• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.647-664
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• 2022

Purpose: The purpose of this study is to derive major policies that domestic small and medium-sized manufacturing companies should consider to maximize productivity and quality improvement by utilizing manufacturing data and AI, and to find priorities and implications. Methods: In this study, domestic and international issues and literature review by country were conducted to derive major considerations such as manufacturing AI technology, manufacturing AI talent, manufacturing AI data and manufacturing AI ecosystem. Additionally, the questionnaire survey targeting 46 experts of manufacturing data and AI industry were conducted. Finally, the major considerations and detailed factors importance were derived by applying the Analytic Hierarchy Process (AHP). Results: As a result of the study, it was found that 'manufacturing AI technology', 'manufacturing AI talent', 'manufacturing AI data', and 'manufacturing AI ecosystem' exist as key considerations for domestic manufacturing AI. After empirical analysis, the importance of the four key considerations was found to be 'manufacturing AI ecosystem (0.272)', 'manufacturing AI data (0.265)', 'manufacturing AI technology (0.233)', and 'manufacturing AI talent (0.230)'. The importance of the derived four viewpoints is maintained at a similar level. In addition, looking at the detailed variables with the highest importance for each of the four perspectives, 'Best Practice', 'manufacturing data quality management regime, 'manufacturing data collection infrastructure', and 'manufacturing AI manpower level of solution providers' were found. Conclusion: For the sustainable growth of the domestic manufacturing AI ecosystem, it should be possible to develop and promote manufacturing AI policies in a balanced way by considering all four derived viewpoints. This paper is expected to be used as an effective guideline when developing policies for upgrading manufacturing through domestic manufacturing data and AI in the future.

• Proceedings of the Technology Innovation Conference
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• 1992.12a
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• pp.119-155
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• 1992

Traditionally, the target of manufaturing technology strategy was derived in a efficiency, cost and productivity. So most activities of the manufacturing brought focus into the engineering technology, equipments and research and improvement of new products to maximize the efficiency. As a resell of this legacy, most of the activities of manufacturing has been executed on the method of quality improvement, development of new equipment to incense the efficiency and the research of materials for new products. Those trends, however overlook the operation management activities which is very important as a assets in competitive strategy. But the market enviornment of morden manufacturing companies faced to the uncertainty and complexity. So they need capability of competition which requires new concept of manufacturing technology strategy to grasp the competitive advantages. In this point of view, this paper deal with the empirical study in korean manufacturing technology strategy of the electic and electronic industry. For the empirical study, check list was made to survey the 98 manufacturing companies. The analysis procedures are as below. First, identify the manufacturing technology group an product structure group by each variable. Second manufacturing technology variables are segmented into product technology and vertical integration, suborder and infrastructure, to analyse the decision making pattern which derive the strategy groups. Third, by the fitness analysis between product structure group and manufacturing technology group, the economic results of a growth rate of sale and a profit rate of sale are tested. In this approach, fitness analysis between product structure group and manufacturing technology group show, as a whole, the no significant values in economic results of the company. But investigating the statistical values shows the trend that econmic result of the complany is somewhat higher when the degree of fitness of manufacturing technology strategy by product structure has high value. Concluding the remarks, the competitive advantages of company lies not in the efficiency of manufacturing systems but in the way of the structure and decision making pattern of the manufacturing system. And the cons i stoney between strategy target and manufacturing technology strategy, and the consistency of manufacturing technology strategy and product structure are the term of competitive advantages.

• Journal of Power System Engineering
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• v.4 no.4
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• pp.92-98
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• 2000

Modern manufacturing systems should cope with the frequent changes in a product model and disturbances in manufacturing process. The control system of such systems must cover a constant adaptation and high flexibility. Holonic Flexible Manufacturing Cell(HFMC) is introduced to handle these issues more successfully. It is based on the concept of autonomous co-operating agent, called 'Holon', which is a building block of a manufacturing system for transforming, transporting, storing and/or validating information and physical objects. In this paper the basic structure of the HFMC is represented by using Unified Modeling Language and Open architecture cell controller is developed for effective integration components of a manufacturing system. Also a new control model, called MuLOM(Multi-Layered Operation Model), is suggested to represent the control behaviour for a holonic flexible manufacturing cell control system.

