• International Journal of CAD/CAM
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• v.2 no.1
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• pp.39-44
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• 2002

This paper presents a structure and architecture of an integrated simulation method (ISM) to meet the requirements of virtual factory engineering (VFE). Combining CAD, VR and discrete event simulation techniques, the ISM provides static and dynamic simulation functions for implementation of VFE throughout the lifecycle. The static simulation can be used to evaluate the factory layout. The dynamic simulation enables us to evaluate ergonomics of factory, process performance of production system, feasibility of production plan and operation of factory, and to train operators safely, which cover the whole VFE lifecycle. The principles of the key techniques of VFE, including virtual factory data management system (VFDMS), static and dynamic simulation, are also discussed. To demonstrate and validate the ISM, a case study has been carried out in an assembly factory.

• The Southeast Asian review
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• v.23 no.3
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• pp.1-48
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• 2013

Taking the cases of Korean garment factories in Binh Duong area, this study aims to explain the phases and causes of the wildcat strikes that have rapidly expanded recently in Vietnam. For the purpose, this study raises several questions as follows. Why the strikes sometimes increase and decrease other times? Why the factory workers prefer a wildcat strike even though it is politically risky, unproductive, and complicated? By the same token, why the foreign management cannot or will not preemptively preclude the wildcat strikes that are usually predictable and the workers are mostly able to accomplish their demands? While answering these questions, this study explores the economic, political, and socio-cultural conditions of the wildcat strikes respectively. Based on the fieldwork in around 30 Korean owned garment factories and the interview with around 100 Vietnamese factory workers in Binh Duong, this study confirms several findings on the phases and causes of the strikes in the area in specific and in Vietnam in general. First, the annual trends of the wildcat strikes reflect the macroeconomic conditions in which the consumer prices and the labor market in Vietnamese economy and business conditions in the world economy are pivotal. Second, however, the influence of macroeconomic conditions on both the management and the workers in the garment factories are differential, depending on the financial situations of the multinational corporations and the workers' capability of reproducing their household economies. Thirdly, the possibility of the wildcat strike in each factory is relatively independent on the financial conditions of a factory and rather associated with the stable political structure and active political processes within the factory that enable the management and the workers to efficiently communicate each other. Lastly, the necessity of establishing political stability in a factory arises from the distinctive social and cultural characteristics of the multinational corporation in which foreign managers and native workers inevitably live in separate and different socio-cultural worlds.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.195-203
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• 2022

Recently, as the introduction of smart factories spreads, interest and research on the positive and negative effects of smart factory introduction are increasing. This study quantitatively analyzed the changes in the workplace innovation index following the introduction of smart factory for 750 companies in the 4 categories of the workplace innovation index. Overall, the workplace innovation index of companies that introduced smart factory was higher than those that did not, and there was a statistically significant difference, especially in the work organization. In addition, as a result of analyzing the effects of smart factory introduction and workplace innovation consulting together, in the case of labor-management relations and work organization, the introduction of a smart factory and consulting were found to match the improvement of the workplace innovation index.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.81-87
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• 2020

This paper creates a standard node for acquiring sensor data from various industrial sensors (IoT/non-IoT) for the establishment of Smart Factory Digital Twin, and provides inter-compatible data by linking zones by group/process to secure data stability and to ensure the digital twin (Digital Twin) of Smart Factory. The process of the Zone Master platform contains interface specifications to define sensor objects and how sensor interactions between independent systems are performed and carries out individual policies for unique data exchange rules. The interface for execution control of the Zone Master Platform processor provides system management, declaration management for public-subscribe, object management for registering and communicating status information of sensor objects, ownership management for property ownership sharing, time management for data synchronization, and data distribution management for Route information on data exchange.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.148-154
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• 2021

We propose the AI Smart Factory Model for integrated management of production processes in this paper .It is an integrated platform system for the production of food packaging containers, consisting of a platform system for the main producer, one or more production partner platform systems, and one or more raw material partner platform systems while each subsystem of the three systems consists of an integrated storage server platform that can be expanded infinitely with flexible systems that can extend client PCs and main servers according to size and integrated management of overall raw materials and production-related information. The hardware collects production site information in real time by using various equipment such as PLCs, on-site PCs, barcode printers, and wireless APs at the production site. MES and e-SCM data are stored in the cloud database server to ensure security and high availability of data, and accumulated as big data. It was built based on the project focused on dissemination and diffusion of the smart factory construction, advancement, and easy maintenance system promoted by the Ministry of SMEs and Startups to enhance the competitiveness of small and medium-sized enterprises (SMEs) manufacturing sites while we plan to propose this model in the paper to state funding projects for SMEs.

