• 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.

• KIEAE Journal
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• v.13 no.5
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• pp.43-50
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• 2013

Recent researches on plant factory system deal with the convergence of lighting technology, agricultural technology inclusive to the high-tech industries worldwide in order to respond to the decreasing crop harvest due to global warming and abnormal weather phenomena. However, the fundamental performance standard is not currently being introduced in the case of plants factory and its commercialization is not activated because of high initial investment and operating cost. Large portion of the initial investment and operating cost of a plant factory is ascribed to artificial light sources and thermal control facilities, therefore, innovation should be provided in order to improve the economics of the plant factory. As an alternative, new plant factory could harness solar thermal and geothermal systems for heating, cooling and ventilation. In this study, a natural light dependent multi-layer plant factory's thermal environment was analyzed with two-dimensional numerical methods to elicit efficient operation conditions for optimized internal physical environment. Depending on the supply air temperature and airflow rate introduced in the facility, the temperature changes around the crops was interpreted. Since the air supplied into the plant factory does not stay long enough, the ambient temperature predicted around the plating trays was not significantly different from that of the supplied air. However, the changes of airflow rate and air flow pattern could cause difference to the temperature around the planting trays. Increasing the amount of time of air staying around the planting trays could improve energy performance in case the thermal environment of a natural light based multi-layer plant factory is considered.

• Korean Journal of Environmental Agriculture
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• v.26 no.3
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• pp.204-209
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• 2007

Agricultural area in the vicinity of the ${\triangle}{\triangle}$ smelting factory in Kyeongbuk province, the third largest zinc smelting factory in the world, was contaminated by high concentration of heavy metals. However, the heavy metals source was not yet directly traced and thus, resulted to a conflict between the factory and residents within its vicinity. In order to determine the level of heavy metal contamination in the arable lands located at the north eastern part of the factory, soils were sampled systematically. To find out the major reason for the occurrence of this problem, waters and aerosols were sampled with constant intervals to the upward and downward direction from the factory and were analyzed to find out the heavy metal concentrations. Cadmium (Cd) and zinc (Zn) of the heavy metals were highly accumulated more than the Korean warning criteria (Cd 1.5, Zn 300 mg $kg^{-1}$) with mean values 1.7 and 407 mg $kg^{-1}$, respectively, at the surface soils (0-20 cm), and heavy metal concentration significantly decreased with increasing soil depth In addition, the concentration of both metals slightly decreased with increasing distance from the factory to the surface soils. Cadmium and Zn were detected in the upward stream water with low concentration and concentrations increased significantly in the downstream after passing across the factory. Aerosol samples also showed traces of Cd and Zn which could be attributed to the contamination of the water system and the surface soils. Conclusively, Cd and Zn emitted from the ${\triangle}{\triangle}$ smelting factory moved with the aerosol in the atmosphere and thus, contaminated the agricultural areas and the water system within it vicinity.

• Journal of Convergence for Information Technology
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• v.8 no.2
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• pp.233-238
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• 2018

SMEs and small enterprises are making various attempts to manage SMEs in terms of equipment, safety and energy management as well as production management. However, SMEs do not have the investment capacity and it is not easy to build a smart factory to improve management and productivity of SMEs. In this paper, we propose a smart factory construction algorithm that partially integrates the factory equipment currently operated by SMEs. The proposed algorithm supports collection, storage, management and processing of product information and release information through IoT device during the whole manufacturing process so that SMEs' smart factory environment can be constructed and operated in stages. In addition, the proposed algorithm is characterized in that central server manages authentication information between devices to automate the linkage between IoT devices regardless of the number of IoT devices. As a result of the performance evaluation, the proposed algorithm obtained 13.7% improvement in the factory process and efficiency before building the Smart Factory environment, and 19.8% improvement in the processing time in the factory. Also, the cost of input of manpower into process process was reduced by 37.1%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.43-57
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• 2021

Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.

