• International Journal of Internet, Broadcasting and Communication
• /
• 제16권1호
• /
• pp.269-279
• /
• 2024

The study explores the evolving landscape of overseas expansion strategies by Korean corporations, focusing on recent geopolitical tensions, the COVID-19 pandemic, and disruptions in global supply chains. It emphasizes the challenges faced by industries producing high-value products and delves into the concept of "Friend-Shoring" policies in the United States, leading major Korean companies to invest in local semiconductor, battery, and automotive factories. Recognizing the potential fragmentation of Korea's manufacturing sector, the paper introduces the "Mother Factory" strategy as a policy initiative, inspired by Japan's model, to establish core production facilities domestically. The discussion unfolds by examining the cases of major companies in Japan and the United States, highlighting the need for Korea to adopt a mother factory strategy to mitigate risks associated with friend-shoring policies. Inspired by Intel's "Copy Exactly" approach, the paper proposes a Korean mother factory model integrating smart factory technology and digital twin systems. This strategic shift aims to enhance responsiveness to geopolitical challenges and fortify the competitiveness of Korean high-tech industries. Finally, the paper proposes a Korean Mother Factory based on smart factory concepts. The suggested model integrates smart factory technology and digital twin frameworks to enhance responsiveness and fortify competitiveness. In conclusion, the paper advocates for the adoption of a comprehensive Korean Mother Factory model to address contemporary challenges, foster advanced manufacturing, and ensure the sustainability and competitiveness of Korean high-tech industries in the global landscape. The proposed strategy aligns with the evolving dynamics of the manufacturing sector and emphasizes technological advancements, collaboration, and strategic realignment.

• Journal of Information Processing Systems
• /
• 제17권6호
• /
• pp.1071-1082
• /
• 2021

Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models.

• 한국정보전자통신기술학회논문지
• /
• 제11권1호
• /
• pp.70-75
• /
• 2018

스마트 팩토리는 설계 개발, 제조, 유통 물류 등 생산 전체 과정에 정보 통신 기술을 적용하여 생산성, 품질, 고객만족도 등을 향상시킬 수 있는 지능형 공장이다. 스마트 팩토리에서 발생되는 데이터의 양은 공장의 규모 및 시설 수준에 따라 많은 차이를 보이지만, 기존의 생산관리시스템을 활용하여 방대한 양의 데이터를 발생시키는 스마트 팩토리 환경에 적용하기에 어려움이 있다. 이로 인해 방대한 양의 빅데이터 처리할 수 있는 빅데이터 분산 처리 시스템의 필요성이 요구되고 있다. 따라서 본 논문에서는 스마트 팩토리 환경에서의 GlusterFS 기반 빅데이터 분산 처리 시스템 설계하였다. 제안하는 빅데이터 분산 처리 시스템은 기존 분산 처리 시스템에 비해 네트워크 트래픽 분산 및 관리를 통해 부하와 데이터 소실 위험도를 감소시켰다.

• 한국멀티미디어학회논문지
• /
• 제19권8호
• /
• pp.1516-1529
• /
• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• 한국인터넷방송통신학회논문지
• /
• 제21권1호
• /
• pp.131-139
• /
• 2021

스마트팩토리는 4차 산업혁명에서 가장 대두되는 분야라고 말할 수 있다. 스마트팩토리에 필요한 공정이나 알고리즘을 개발하는 데에는 필수적으로 스마트팩토리에서 나오는 데이터값들이 필요하지만 그러한 데이터를 얻기 위해서는 현실적으로 많은 어려움이 있다. 따라서 본 연구에서는 스마트팩토리에 관한 연구를 돕기 위해 스마트팩토리 속 여러 공정의 데이터들을 보다 현실적으로 시뮬레이션할 수 있는 데이터 생성기를 개발하였다. 추가로, 설정 프리셋과 직관적인 UI 구성 등, 데이터 생성자의 편의를 위한 기능들도 개발하였다. 이 데이터 생성기를 이용함으로써, 스마트팩토리 환경에 필요한 여러 시스템을 만들 때 현실적인 데이터를 손쉽고 간단하게 생성하여 스마트팩토리 환경을 시뮬레이션하는 데 많은 도움을 받을 수 있을 것이다.

