• Journal of the Korea Safety Management & Science
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• v.26 no.1
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• pp.45-53
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• 2024

A Smart factories are systems that enable quick response to customer demands, reduce defect rates, and maximize productivity. They have evolved from manual labor-intensive processes to automation and now to cyber-physical systems with the help of information and communication technology. However, many small and medium-sized enterprises (SMEs) are still unable to implement even the initial stages of smart factories due to various environmental and economic constraints. Additionally, there is a lack of awareness and understanding of the concept of smart factories. To address this issue, the Cooperation-based Smart Factory Construction Support Project was launched. This project is a differentiated support project that provides customized programs based on the size and level of the company. Research has been conducted to analyze the impact of this project on participating and non-participating companies. The study aims to determine the effectiveness of the support policy and suggest efficient measures for improvement. Furthermore, the research aims to provide direction for future support projects to enhance the manufacturing competitiveness of SMEs. Ultimately, the goal is to improve the overall manufacturing industry and drive innovation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.1-17
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• 2022

The purpose of this study is to suggest a plan to improve the level of acceptance of related technologies and the transition to smart factories of small and medium-sized manufacturing enterprises by using 'technology readiness' and 'integrated technology acceptance model'. To this end, the research hypothesis was verified by collecting questionnaire data from 130 small and medium-sized manufacturing companies in Korea and conducting path analysis. First, optimism affects performance expectations, social influence, and facilitation conditions, innovation affects performance expectations, effort expectations, and social influence, discomfort affects performance expectations, social influence, and facilitation conditions, and anxiety affects effort expectations, social influence and facilitation conditions. has been proven to affect Finally, performance expectations, effort expectations, social influence, and facilitation conditions were verified to have a significant positive effect on the intention to accept technology.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.107-124
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• 2021

This study is a study on the innovation resistance that may arise in connection with the introduction and use of smart factory-related technologies by SMEs. It is to study the effect of the leading factors of innovation resistance on innovation resistance and the effect of innovation resistance on use intention by using the extended innovation resistance model. A total of 176 survey data were used for the study, and the study was conducted using SPSS 25 and Smart PLS 2.0. Relative advantage, suitability, perceived risk, social impact, and organizational characteristics have a significant effect on innovation resistance, and innovation resistance was tested to have a significant effect on the intention to use. As an implication according to the research, a plan to improve the level of introduction and use of smart factories using the expanded innovative storage model was presented by dividing positive and negative factors, and factors that should be improved and factors that should be reduced are presented. It was specifically presented.

• Journal of Practical Engineering Education
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• v.11 no.2
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• pp.269-282
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• 2019

The purpose of this study is to identify the level of development of major technologies used in smart manufacturing in Korea and to use it as an objective basis for establishing smart manufacturing R & D personnel training policies. We select 25 key technologies to build and operate smart factories for the US, Germany, Japan, EU, Korea, and China, and examine the level (%) and gap (year) by smart manufacturing technology in each country. Based on the results, it is expected to contribute to reinforcing the global market competitiveness of the Korea manufacturing industry by checking the current status of R & D personnel training and suggesting policy suggestions for nurturing R & D personnel.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.55-61
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• 2020

The communication issues expected for SMEs are that 1) IT systems are not easy to connect, 2) data collection and integration by heterogeneous systems are difficult, and 3) various fieldbuses and protocols make interfaces difficult. Usually, SMEs often have automation built before the introduction of smart factories. It is necessary to provide communication technology such as Sensing to meet the heterogeneous system level with the old aged sensors in the automation equipment and communication network of SMEs. We will consider how to improve the network interface before applying the latest network technology at the time of preparation using PI.

• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.79-94
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• 2022

Purpose - The purpose of this study is to examine firm-level attributes related to Korean manufacturing small and medium-sized enterprises' (SMEs') decisions to implement smart factories. Design/methodology/approach - This study uses the provided by the Ministry of SMEs and Startups of Korea and the Korea Federation of SMEs. Manufacturing SMEs' decisions to implement smart factories in 2018-2019 were analyzed using multinomial logit and ordered logit models. Findings - The findings of this study suggest that firms' decisions to implement smart factories were positively related to firm size, R&D intensity, international market scope, and transactional relationships with customers. However, smart factory implementation decisions were not related to firm age and CEO gender. Research implications or Originality - This study illuminates firm-level attributes that may drive organizational innovation in the era of Industry 4.0 and thus contributes to the innovation adoption literature. This study also contributes to growing research on smart factories by analyzing the actual, progressive decisions to implement smart factories, as opposed to perceived intentions to implement them.

