• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.219-227
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• 2020

Smart Factory is one of the fastest developing and changing fourth industrial revolution fields. In particular, the degree of introduction and maturity level in the smart factory is an important part. In this paper, a cluster analysis of companies introduced smart factory was performed based on a new maturity assessment model. The 68% of 193 companies surveyed were at the basic level, with only 21% being the middle one. Most SMEs cited lack of funds as the main reason for not entering the middle one. As a result of the cluster analysis, it was found that all clusters had similar patterns but grouped into one of three levels of high, middle, and low depending on maturity level of smart factory operation, and process domain had the highest maturity and data domain was lowest among the 6 domains. Through this, analysis of more specific and quantified maturity levels can be performed using 6-domain smart factory maturity evaluation model.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.982-986
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• 2005

Digital Manufacturing is a technology to facilitate effective product developments and agile productions by digital environments representing the physical and logical schema and the behavior of real manufacturing system including manufacturing resources, processes and products. A digital virtual factory as a well-designed and integrated environment is essential for successful applications of this technology. In this research, we constructed a sophisticated digital virtual factory by measuring and 3-D CAD modeling using parametric methods. Specific parameters of each objects were decided by object-oriented schema of the digital factory. It is expected that this method is very useful for constructions of a digital factory, and helps to manage diverse information and re-use 3D models.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.1
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• pp.47-52
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• 2000

This study objectively in examines materials related to factory rationalization of D Corp., a regionally based enterprise. One reason that previous factory rationalizations have not been all that effective is that each firm has not used strategies specially designed for it Despite the fact that each firm has a different culture, and different human and physical resources, the application of rationalization without any modifications has produced many problems. In order to stabilize the production system and reduce the capacity of the factory, D Corp. changed the basic 5 S's and stimulated the factory atmosphere through computer education. Rationalization stabilized and standardized the factory, and organized the physical resources and each area of the factory according to their place in the process of production. It also made improvements that verified the party responsible for the flow of the complex production system, and simplified analysis supervision of production, and ex post management. We think that the successful example of D Corp. can serve as a real, tangible model for small and medium regionally-based firms to follow.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.133-152
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• 2017

Smart factory has been regarded as a big opportunity for manufacturing industries. However, little literature has been studied for the current status of Korean smart factory. Our paper tries to find gaps between research and real world by summarizing the recent literature and cases in Korean context. As the present level of smart factory introductions in Korean small manufacturing firms is lower than what a variety of literature says, our study points out that more efforts, investments and government support are required to catch up with the knowhow and technologies of developed countries although real-time control, enhanced productivity have been obtained. In future research, we will continue the smart factory study with the accumulated real data.

• 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.