• International Journal of Internet, Broadcasting and Communication
• /
• 제13권3호
• /
• pp.148-154
• /
• 2021

We propose the AI Smart Factory Model for integrated management of production processes in this paper .It is an integrated platform system for the production of food packaging containers, consisting of a platform system for the main producer, one or more production partner platform systems, and one or more raw material partner platform systems while each subsystem of the three systems consists of an integrated storage server platform that can be expanded infinitely with flexible systems that can extend client PCs and main servers according to size and integrated management of overall raw materials and production-related information. The hardware collects production site information in real time by using various equipment such as PLCs, on-site PCs, barcode printers, and wireless APs at the production site. MES and e-SCM data are stored in the cloud database server to ensure security and high availability of data, and accumulated as big data. It was built based on the project focused on dissemination and diffusion of the smart factory construction, advancement, and easy maintenance system promoted by the Ministry of SMEs and Startups to enhance the competitiveness of small and medium-sized enterprises (SMEs) manufacturing sites while we plan to propose this model in the paper to state funding projects for SMEs.

• Journal of Information Processing Systems
• /
• 제15권1호
• /
• pp.181-188
• /
• 2019

A production system is a management system that supports all activities to perform production operations at the manufacturing site. From the point-of-view of a smart factory, smart manufacturing systems redesigned the concept of onsite production systems to fit the entire system and its necessary functional composition. In this study, we select the key functions needed to build a smart factory for a PCB line and propose a new six-step model for the deployment of a smart manufacturing system by integrating essential functions. The smart manufacturing system newly classified the production and operation tasks of PCB manufacturing and selected necessary functions through requirement analysis and benchmarking of advanced companies. The selected production operation tasks are mapped to the functions of the system and configured into seven modules, and the optimal deployment model is presented to allow flexible responses to the characteristics of the tasks. These methodologies are first presented in this study, and the proposed model was applied to the PCB line to confirm that they had significant changes in the work method, qualitative effects, and quantitative effects. Typically, lead time and WIP have reduced by about 50%.

• International journal of advanced smart convergence
• /
• 제12권2호
• /
• pp.173-181
• /
• 2023

Data collection is an essential element in the construction and operation of a smart factory. The quality of data collection is greatly influenced by network conditions, and existing wireless network systems for IoT inevitably lose data due to wireless signal strength. This data loss has contributed to increased system instability due to misinformation based on incorrect data. In this study, I designed a distributed MQTT IoT smart sensor and gateway structure that supports wireless multicasting for smooth sensor data collection. Through this, it was possible to derive significant results in the service latency and data loss rate of packets even in a wireless environment, unlike the MQTT QoS-based system. Therefore, through this study, it will be possible to implement a data collection management system optimized for the domestic smart factory manufacturing environment that can prevent data loss and delay due to abnormal data generation and minimize the input of management personnel.

• 대한산업공학회지
• /
• 제43권3호
• /
• pp.229-237
• /
• 2017

Presented in this paper is a case study of constructing a smart factory for aircraft parts. The construction procedure involves four phases. First of all, its management goals are set, and layout design and simulation are carried out in the conceptual design phase. In the detail design phase, operating scenarios for each module are written out, and probable risks are analyzed by expert groups, and then requirements for developing equipments and subsystems are determined with consideration for element technologies and their integration schemes into the smart factory. In the fabrication and installation phase, system development, equipment fabrication and installation are proceeded in a separate manner, and then integrated together subsequently. In the operation and improvement phase, the factory is stabilized, sophisticated and improved constantly during real operation.

• 한국정보시스템학회지:정보시스템연구
• /
• 제27권4호
• /
• pp.35-70
• /
• 2018

Purpose Recently, 3D factory simulation technology has emeged as a powerful tool for modeling and analysis of a wide range of production systems, however, it has been not paid much attention in Korea. In this context, this paper aims to provide a comprehensive literature review on discrete event simulation softwares and introduce a promising 3D factory simulation software called FlexSim and its application domains. Design/methodology/approach In order to demonstrate worldwide popularity and technical superiority of FlexSim software, we analyzed the recent list of rankings for commercial discrete simulation softwares released by winter simulation conference and users' opinions collected from business software review site. Moreover, several main application domains are derived from a review of the previous research papers that deal with applications of FlexSim software. Findings FlexSim software recently moved up the list of major commercial simulation softwares, and technical superiorities of the software demonstrate that it is a promising tool for practical 3D factory simulation. Moreover, recent research papers suggest that FlexSim software can be used as a component of smart factory system. In this context, it is expected that FlexSim software becomes more popular in the era of industry 4.0.