial revolution era, various commercial smart platforms for smart system implementation are being developed and serviced. However, since most of the smart platforms have been developed for general purposes, they are difficult to apply / utilize because they cannot satisfy the requirements of real-time data management, data visualization and data storage of smart factory system. In this paper, we implemented an open source based smart manufacturing big data platform that can manage highly efficient / reliable data integration for the diagnosis diagnostic system of manufacturing robots.