• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.355-362
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• 2021

Through the Korean version of the New Deal 2.0, manufacturing-oriented SMEs are facing a new environmental change called smart factory construction. In addition, SMEs are facing new security threats along with a contactless environment due to COVID-19. However, it is practically impossible to apply the previously researched and developed security management system to protect the core technology of manufacturing-oriented SMEs due to the lack of economic capacity of SMEs. Therefore, through research on security management systems suitable for SMEs, it is necessary to strengthen their business competitiveness and ensure sustainability through proactive responses to security threats faced by SMEs. The security management system presented in this study is a security management system to prevent technology leakage applicable to SMEs by deriving and reflecting the minimum security requirements in consideration of technology protection point of view, smart factory, and remote access in a non-contact environment. It is also designed in a modular form. The proposed security management system is standardized and can be selectively used by SMEs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.444-446
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• 2022

In this study, technology is rapidly developing due to the fourth industry, which is drawing attention these days, and the pace of change in today's industries is changing in an instant. Among them, the study compares the various systems of smart factory, a manufacturing industry that requires customized production as workers are gradually decreasing due to the aging population and consumers' needs are diversified and diversified.

• Journal of the Korea Safety Management & Science
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• v.26 no.2
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• pp.117-125
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• 2024

The objective of this study is to identify the priority of elements for effective implementation of MES in small and medium-sized manufacturing enterprises trying to develop into smart factories. For this purpose, the Delphi method and the Analytic Hierarchy Process(AHP) mothod are applied. As a result of the study, the cooperation of the members in the supply chain is the most important factor for small and medium-sized enterprises in order to survive in the global competitive environment. Therefore, the enterprises need to make various efforts to create synergies through the technical strength of suppliers and the cooperation in the process of introducing and operating MES.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.97-110
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• 2018

With the advent of the 4-th industrial revolution, manufacturing companies have increasing interests in the realization of smart manufacturing by utilizing their accumulated facilities data. However, most previous research dealt with the structured data such as sensor signals, and only a little focused on the unstructured data such as text, which actually comprises a large portion of the accumulated data. Therefore, we propose an association rule mining based facility error pattern extraction framework, where text data written by operators are analyzed. Specifically, phrases were extracted and utilized as a unit for text data analysis since a word, which normally used as a unit for text data analysis, is unable to deliver the technical meanings of facility errors. Performances of the proposed framework were evaluated by addressing a real-world case, and it is expected that the productivity of manufacturing companies will be enhanced by adopting the proposed framework.

• The Journal of Bigdata
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• v.3 no.1
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• pp.95-103
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• 2018

In recent years, many people in the manufacturing field have been making efforts to increase efficiency while analyzing manufacturing data generated in the process according to the development of ICT technology. In this study, we propose a data mining based manufacturing process using decision tree algorithm (CHAID) as part of a smart factory. We used 432 sensor data from actual manufacturing plant collected for about 5 months to find out the variables that show a significant difference between the stable process period with low defect rate and the unstable process period with high defect rate. We set the range of the stable value of the variable to determine whether the selected final variable actually has an effect on the defect rate improvement. In addition, we measured the effect of the defect rate improvement by adjusting the process set-point so that the sensor did not deviate from the stable value range in the 14 day process. Through this, we expect to be able to provide empirical guidelines to improve the defect rate by utilizing and analyzing the process sensor data generated in the manufacturing industry.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.216-221
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• 2022

The 4th industrial revolution is accelerating the transition to digital innovation in various aspects of our daily lives, and efforts for manufacturing innovation are continuing in the manufacturing industry, such as smart factories. The 4th industrial revolution technology in manufacturing can be used based on AI, big data, IoT, cloud, and robots. Through this, it is required to develop a technology to establish a production facility data collection and analysis system that has evolved from the existing automation and to find the cause of defects and minimize the defect rate. In this paper, we implemented a system that collects power, environment, and status data from production facility sites through IoT devices, quantifies them in real-time in a cloud computing environment, and displays them in the form of MQTT-based real-time infographics using widgets. The real-time sensor data transmitted from the IoT device is stored to the cloud server through a Rest API method. In addition, the administrator could remotely monitor the data on the dashboard and analyze it hourly and daily.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.165-166
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• 2020

Internationally, manufacturers attempted respectable portion of in-house logistics to satisfy end users and decrease manpower to compete for manufacturing price and quality optimization. Mostly, manufacturers operate variety of facilities such as collaborative robots, conveyor, etc. based on PLC. To achieve it, manufactures shall operate the optimized number of manufacturing processes with logic controlled by computer to reduce human errors. In prior to it, manufacturing industry still own plenty of fields which have not yet been adjusted with automation. For example, we shall put in-house logistics on the issue. This study focuses on manufacturing industry, evaluate efficiency, costs, etc. in all aspects and suggest alternatives by analysis SWAT and OEE, let alone reason of weakness.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.42-48
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• 2022

Currently, the world is facing severe inflation due to Corona and the war in Ukraine, and it is causing a lot of difficulties for us. Companies are facing a lot of restrictions on their economic activities compared to the past due to supply chain problems and foreign exchange rates. In this situation, many countries have been implementing various smart factory promotion projects to secure competitiveness through productivity improvement in the manufacturing industry. In this study, the contents of smart factory promotion in major countries were reviewed, and problems raised about the implementation of smart factory in Korea, which are being implemented based on this, were described. It is most reasonable to judge the success of a smart factory by the achievement of the performance indicators presented at the time of the project. Therefore, based on the performance index of the business, which is a key factor in determining the success or failure of a smart factory, we investigated whether the company's smart factory promotion can be carried out successfully through examples

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.295-302
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• 2022

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learning-based prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.

• Smart Media Journal
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• v.12 no.1
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• pp.54-61
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• 2023

Pricing, as one of the most important aspects of a business, should be taken seriously. Whatever affects a company's pricing system tends to affect its profits and losses as well. Currently, many manufacturing companies fix product prices manually by members of an organization's management team. However, due to the imperfect nature of humans, an extremely low or high price may be fixed, which is detrimental to the company in either case. This paper proposes the development of a fuzzy-based price expert system (Expert Fuzzy Price (EFP)) for manufacturing companies. This system will be able to recommend appropriate prices for products in manufacturing companies based on four major pricing strategic goals, namely: Product Demand, Price Skimming, Competition Price, and Target population.