• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.471-476
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• 1995

This paper focues on development and implementation of a perfomance management algorithm for IEEE802.4 token bus networks to serve large-scale integrated manufacturing systems. Such factory automation networks have to satisfy delay constraints imposed on time-critical messages while maintaining as much network capacity as possible for non-time-critical messages. This paper presents the structure of a network performance manager that possesses the knowledge about perfomance management in a set of fuzzy rules and deriving its action through fuzzy inference mechanism. The efficacy of the performance management has been demonstrated by a series of simulation experiments.

• Journal of the Korea Safety Management & Science
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• v.16 no.3
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• pp.377-388
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• 2014

There have been a lot of studies about overseas factory location in order to meet various needs of industries according to the international economic developments. However, most of the studies were written about generic theory for general industries or mainly concerned to high capital industries. This study is focusing on the sewing and apparel industries which represent labor intensive and small-medium type of enterprises. For this study, AHP(Analytic Hierarchy Process) methods were applied to make proper analysis after wide range of survey to clarify determinants which provide a guidance for overseas factory location. As a result of the analytical researches done in the thesis the most important which should be taken consideration while determining of overseas factory location for sewing and apparel products industry are as follows - economic factors(0.569), geographic factors(0.171), social and cultural factors(0.157), regulations and institutional factors(0.103). In the last, S company is examined for example to show how this determinants have practically been applied.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1516-1529
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• 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.

• 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 for Quality Management
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• v.36 no.2
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• pp.28-35
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• 2008

This study deals with an Excel-based scheduling system for a small and medium sized manufacturing factory without sufficient capability for managing full-scale information systems. The factory has the bottleneck with identical machines and unique batching characteristics. The scheduling problem is formulated as a variation of the parallel-machine scheduling system. It can be solved by a two-phase method: the first phase with an ant colony optimization (ACO) heuristic for order grouping and the second phase with a mixed integer programming (MIP) algorithm for scheduling groups on machines.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.98-102
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• 2002

High-Tech 산업은 국제적인 경쟁력과 갈수록 짧아지는 제품의 Life-cycle로 인해 그 경쟁이 매우 치열해 지고 있다. 따라서. 변동되는 시장 상황에 맞추어 자재의 구매/조달, 연구 개발, 생산, 물류/배송, 판매, 및 고객에 대한 납기 약속 등을 신속하고 정확하게 처리하기 위해 전 세계적으로 SCM(Supply Chain Management)관점에서 APS(Advanced Planning and Scheduling)시스템을 사영하고 있다. 특히, 고객으로부터 주문량이 할당되면 주어진 자재와 공장의 생산능력 (capacity)을 계산하여 생산가능 유무 및 생산가능일을 알려주는 공장내의 생산 계획 및 스케줄링 방법은 현재는 물론 앞으로도 지속적으로 발전시켜야 될 부분으로서 그 중요성이 점점 확대되고 있다. 따라서, 본 연구에서는 High-Tech 산업 중에서 전자 산업의 요구 사항을 분석하고, 이러한 요구 사항들을 해결하기 위래 세계적으로SCM 시장에서 가장 많은 경쟁력을 가지고 있는 i2 Technologies사의 생산 계획 및 스케줄링 툴인 Factory Planner를 사용한 사례를 제시한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.3C
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• pp.233-240
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• 2012

Unlike traditional factory environment, in an industrial factory network applied wireless sensor network technologies, all procedures of discovery, identification and verification of devices should be performed in an automatic fashion. To address these challenges, we design a management system using the device registry server that we propose in this paper. In the phase of device discovery, the proposed system utilizes properties of routing protocol running in factories. Also, in the phase of identification and verification, the system uses unique and general information of a device stored within the device registration server. Such a way allows management system to reduce implementation complexity and to easily manage devices in a factory applied with a wireless network consisting of heterogeneous devices.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.228-238
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• 2009

This paper first presents two supply chain design problems: 1) a factory location setting and factory selection problem, and 2) a factory location setting and factory/supplier selection problem. The first involves a number of location known retailers choosing one factory to supply their demands from a number of factories whose locations are to be determined. The goal is to minimize the transportation and manufacturing cost to satisfy the demands. The problem is then augmented into the second problem, where the procurement cost of the raw materials from a chosen material supplier (from a number of suppliers) is considered for each factory. Economic beneficial is taken into account in the cost evaluation. Therefore, the partner selections will influence the cost of the supply chain significantly. To solve these problems, an agent gaming and genetic algorithm hybrid method (AGGAHM) is proposed. The AGGAHM consecutively and alternatively enable and disable the advancement of agent gaming and the evolution of genetic computation. Computation results on solving a number of examples by the AGGAHM were compared with those from methods of a general genetic algorithm and a mutual frozen genetic algorithm. Results showed that the AGGAHM outperforms the methods solely using genetic algorithms. In addition, various parameter settings are tested and discussed to facilitate the supply chain designs.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.246-252
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• 2020

Facial recognition is a biometric technology that is used in various fields such as user authentication and identification of human characteristics. Face recognition applications are practically used in various fields, but very few applications have been developed to improve the factory work environment. We implemented applications that uses face recognition to identify a specific employee in a factory .work environment and provide customized information for each employee. Factory workers need documents describing the work in order to do their assigned work. Factory managers can use our application to register documents needed for each worker, and workers can view the documents assigned to them. Each worker is identified using face recognition, and by tracking the worker's face during work, it is possible to know that the worker is in the workplace. In addition, as a mobile app for workers is provided, workers can view the contents using a tablet, and we have defined a simple communication protocol to exchange information between our applications. We demonstrated the applications in a factory work environment and found several improvements were required for practical use. We expect these results can be used to improve factory work environments.

• Information Systems Review
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• v.25 no.3
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• pp.199-220
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• 2023

Projects to deploy and diffuse smart factories in South Korea are aimed at enhancing national manufacturing competitiveness. However, a significant portion of deployed companies remain at the basic stage and struggle to utilize smart factories regularly. Existing studies have primarily focused on the technical aspects of smart factories, using data analytics and case studies, leading to a gap in empirical research on continuous use and upgrade intentions. This study identifies key factors influencing smart factory usage and user satisfaction, drawing on the Information Systems Success Model (ISSM) and previous research. It empirically examines the impact of these factors on continuous use intention, management performance, and advancement acceptance intention through smart factory usage and user satisfaction. A structural equation model is employed to validate the research hypotheses, using survey data from 287 small and medium-sized manufacturing enterprises (SMEs) that have adopted smart factories. Results demonstrate that system quality, information quality, service quality, and government support significantly affect smart factory usage, while service quality and government support influence user satisfaction. Furthermore, smart factory usage and user satisfaction have positive effects on management performance, continuous use intention, and subsequently advancement acceptance intention. This study provides novel insights by demonstrating the specific impact mechanisms of smart factory user satisfaction on the business and the intentions of manufacturing SMEs regarding continuous use and advancement acceptance, leveraging the ISSM.