• Journal of Digital Convergence
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• v.17 no.7
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• pp.115-124
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• 2019

This study is an empirical study of 'the effect of the key factors of Smart Factory on management performance through internal environmental factors in small and medium enterprises'. The purpose of the research is to verify that the implementation of a Smart Factory affects the performance of management and contribute to the continued development of the company, and to suggest the national policy of expanding the deployment of a Smart Factory. The procedures were surveyed by working-level officials of small and medium-sized manufacturing companies with a Smart Factory and statistically analyzed with the SPSS and SMART PLS. The results of the study showed that first, the environmental factors within the company had a positive effect on the key components of the Smart Factory. Second, the key factor in Smart Factory has had a positive impact on management performance. The above evidence shows that the key factors in smart factory considering the environmental factors of an enterprise affect its management performance, thus laying the theoretical foundation for the performance of smart factory construction. In the future, we will study how to build a Smart Factory.

• The Journal of Economics, Marketing and Management
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• v.8 no.3
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• pp.23-36
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• 2020

Purpose: The purpose of this study is to identify the strategic direction of organizations and their employees to efficiently utilize smart factories and enhance business performance among Korean manufacturing companies. Research design, data, and methodology: We derived a structured research model to check the mediated effect of utilization of smart factory between the characteristics of smart factory and the innovation activities. Results: Quality characteristics of smart factory and Innovation activities were all found to have a statistically significant effect on utilization of smart factory, utilization of smart factory was found to have a statistically significant effect on the business performance. And it has been shown that the utilization of smart factory is partially mediated relative to the quality characteristics of smart factory and business performance and relative to innovation activities and business performance. Conclusions: Smart factory builders can reflect the areas that affect utilization of the smart factory in their strategies by considering the quality characteristics of the smart factory and innovation Activities. Therefore, smart factory builders can identify the quality characteristics of smart factory and reflect them in the process and analyze active utilize measures through the innovative activities of the employees of the organization, thereby influencing business performance.

• IE interfaces
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• v.16 no.1
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• pp.63-69
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• 2003

Virtual Manufacturing is a technology facilitating effective development and agile production of products via computer models representing physical and logical schema and the behavior of the real manufacturing systems including manufacturing resources, environments and products. For the successful application of this technology, a virtual factory as a well-designed and integrated environment is essential. To construct a virtual factory in effective and concurrent manners, a supporting information infrastructure for managing diverse models of virtual factory is very important. In this paper, we constructed the web-based information management system for many engineering activities related with a virtual factory. Using this system, users can handle all information and diverse digital files including attributes, parameters, 3-D CAD files, simulation models, and etc. of a cell, line and whole factory. We expect that this information management system for a virtual factory helps us achieve great time savings and advances in accuracy for construction and maintenance activities of virtual factories.

• Journal of the Korea Safety Management & Science
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• v.26 no.3
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• pp.123-131
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• 2024

In this study, two things were analyzed for Korean Standards certification factory review evaluation items. First, it was examined whether there is a difference between the Practice of factory review evaluation items and Post-Management according to the change in perception of the factory review evaluation items of Korean Standards certification personnel. Second, the moderating effect of the Korean Standards certification personnel's perception of the effect of the Practice of the factory review evaluation items of Korean Standards certification personnel on Post-Management was verified. Although there is a statistically significant difference between Practice and Post-Management according to changes in perception of Korean Standards personnel, there is no statistically significant difference in the impact (slope) of Practice on Post-Management according to changes in perception of Korean Standards certification personnel. As the perception of factory review evaluation items increases, the Practice and Post-Management of factory review evaluation items are increasing, but the impact of the Practice of factory review evaluation items on Post-Management does not affect it. In order to further advance Korean Standards certification, which plays an important role in maintaining the quality level of products produced by companies, efforts to raise the level of perception of Korean Standards certification personnel are considered necessary.

• Journal of Korean Society for Quality Management
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• v.48 no.1
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• pp.1-11
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• 2020

Purpose: This paper treats the survey result from the suppliers of smart factories. Based on the survey results, it is provided suggestions about government policies of the smart factory. Methods: For providing political suggestions, the survey of smart factory is conducted. The survey results are analyzed by the correlation and association methods based on the stratification. Results: The survey results are analyzed for extracting policy-level suggestions. Multiple policy-level suggestions are identified and presented in the conclusion. Conclusion: Six policy-level suggestions are presented for enhancing the management efficiency of suppliers of smart factory.

