• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.3
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• pp.59-71
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• 2020

This study is empirically intended to look into the effects of smart factory technologies on quality and innovation performance in small and medium-sized Enterprises(SMEs). The research results are as follows. Device and application technologies for smart factory had a positive effect on the information quality and system quality, while platform technologies had an insignificant effect on the information quality and system quality, rejecting the effect of platform technologies for smart factory on information quality and system quality. Device technologies for smart factory had also a significant effect on innovative performance, while platform and application technologies had an insignificant effect on innovative performance, rejecting the effect of platform and application technologies for smart factory on innovative performance. The system quality had a significant effect on innovative performance, while the information quality had an insignificant effect on innovative performance. The quality played a partial mediating role in the effect of device technologies for smart factory on innovative performance. These results indicate that small and medium-sized venture firms should implement a high standard of information quality management(IQM) through interconnection as the kernel of a smart factory in the 4th revolutionary era, and that they can improve their corporate performance through the interlocking between components from manufacturing design to execution and analysis and the integrated management of systematic information collected from devices if necessary.

• Journal of the Korea Safety Management & Science
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• v.26 no.2
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• pp.107-115
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• 2024

This study conducted frequency analysis, reliability analysis, descriptive statistics, and correlation analysis to determine the impact of quality control managers' perception and implementation of KS certification factory inspection evaluation items on follow-up management. Through a multiple linear regression model, the influence of KS certification officer's awareness and implementation of KS certification factory inspection on post management was found to have a positive (+) influence on post management, with implementation having a greater influence on post management than awareness. It was having an impact. The independent variable (perception) has a statistically significant impact on the mediating variable (execution), and in the stage of verifying the mediating effect, the influence of the independent variable (perception) on the dependent variable (follow-up management) has a statistically significant impact. , In the stage where the independent variable (perception) and the mediator (implementation) are input simultaneously, both the independent variable and the mediator have a statistically significant effect on the dependent variable, indicating that there is a mediation effect.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.70-75
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• 2018

Smart Factory is an intelligent factory that can enhance productivity, quality, customer satisfaction, etc. by applying information and communications technology to the entire production process including design & development, manufacture, and distribution & logistics. The precise amount of data generated in a smart factory varies depending on the factory's size and state of facilities. Regardless, it would be difficult to apply traditional production management systems to a smart factory environment, as it generates vast amounts of data. For this reason, the need for a distributed big-data processing system has risen, which can process a large amount of data. Therefore, this article has designed a Gluster File System (GlusterFS)-based distributed big-data processing system that can be used in a smart factory environment. Compared to existing distributed processing systems, the proposed distributed big-data processing system reduces the system load and the risk of data loss through the distribution and management of network traffic.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.219-227
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• 2020

Smart Factory is one of the fastest developing and changing fourth industrial revolution fields. In particular, the degree of introduction and maturity level in the smart factory is an important part. In this paper, a cluster analysis of companies introduced smart factory was performed based on a new maturity assessment model. The 68% of 193 companies surveyed were at the basic level, with only 21% being the middle one. Most SMEs cited lack of funds as the main reason for not entering the middle one. As a result of the cluster analysis, it was found that all clusters had similar patterns but grouped into one of three levels of high, middle, and low depending on maturity level of smart factory operation, and process domain had the highest maturity and data domain was lowest among the 6 domains. Through this, analysis of more specific and quantified maturity levels can be performed using 6-domain smart factory maturity evaluation model.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.982-986
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• 2005

Digital Manufacturing is a technology to facilitate effective product developments and agile productions by digital environments representing the physical and logical schema and the behavior of real manufacturing system including manufacturing resources, processes and products. A digital virtual factory as a well-designed and integrated environment is essential for successful applications of this technology. In this research, we constructed a sophisticated digital virtual factory by measuring and 3-D CAD modeling using parametric methods. Specific parameters of each objects were decided by object-oriented schema of the digital factory. It is expected that this method is very useful for constructions of a digital factory, and helps to manage diverse information and re-use 3D models.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.1
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• pp.47-52
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• 2000

This study objectively in examines materials related to factory rationalization of D Corp., a regionally based enterprise. One reason that previous factory rationalizations have not been all that effective is that each firm has not used strategies specially designed for it Despite the fact that each firm has a different culture, and different human and physical resources, the application of rationalization without any modifications has produced many problems. In order to stabilize the production system and reduce the capacity of the factory, D Corp. changed the basic 5 S's and stimulated the factory atmosphere through computer education. Rationalization stabilized and standardized the factory, and organized the physical resources and each area of the factory according to their place in the process of production. It also made improvements that verified the party responsible for the flow of the complex production system, and simplified analysis supervision of production, and ex post management. We think that the successful example of D Corp. can serve as a real, tangible model for small and medium regionally-based firms to follow.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.133-152
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• 2017

Smart factory has been regarded as a big opportunity for manufacturing industries. However, little literature has been studied for the current status of Korean smart factory. Our paper tries to find gaps between research and real world by summarizing the recent literature and cases in Korean context. As the present level of smart factory introductions in Korean small manufacturing firms is lower than what a variety of literature says, our study points out that more efforts, investments and government support are required to catch up with the knowhow and technologies of developed countries although real-time control, enhanced productivity have been obtained. In future research, we will continue the smart factory study with the accumulated real data.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.274-280
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• 2024

The smart factory promotion project is a project that improves the entire management environment system, including the production process, using ICT technology. According to the 2019 Smart Factory Survey and Analysis Research Report of the Ministry of SMEs and Startups, small and medium-sized enterprises that introduced smart factories reported positive effects such as increased productivity, improved quality, and reduced costs on average. On the other hand, the survey results of companies that promoted the project despite positive results showed that there was room for improvement. This study dealt with the contents of the survey conducted on companies by the smart factory promotion agency in 2020 regarding the infrastructure configuration for promoting smart factories. We examined the meaningful contents implied by the data related to the infrastructure configuration. These meaningful survey results can lead to more efficient business promotion in the future when promoting smart factory projects.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.471-476
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• 1995

This paper focues on development and implementation of a perfomance management algorithm for IEEE802.4 token bus networks to serve large-scale integrated manufacturing systems. Such factory automation networks have to satisfy delay constraints imposed on time-critical messages while maintaining as much network capacity as possible for non-time-critical messages. This paper presents the structure of a network performance manager that possesses the knowledge about perfomance management in a set of fuzzy rules and deriving its action through fuzzy inference mechanism. The efficacy of the performance management has been demonstrated by a series of simulation experiments.