• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.57-64
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• 2024

The introduction of Smart Factories has transformed manufacturing towards more objective and efficient line management. However, most companies are not effectively utilizing the vast amount of sensor data collected every second. This study aims to use this data to predict product quality and manage production processes efficiently. Due to security issues, specific sensor data could not be verified, so semiconductor process-related training data from the "SAMSUNG SDS Brightics AI" site was used. Data preprocessing, including removing missing values, outliers, scaling, and feature elimination, was crucial for optimal sensor data. Oversampling was used to balance the imbalanced training dataset. The SVM (rbf) model achieved high performance (Accuracy: 97.07%, GM: 96.61%), surpassing the MLP model implemented by "SAMSUNG SDS Brightics AI". This research can be applied to various topics, such as predicting component lifecycles and process conditions.

• Journal of Powder Materials
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• v.29 no.2
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• pp.166-175
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• 2022

Ppuri or Root technology primarily includes technologies such as casting, mold, plastic working, welding, heat treatment and surface treatment. It is regarded as an essential element for improving the competitiveness of the quality of final products. This study investigates the current status of smart factory implementation for Ppuri companies and analyzes the influencing relationships among various company factors. The factors affecting smart factory implementation for Ppuri companies are sales, exports, number of technical employees, and holding corporate research institutes. In addition, this research shows that even if smart factory implementation is pursued for data collection, data utilization is not implemented properly. Thus, it is suggested that the implementation of smart factories requires not only the availability of facilities and systems but also proper data utilization.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.321-326
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• 2023

The purpose of this study is to create and present a formalized module of ISP (Information Strategy Plan), a smart factory consulting method that is helpful to companies or consultants who will build smart factories. Order of study is First, the theoretical research direction is established through the investigation of related papers. Second, ISP policy research practices are compared to derive practical implementation methods. Third, in order to derive a standardized module method related to the final smart factory ISP, related cases of the government and individuals are compared. As a result of previous research, ISP (Information Strategy Planning), a consulting methodology, is similar to Deming's PDCA, and is regarded as Plan (environment and current status analysis), Do (establishment of future model goals), Check (establishment of implementation plan), and Act (follow-up management). As a result of the study, we obtained the following results. The first step is to analyze the current status and identify the purpose of introduction and problems in plant operation. In the second step, establish a consulting plan and derive a proposal description, strategic task, and master plan. Step 3 establishes detailed action plans, evaluates consulting outputs and consulting, and reports performance. Step 4 is established as follow-up management consulting. The limitation of the study is that although related data were compared to develop the consulting methodology into a standardized module, FGI analysis through experts or Delphi survey were not conducted, so there is a limit to the reliability of the mapping results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.1-17
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• 2022

The purpose of this study is to suggest a plan to improve the level of acceptance of related technologies and the transition to smart factories of small and medium-sized manufacturing enterprises by using 'technology readiness' and 'integrated technology acceptance model'. To this end, the research hypothesis was verified by collecting questionnaire data from 130 small and medium-sized manufacturing companies in Korea and conducting path analysis. First, optimism affects performance expectations, social influence, and facilitation conditions, innovation affects performance expectations, effort expectations, and social influence, discomfort affects performance expectations, social influence, and facilitation conditions, and anxiety affects effort expectations, social influence and facilitation conditions. has been proven to affect Finally, performance expectations, effort expectations, social influence, and facilitation conditions were verified to have a significant positive effect on the intention to accept technology.

• 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.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.870-882
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• 2020

It is that failure of equipment at the factory site causes personal injury and property damage. We are required a real-time monitoring and risk forecasting techniques to prevent for equipment failure. In this paper, we proposed a 3-phase smart plug and real-time monitoring system that can be used in factories, and collected environmental information and power information using a smart plug to analyze the data. In order to analyze the correlation between the risk situation and the collected data, we predicted the risk situation using Linear Regression, SVM, and ANN algorithms. As a result, the SVM and ANN algorithms obtained high predictive accuracy and developed a mobile app that could use it to check the risk forecast results.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.2
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• pp.85-91
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• 2015

With the increasing popularity of Smart Grid infrastructure, it is much easier to collect energy usage data using AMI (Advanced Measuring Instrument) from residential housing, buildings and factories. Several researches have been done to improve an energy efficiency by analyzing the collected energy usage data. However, it is not easy to store and analyze the energy data using a traditional relational database management system since the data size grows exponentially with an increasing popularity of Smart grid infrastructure. In this paper, we are proposing a Hadoop based Big data system to store and analyze energy usage data. Based on our limited experiments, Hadoop based energy data analysis is three times faster than that of a relational database management system based approach with the current system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.66-68
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• 2022

Forklift trucks are frequently used in logistics warehouses and factories to transport large amounts of cargo. Among the recent accidents at industrial sites, the number of accidents caused by forklifts was the highest. Most accidents are caused by being hit or caught by forklifts. Various safety devices are being applied to prevent forklift accidents. This paper analyzes the efficiency of introducing a smart control method to prevent industrial accidents that may occur in the operation of forklifts. The smart control method generates an amount of money for initial introduction, but if subsidies are secured through government-supported projects, the burden on the workplace will be reduced. When a smart control method is introduced, it is possible to prevent collisions between workers and forklifts and accidents of trapped forklifts.

• The Journal of Information Systems
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• v.32 no.3
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• pp.205-224
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• 2023

Purpose This study investigates the impact of organizational characteristics on organizational performance through case studies of smart factory implementation in the context of Korean small and medium Enterprises (SMEs). To achieve this goal, this study adopts the smart factory index of KOSMO (Korea Smart Manufacturing Office) established by Korean Ministry of SMEs and Startups. We visited 3 firms implemented smart factory projects. This study presents the results of field study in detail with evaluation criteria on how organizational competences like AI technology adoption and facility automation can be exploited to positively influence organizational performance through smart factory implementation. Design/methodology/approach There are not so many results of empirical studies related to smart factories in Korea. This is because organizational support and user involvement are required for facility AI platform service beyond factory automation after the start of the 4th Industrial Revolution. Korean government's KOSMO (Korean Smart Manufacturing Office) has developed and proposed a level measurement index for smart factory implementation. This study conducts case studies based on the level measurement method proposed by KOSMO in the process of conducting case studies of three companies belonging to the root and mechanic industries in Korea. Findings The findings indicate that organizational competences, such as facility AI platform adoption and user involvement, are antecedents 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 competences and organizational performance through smart factory case studies. This study suggests that SMEs should focus on enhancing their organizational competences for improving organizational performance through implementing smart factory projects.

• Journal of Science and Technology Studies
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• v.18 no.2
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• pp.269-306
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• 2018

Through this study, it is aimed to derive the policy direction considering the characteristics of the present Smart Factory, the industrial condition of Pohang area, and the promotion field. Secondly, the questionnaire data of the regional enterprises will prepare for the improvement of the industrial structure and the implications for efficiency, and preparation for regional preparation and industrial changes in preparation for the next generation of production revolution. The construction of Smart Factory in Pohang can be divided into two major directions. First, it is analyzed that smart factory pilot projects are highly needed, focusing on competitive medical precision manufacturing field among the SMEs in the region, primary metal and nonmetal manufacturing industries, and other machinery fields. In addition, local SMEs are willing to introduce smart factories for reasons of quality improvement and cost reduction, and it is confirmed that they will actively promote employee training and expertise if they can upgrade continuously.