• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.197-207
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• 2018

SMEs manufacturing Factory, which are small-scale production systems of various types, mass-produce and sell products in order to meet customer needs. This means that the company has an excessive amount of material supply to reduce the loss due to lack of inventory and high inventory maintenance cost. And the products that fail to respond to the demand are piled up in the management warehouse, which is the reality that the storage cost is incurred. To overcome this problem, this paper uses ARIMA model, a time series analysis technique, to predict demand in terms of seasonal factors. In this way, demand forecasting model based on economic order quantity model was developed to prevent stock shortage risk. Simulation is carried out to evaluate the effectiveness of the development model and to demonstrate the effectiveness of the development model as applied to SMEs in the future.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.192-203
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• 2020

Remodeling is urgent as the number of closed factories in the industrial complex increases due to the long-term economic decline. Accordingly, the government is promoting the remodeling of a closed-up factory as one of the industrial complex restructuring projects underway to restore the competitiveness of aging industrial complexes. The purpose of this study in to analyze the feasibility of remodeling of 4 closed-up factory in an industrial complex. In details, this study analyzes the factories that need to be remodeled with the reasonable grounds, and suggests in what order and manner the remodeling should proceed. The data were collected from literature review and on site field survey. As a result of the study, the feasibility of remodeling was recognized in all four closed factories, but Factory A was found to require a more detailed rental attraction plan and management operation plan. The final conclusions were verified through working-level meetings and experts' advise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.471-477
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• 2021

In the field of display, a smart factory means more efficient display manufacturing using AI/BIG DATA technology not only for job automation, but also for existing process management, moving facilities, process abnormalities, and defect classification. In the past, when defects appeared in the display manufacturing process, the classification of defects and coping with process abnormalities were different, a lot of time was consumed for this. However, in the field of display manufacturing, advanced process equipment must be used, and it can be said that the competitiveness of the display manufacturing industry is to quickly identify the cause of defects and increase the yield. In this paper, we will summarize the cases in which smart factory AI/BIG DATA technology is applied to domestic display manufacturing, and analyze what advantages can be derived compared to existing methods. This information can be used as prior knowledge for improved smart factory development in the field of display manufacturing using AI/BIG DATA.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.1-18
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• 2021

The purpose of this study is to study the influencing factors that can affect the acceptance intention and acceptance behavior of the 4th Industrial Revolution and smart factory-related technologies by using the expanded UTAUT. Through this, by grasping which influencing factors affect the introduction and acceptance of related technologies, it is to derive strategies for responding to the fourth industrial revolution by manufacturing companies and accepting smart factory related technologies. A survey was conducted on various manufacturing companies, and 167 copies were used for research. As a result of the testing of research hypotheses, performance expectation, social impact, promotion conditions, network effect, and innovation have a positive (+) significant effect on acceptance intention. However, expectation of effort had a positive (+) effect on acceptance intention, but was not significant. Acceptance intention was tested to have a positive (+) significant effect on acceptance behavior. Therefore, factors that should be improved by individual manufacturing companies in the process of responding to the 4th industrial revolution and the introduction and acceptance of smart factory-related technologies are clearly presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1525-1530
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• 2021

By applying AI-based efficient inventory management and logistics optimization technology using the smart factory's production plan and manufacturing data, the company's productivity improvement and customer satisfaction can be expected to increase. In this paper, we proposed a system that collects data from the factory's production process, stores it in the cloud, and uses the manufacturing data stored there to apply AI-based supply chain optimization technology later. While the existing system supported approximately 10 to 20 data types, the proposed system is designed and developed to support more than 100 data types. In addition, in the case of the collection cycle, data can be collected 1-2 times per second, and data collection in TB units is possible. Therefore This system is designed to be applied to the existing factory of past in addition to the smart factory.

