• Fashion & Textile Research Journal
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• v.22 no.5
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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.

• Journal of the Korea Management Engineers Society
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• v.23 no.4
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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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• v.26 no.2
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.783-788
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• 2021

Smart manufacturing is defined as the fully ICT-based manufacturing process which digitized, optimized, and automized the of manufacturing system in smart factory which includes product planning, design, production, quality, stock, procure. In this paper, we introduce the development of domestic standardization of smart factory and manufacturing data which are generated in operation of smart factory. We focus on general standardization of smart factory/ICT-based manufacturing system and data transactions related issues since the range of standardization is too wide. Based on these standardization review, we discuss the several concerns for utilization of manufacturing data.

• Journal of Information Processing Systems
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• v.15 no.1
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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%.

• Journal of Information Technology Services
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• v.20 no.3
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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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• v.14 no.3
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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.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.4
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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.