• The Journal of Economics, Marketing and Management
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• v.12 no.4
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• pp.65-76
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• 2024

Purpose: This study aims to identify the relationship between SMEs' digital transformation capabilities, smart factory utilization, and management performance. It also aims to suggest how companies strategically utilize smart factories to achieve a competitive advantage and sustainable growth through empirical analysis of differences in innovation resistance and organizational characteristics. Research design, data, and methodology: This study Implement for SME's building smart factories did. The survey was conducted for 90days from October 1st, 2023 to December 31th, 2023. Total of 210 surveys were collected, and 186 surveys, excluding ones with missing value and outliers (64 surveys), were used. Results: The results of the empirical analysis based on previous research are as follows. First, digital transformation capabilities such as digital technology, digital leadership, and digital strategy affect smart factory utilization. Second, smart factory use affects operational performance. Third, innovation resistance has a moderating effect in the relationship with digital transformation capabilities, smart factory utilization, and management performance. Fourth, organizational characteristics have a moderating effect in the relationship with digital transformation capabilities, smart factory utilization, and management performance. Conclusions: Explore strategic ways to improve your organization's digital transformation capabilities. It is necessary to establish a strategy to make organizational members aware of the necessity and importance of introducing a new system through centralization of the organization.

• The Journal of Economics, Marketing and Management
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• v.11 no.6
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• pp.43-55
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• 2023

Purpose: The purpose of this study was to identify the relationship between smart factory utilization and continued use intention between UTAUT, dynamic capabilities of smart factory construction companies and present the company's strategic direction. Research design, data, and methodology: In this study, a structured research model was derived to confirm the relationship between UTAUT, dynamic capabilities, smart factory utilization and continued use intention and the difference according to Technology perception. For analysis a total of 223 valid questionnaires from e-commerce users were used. Confirmatory factor analysis, correlation analysis, and structural equations were conducted to verify. Results: Both UTAUT, dynamic capabilities had a significant effect on smart factory utilization as well as continued use intention. It was found that the relationship between UTAUT, dynamic capabilities, smart factory utilization, and continued use intention. differed depending on the technology perception. Conclusions: Organizational members utilize the smart factory in anticipation of effects such as work performance and various improvements. Smart factory data will be used continuously when it is useful for business processes and operations. It is necessary to establish strategies and provide training to improve the technical level and capabilities of organizational members. Through this, a strategy is needed that can be continuously used by utilizing the information obtained through smart factory to improve work efficiency, productivity and efficiency increase is needed

• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.281-294
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• 2023

Purpose - The purpose of this study is to explore the determinants of the organizational level for the advancement of smart factory. We suggested three determinants of the organizational level such as CEO's entrepreneurship, high-involvement human resource management, and cooperative industrial relations. Design/methodology/approach - The population of our survey was manufacturing SMEs, and we took a sample and conducted a survey of 232 companies. Since the level of smart factory advancement, which is a dependent variable, was measured on an ordinal scale, ordinal logistic regression analysis was used to test the hypothesis. Findings - The higher the level of high-involvement human resource management, the higher the level of smart factory advancement. As the level of high-involvement human resource management increases by one unit, the probability of smart factory advancement increases by 22.8%. On the other hand, the CEO's entrepreneurship did not significantly affect the level of smart factory advancement. Interestingly, the cooperative industrial relations negatively affected to the level of smart factory advancement, contrary to the hypothesis prediction. Research implications or Originality - This study explored determinants at the organizational level that affect the advancement of smart factories. Through this, various implications are presented for related research and policy fields.

• 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 Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2677-2682
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• 2015

Smart factory has the function of integrated management of production process management, logistics management as a intelligent factory, it is also emerging as the core of new industry which converges ICT and manufacturing business. We suggested Smart factory logistics management system which embedded position tracking technology and the system converges ICT and IoT. This suggested system can manage all the processes from production to release by tracking route and position based on signal strength of bluetooth 4.0 beacon tag. For the more, we will expect to apply to the various type of factory environments like detachable installation, optimized management using sensor.