• Journal of the Korea Safety Management & Science
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• v.3 no.4
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• pp.103-112
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• 2001

In Factory, as the number of machine is increased the more maintenance efforts are necessary. Multi maintenance issues may occur at a certain time and the determination of maintenance sequence is needed. In this study, we first compare the priority of machines and the impact value using modified FMEA(Failure Mode Effect and Analysis) method. Also, CBR(Case-based Reasoning) approach is applied to retrieve similar fault cases of current machine problem. The proposed methodology will be useful to implement decision support system of maintenance sequence for CMMS/EAM (Computerized Maintenance Management System/Enterprise Asset Management).

• International conference on construction engineering and project management
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• 2015.10a
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• pp.608-611
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• 2015

The objectives of this research are 1) control of schedule or improvement of management for aerial environment, 2) distribution of responsibility to the parties concerned (factory, material company, construction company, design and engineering, occupancy). The results show the relative priority of the four major items in wall-based apartment buildings and in column-based apartment buildings. An analysis of the parties responsible for improvement based on the IAQ results shows more efforts to improve IAQ are needed in material factories and engineering/design companies.

• Management & Information Systems Review
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• v.38 no.1
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• pp.23-41
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• 2019

Smart Factory is different from existing factory automation in that it aims to produce personalized products with minimum time and cost through ICT. However, previous researches, not from consumers but from product suppliers, have focused on technology trends and technology application methods. In order for Smart Factory to be successful, it must go beyond supplier-focus to meet the needs of consumers. In this study, we surveyed the purchase intention of the personalized product manufactured by smart factory. Influencing factors of purchase intention were drawn as consumers' need for uniqueness, innovativeness, need for touch, and privacy concern, based on previous research. As results of data analysis, it was confirmed that respondents were willing to purchase personalized products, and that consumers' need for uniqueness, innovativeness, and need for touch had a significant impact on purchase intention of personalized products. Our findings can be summarized as follows. First, Consumers' need for uniqueness was found to have positive effects(${\beta}=0.168$) on purchase intention of personalized products. The desire to differentiate themselves from others will be reflected in their personalized products. Therefore, consumers with a higher desire for uniqueness tend to be more willing to purchase personalized products. Second, consumer innovativeness was found to have positive effects(${\beta}=0.233$) on purchase intention of personalized products. Personalized shoes suggested in this study is a new type of personalized product that is manufactured by the latest information and communication technologies such as multi-function robots and 3D printing. Therefore, consumers seeking innovative new experiences are more willing to purchase personalized products. Third, need for touch was found to have positive effects(${\beta}=0.299$) on purchase intention of personalized products. In a smart factory environment, prosuming participation is given to consumers. If consumers participate in the product development process and reflect their requirements on the product, they are expected to increase their purchase intention by virtually satisfying the need for touch. Fourth, privacy concern was found to have no significantly related to purchase intention of personalized products. This is interpreted as a willingness to tolerate the risk of exposing personal information such as home address, telephone number, body size, and preference for consumers who feel highly useful in personalized products.

• Journal of Engineering Education Research
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• v.3 no.1
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• pp.42-52
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• 2000

A Factory/Production Facilities Automation involves compound techniques as well as various applied engineering fields. Therefore, there exist lots of difficult problems related with its introduction, operation and management in small and medium scale companies. In this paper, we extract major contents for its effective traininng program reflected from industrial needs and propose a prototype model for the enhancement of its field capabilites.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1484-1493
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• 2014

Recently, Industry 4.0 (next generation industrial revolution) designed by Germany to retain initiative in manufacturing business is actively studied. Goal of Industry 4.0 is 'Smart factory' which manages progress of production, supply logistics and services. To achieve the goal, we can construct value creation and new business model by integrating organically with production management systems which is existing and cyber-physical systems, Internet of Things, Services Internet and sensor, etc. However, if integration with production management systems does not work effectively by adding and developing new technologies, It does not have performance. Hence, in this research, we will analysis Industry 4.0 which is possible for small quantity batch production and one of the light and flexible manufacturing systems, and based on this, we will suggest methodology to horizontally integrate with production management systems.