• The Journal of Information Systems
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• v.31 no.1
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• pp.197-218
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• 2022

Purpose This study examines the roles of firm-level smart factory implementation in the relationship between organizational competence and organizational performance in the context of Korean small and medium Enterprises (SMEs). To achieve this goal, this study presents and empirically tests a research model with evaluation data conducted by industrial experts on how organizational competence can be exploited to positively influence organizational performance through smart factory implementation. Design/methodology/approach Organizational competence are based on the research construct developed by Odważny et al.(2018). Research constructs on smart factory are based on the measurement model developed by Korea Technology and Information Promotion Agency for Korea small and medium Enterprises (TIPA) (2020) and organizational performance are based on the performance construct developed by Kwon(2019). To complete the investigation, we collected 31 firm data conducted by industrial experts in Korea from Dec 2018 to Dec 2020. Most of firm was implemented officially by government budget granted for smart factory of Korea SMEs. To test our hypotheses, partial least squares (PLS) method was employed. Findings The findings indicate that organizational competence is antecedent to influence smart factory implementation, while smart factory implementation has significant relationship with organizational performance. This study provides a better understanding of the connection between organizational competence and organizational performance through smart factory implementation. So companies should focus on enhancing organizational competence and implementing smart factory to obtain sustainable competitiveness.

• Korean Journal of Environmental Agriculture
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• v.16 no.2
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• pp.161-165
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• 1997

This experiment were carried out to clarify the effects of several factory wastes on the emergence and seedling growth of five crop species, rice, Chinese cabbage, melon, and tomato. Wastes of three factories treated by several concentrations on the soil in which crop were seeded. In rice seedling experiments, the rice seedlings were treated with factory wastes hydroponically. Factory wastes used in the experiment were obtained from leather, phenol resin, and dye factory. The growth of rice seedlings was inhibited by each factory wastes, but the dry weight of rice seedling was increased by the low concentration below 1/16 dilution of leather factory waste. During 15 days, dry matter accumulation of rice seedlings treated with undiluted factory wastes decreased to 46.0, 51.4, -5.4% of control by treating wastes of phenol resin, leather, and dye factory respectively. The injury of crops by leather factory waste was severe in tomato but slight in barley. Waste of phenol resin factory affects highly both on Chinese cabbage and on melon. When dye factory waste was treated on each crop, all plants died in the treatments of waste solution which diluted to 1/8 of original waste. Tomato and melon were most sensitive crop species to the waste of dye factory. Although the responses of crops to each factory waste were various, the degree of injuries were more higher in vegetables than cereal crops.

• IE interfaces
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• v.21 no.1
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• pp.131-140
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• 2008

To shorten product development time and cost, integrated information managements of product, manufacturing process and resource are essential. In the area of material addressing, process engineers should make their decisions in the manner of collaborative engineering in order to reduce the manufacturing preparation time and cost in the product development and production. A digital virtual factory which is an united digital model of entire factory could be very useful for these areas. In this paper, the digital virtual factory is constructed and used for material addressing and analysis of an automotive general assembly shop. We developed the material addressing system for automotive general assembly shops using digital virtual factory models and nesting algorithms, and applied it to realistic problems of a Korean automotive company as an convenient and effective way of material addressing.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.586-592
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• 2000

There is currently no satisfactory way to optimize supplemental lighting in a greenhouse-type plant factory especially concerning plant production. In a commercial plant factory, we got outside radiation data, inside radiation data and lamp running data. They have a correlation, but have much disorder. By using regression, tendency between the outside and the inside including supplemental lighting was found. We could estimate the average transmittance of this plant factory. From this estimation, we could admit the amount of inside radiation was supplied as much supplied compared to natural radiation. Then we are trying to investigate of the production amount and the supplemental lighting. Plant factory is environmentally controlled, the temperature and humidity are not actually controlled stable. We propose a design of neural network model could be useful to estimate the profit resulting from the operation of supplemental lighting.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.960-964
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• 2005

Digital Virtual Manufacturing is a technology to facilitate effective product developments and agile productions by digital models representing the physical and logical schema and the behavior of real manufacturing systems including products, process, manufacturing resources and plants. 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 of the shipbuilding company's section steel shop by 3-D CAD and virtual manufacturing simulation. This digital virtual factory can be applied for diverse engineering activities in design, manufacturing and control of the real factory.