• Journal of Information Technology Applications and Management
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• v.30 no.5
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• pp.41-57
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• 2023

This paper aims to analyse the factors influencing the implementation of smart factories and their performance after implementation, using the grounded theory analysis method based on interview data. The research subjects were 21 companies that were selected by the Smart Manufacturing Innovation Promotion Group under the SME Technology Information Promotion Agency in 2020-2021 as the best case smart factory implementation companies, and introduced the intermediate stage 1 or above. A total of 87 concepts were generated as a result of the analysis. We were able to classify them into 16 detailed categories, and finally derived six broad categories. These six categories are "motivation for adoption", "adoption context", "adoption level", "technology adoption", "usage effect" and "management effect". As a result of the overall structure analysis, it was found that the adoption level of smart factory is determined by the adoption motivation, the IT technology experience affects the adoption level, the adoption level determines the usage and usage satisfaction, internal and external training affects the usage and usage satisfaction, and the performance or results obtained by the usage and usage are reduced defect rate, improved delivery rate and improved productivity. This study was able to derive detailed variables of environmental factors and technical characteristics that affect the adoption of smart factories, and explore the effects on the usage effects and management effects according to the level of adoption. Through this study, it is possible to suggest the direction of adoption according to the characteristics of SMEs that want to adopt smart factories.

• The Journal of Economics, Marketing and Management
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• v.12 no.4
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• pp.13-25
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• 2024

Purpose: Korea's construction industry has faced declining productivity and quality issues due to labor-intensive onsite construction and variables like weather, material price fluctuations, and labor shortages. The modular housing industry, introduced in Korea in 2003, offered benefits like reduced construction time and enhanced productivity through offsite manufacturing. However, its adoption remains limited due to high costs, quality concerns, and low consumer acceptance. Research Design, Data, and Methodology: This study explores the feasibility and impact of implementing smart factory technologies in the modular housing industry to overcome these barriers. Using survey data from 179 construction industry experts, the study employs frequency and regression analysis to identify key factors influencing the adoption of modular housing and the effectiveness of smart factories. Findings suggest that government-led educational programs and strong policy support are essential for successful implementation, enhancing productivity, reducing costs, and improving quality. Conclusions: The study emphasizes the need for standardization of modular housing, deregulation of relevant laws, and increased public awareness to stimulate market growth and innovation. Policy recommendations include financial support for modular manufacturers transitioning to smart factories, ensuring stable supply volumes, and promoting the benefits of modular housing to consumers. Integrating smart factory technologies can lead to significant advancements in the modular housing industry, contributing to the sustainable development and modernization of Korea's construction sector.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.61-68
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• 2020

In the era of the fourth industrial revolution, many production plants are gradually evolving into smart factories that apply information and communication technology to manufacturing, distribution, production, and quality management. The conversion from conventional factories to smart factories has resulted in the automation of production sites using the internet and the internet of things (IoT) technology. Thus, labor-intensive production can easily collect necessary information. However, implementing a smart factory required a significant amount of time, effort, and money. In particular, labor-intensive production industries are not automated, and productivity is determined by human skill. A representative industry of such industries is sewing the industry. In the sewing industry, wherein productivity is determined by the operator's skills. This study suggests that production performance, inventory management and product delivery of the sewing industries can be managed efficiently with existing production method by using smart buttons incorporating IoT functions, without using automated machinery.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2002.02a
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• pp.26-33
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• 2002

Die Factory follows typical order adaptive manufacturing, and delaying delivery affects directly product development of customer, Manufacturing Management System is tried to comply with the appointed date of delivery. It acquires running signal from NC milling, calculates manufacturing results, and offers the basic data to manage the operation ratio. Thus it offers Production data necessary to accomplish the objective of progress improvement for Unmanned Manufacturing. Manufacturing Management System runs on Web Environment, and is composed of electronic work order, operation ratio data acquisition and totaling module.