• IE interfaces
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• v.10 no.2
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• pp.91-97
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• 1997

A mass production system was implemented to reduce a manufacturing cost in a way of copying with a strong world market competition. However customer's demands are changing so rapidly and the mass production system is no longer competitive to meet the demands. FMS(Flexible Manufacturing System) has been introduced as a replacement for the mass production system, but it still does not meet system's requirements. A new manufacturing system, called a holonic manufacturing system (HMS), is emerging. This paper is giving a first approach of a HMS for a multirobot cooperative process.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.512-515
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• 1996

A mass production system was implemented to reduce a manufacturing cost in a way of copying with a strong world market competition. However customer's demands are changing so rapidly and the mass production system is nolonger competitive to meet the demands. FMS (Flexible Manufacturing System) has been introduced as a replacement for the mass production system, but it still does not meet system's requirements. A new manufacturing system, called a holonic manufacturing system(HMS), is emerging. In this paper it is introduced an autonomous cooperative system under the concept of HMS.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.236-247
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• 2015

This study aims to examine the effects of industrial clustering and manufacturing flexibility on innovation capability and operational performance. This study follow a survey method to collect data pertaining to the phenomena of industrial clustering, manufacturing flexibility, innovation capability, and operational performance by utilizing a single respondent design. A total of 124 Indonesian manufacturing SMEs are taken to test the proposed theoretical model by utilizing covariance-based structural equations modeling approach. It was found that both industrial clustering and manufacturing flexibility was positively associated with operational performance and innovation capability as well. In addition, innovation capability may account for the effects of industrial clustering and manufacturing flexibility on operational performance. This implies that manufacturing SMEs have to reorient their production and operation perspectives, including agglomerate with other similar or related SMEs to develop and utilize their own resources. The SMEs also need to possess some degree of manufacturing flexibility in respond to the uncertain environment and market changes. In addition, the SMEs should put a greater emphasize to use industrial cluster and manufacturing flexibility benefits to generate innovation capability to achieve high performance.

• Journal of the Korean Society of Costume
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• v.58 no.8
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• pp.63-73
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• 2008

From the ancient Korea to the late Joseon Korean fur and leather had been preferred in and out of Korea for their good quality and excellent manufacturing skill. Since Unified Silla (A.D.676${\sim}$A.D.936) Korean fur and leather were manufactured divisionally by workmen specialized in materials and products, and such manufacturing process was succeeded to Goryeo and Joseon. Manufacturing of fur and leather was consisted of as follows: hunting and butchering - peeling - beating with a paddle and removing fat - oil manufacturing - drying - tanning, then cutting and sewing, and there was a special caring method. In order to make good fur and leather, each process of manufacturing needed particular techniques and all available methods were tried to have tender fur and leather by using smoking, excrement, lime, vegetable tannin and even cerebral liquid. And also required mouth-chewing and hand-pounding with a lot of time and of labor Keeping furs resilience and flexibility, sowing several skins together, even when the after-all-process skin was converted into clothes, was much more difficult than sewing fabric. Thus, the manufacturing cost was as much expensive as skin materials, and the volume of manufacturing of fur and leather was also limited. Therefore, fur and leather must have been popular for scarcity value in the manufacturing process, and this scarcity must have caused an extreme luxury of fur.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.149-156
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• 2022

Manufacturing process mining performs various data analyzes of performance on event logs that record production. That is, it analyzes the event log data accumulated in the information system and extracts useful information necessary for business execution. Process data analysis by process mining analyzes actual data extracted from manufacturing execution systems (MES) to enable accurate manufacturing process analysis. In order to continuously manage and improve manufacturing and manufacturing processes, there is a need to structure, monitor and analyze the processes, but there is a lack of suitable technology to use. The purpose of this research is to propose a manufacturing process analysis method using process mining and to establish a manufacturing process mining system by analyzing empirical data. In this research, the manufacturing process was analyzed by process mining technology using transaction data extracted from MES. A relationship model of the manufacturing process and equipment was derived, and various performance analyzes were performed on the derived process model from the viewpoint of work, equipment, and time. The results of this analysis are highly effective in shortening process lead times (bottleneck analysis, time analysis), improving productivity (throughput analysis), and reducing costs (equipment analysis).

• East Asian Economic Review
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• v.23 no.2
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• pp.169-202
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• 2019

Productivity in agriculture or services has long been understood as playing an important role in the growth of manufacturing. In this paper we present a general equilibrium model in which manufacturing growth is stimulated by non-manufacturing sectors that provides goods used in both research and final consumption. The model permits the evaluation of two policy options for stimulating manufacturing growth: (1) a country imports more non-manufacturing goods from a foreign country with higher productivity and (2) a country increases productivity of domestic non-manufacturing. We find that both policies improve welfare of the economy, but depending on the policy the manufacturing sector responses differently. Specifically, employment and value-added in manufacturing increase with policy (1), but contract with policy (2). Therefore, specialization of the import non-manufactured goods helps explain why some Asian economies experience rapid growth in the manufacturing sector without progress in other sectors.

• 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.