• Journal of the Korean Society of Systems Engineering
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• v.11 no.2
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• pp.95-105
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• 2015

Manufacturing Execution System(MES) is in charge of manufacturing execution in the shop floor based on the inputs given by high level information such as ERP, etc. The typical MES implemented is not tightly interconnected with shop floor control system including real (or near real) time monitoring and control devices such as PLC. The lack of real-time interfaces is one of the major obstacles to achieve accurate and optimization of the total performance index of the shop floor system. Smart factory system in the paradigm of Industry 4.0 tries to solve the problems via CPS (Cyber Physical System) technology and FILS (Factory In-the-Loop System). In this paper, we conducted Systems Engineering Approach to design an advanced MES (namely Smart MES) that can accommodate CPS and FILS concept. Specifically, we tailored Systems Engineering Process (SEP) based on an International Standard formalized as ISO/IEC 15288 to develop Stakeholders' Requirements (StR), System Requirements (SyR). The deliverables of each process are modeled and represented by the SysML, UML customized to Systems Engineering. The results of the research can provide a conceptual framework for future MES that can play a crucial role in the Smart Factory.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.117-125
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• 2020

According to the government's smart factory promotion project for small and medium-sized enterprises, more than 10,000 intelligent factories are scheduled or already built in the country and the government-led goal is to nurture 100,000 skilled workers by 2022. Smart Factory introduces numerous types of education and training courses from the supplier's point of view, such as training institutions belonging to local governments, some universities, and public organizations, in the form of an efficient resource management system and ICT technology convergence in the automated manufacturing equipment. The lack of linkage with the NCS, the standard for training, seems to have room for rethinking and direction. Results of survey is provided for the family companies of K-University in the metropolitan area and Chungnam area, and analyzes job demands by identifying whether or not they want to introduce smart factories. Defining the practitioners who will serve as a window for the introduction of smart factory technology within the company, setting up a training goal in consideration of the career path, and including the level of training required competency units, optional competency units, and training time suitable for introducing and operating smart factories. Author would like to present an NCS-based qualification design plan.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.447-466
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• 2021

Purpose: This article aims 1) to propose Quality 4.0 concept through surveying related literature, 2) to suggest key elements of Quality 4.0 by arranging the elements of Quality 4.0 that appeared in the literature, 3) to determine the levels of Quality 4.0, and 4) to suggest ideas for effective deployment of Quality 4.0. Methods: Eleven papers or documents are reviewed for Quality 4.0 concept; two papers and one document are investigated for key element extraction of Quality 4.0; and smart factory roadmap and industry 4.0 maturity model are studied to determine the levels of Quality 4.0. Results: 1) Quality 4.0 definition is proposed. 2) Three key elements are determined: data acquisition and analytics, connection and integration, and leadership and culture. 3) Six Quality 4.0 levels are determined. 4) Some suggestions are addressed for effective deployment of Quality 4.0. Conclusion: 1) Definition, key elements, levels, and some suggestions on effective deployment of Quality 4.0 are addressed. 2) Specific contents of Quality 4.0 education and training courses should be provided in the future. 3) Two future research directions are proposed.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.59-73
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• 2023

In the era of the 4th Industrial Revolution, manufacturing is the implementation of smart factories through digital transformation, and refers to consumer-centered intelligent factories that combine next-generation digital new technologies and manufacturing technologies beyond the existing factory automation level. In order to successfully settle such a smart factory, it is necessary to train professionals. However, education for smart factories is difficult to have actual field mechanical facilities or overall production processes. Therefore, there is a need for a system that can visualize and control the flow and process of logistics at the actual production site. In this paper, the logistics flow of the actual site was implemented as a small FMS, a physical system, and the production process was implemented as a digital system. In real-time synchronization of the physical system and the digital system, the location of AGV and materials, and the process state can be monitored to see the flow of logistics and process processes at the actual manufacturing site. The developed digital twin system can be used as an effective educational system for training manpower in smart factories.