• Proceedings of the KSR Conference
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• 2005.05a
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• pp.193-198
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• 2005

According to the ISO 14040(1997), Life Cycle Assessment is not the tool only focusing on the emissions from the manufacturing processes of a product, but the tool also expressing environmental adverse impact quantitatively through products entire life cycle (i.e. raw material acquisition, manufacturing, transportation, use, and end-of-life stage). Because the LCA for EMUs(Electrical Multiple Units), however, requires astronomical time and cost for collecting big amount of data. it is inevitable to bring in the simplified LCA methodology, In this study, we introduced standardized methodology of LCA in the world, and found appropriate S-LCA methodology for EMUs. Furthermore, we recommended how to evaluate the environmental impact of EMUs in detail and precisely, using the S-LCA.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.221-234
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• 2013

In this paper, we report bankruptcy risk level forecasting result for automobile parts manufacturing industry. With the premise that upstream supply risk and downstream demand risk could impact on automobile parts industry bankruptcy level in advance, we draw upon industry input-output table to use the economic indicators which could reflect the extent of supply and demand risk of the automobile parts industry. To verify the validity of each economic indicator, we applied simple linear regression for each indicators by varying the time lag from one month (t-1) to 12 months (t-12). Finally, with the valid indicators obtained through the simple regressions, the composition of valid economic indicators are derived using stepwise linear regression. Using the monthly automobile parts industry bankruptcy frequency data accumulated during the 5 years, R-square values of the stepwise linear regression results are 68.7%, 91.5%, 85.3% for the 3, 6, 9 months time lag cases each respectively. The computational testing results verifies the effectiveness of our approach in forecasting bankruptcy risk forecasting of the automobile parts industry.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.1
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• pp.1-12
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• 2016

The current economic crisis is making new demands on manufacturing industry, in particular, in terms of the flexibility and efficiency of production processes. This requires production and administrative processes to be meshed with each other by means of IT systems to optimise the use and capacity utilisation of machines and lines but also to be able to respond rapidly to wrong developments in production and thus to minimise adverse impacts on the business. The future scenario of the "smart factory" represents the zenith of this development. The factory can be modified and expanded at will, combines all components from different manufacturers and enables them to take on context-related tasks autonomously. Integrated user interfaces will still be required at most for basic functionalities. The complex control operations will run wirelessly and ad hoc via mobile terminals such as PDAs or smartphones. The comnination of IoT, and Big Data optimisation is bringing about huge opportunities. these processes are not just limited to manufacturing, anywhere a supply chain environment exists can benefit from information provided by linked devices and access to big data to inform their decision support. Building a smart factory with smart assets at its core means reaching those desired new levels of productivity and efficiency. It means smart products that leverage advanced traceability, connectivity and intelligence. For businesses, it means being able to address the talent crunch through more autonomous. In a Smart Factory, machinery and equipment will have the ability to improve processes through self-optimization and autonomous decision-making.

• Journal of Service Research and Studies
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• v.8 no.1
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• pp.73-87
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• 2018

Countries around the world are preparing policies to promote service economy. Recently, as the fourth industrial revolution is accelerating, interest in the service industry is increasing. Korea's service industry is among the lowest among OECD countries in terms of employment, value-added and productivity, and it is time to explore new development strategies. The Korean government is establishing a service economic development strategy to promote employment and economic vitality. However, in the era of the 4th industrial revolution, the service industry is very important in that it has to be fused with the manufacturing industry. This study examines the service industry policy related to the 4th industrial revolution which the central government, local governments, and countries around the world are pursuing through literature review. The Big data analysis is used to determine the interest rate of the seven major service industries and core technologies for the fourth generation industrial revolution.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.154-161
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• 2018

This study conducted to predict the stock market prices based on the assumption that internet news articles might have an impact and effect on the rise and fall of stock market prices. The internet news articles were tested to evaluate the accuracy by comparing predicted values of the actual stock index and the forecasting models of the companies. This paper collected stock news from the internet, and analyzed and identified the relationship with the stock price index. Since the internet news contents consist mainly of unstructured texts, this study used text mining technique and multiple regression analysis technique to analyze news articles. A company H as a representative automobile manufacturing company was selected, and prediction models for the stock price index of company H was presented. Thus two prediction models for forecasting the upturn and decline of H stock index is derived and presented. Among the two prediction models, the error value of the prediction model (1) is low, and so the prediction performance of the model (1) is relatively better than that of the prediction model (2). As the further research, if the contents of this study are supplemented by real artificial intelligent investment decision system and applied to real investment, more practical research results will be able to be developed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.4
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• pp.608-612
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• 2012

To reduce these costs and time by finite element analysis program has been much research (3~4). At virtual CAE program as like Abaques, Ansys, Ls-dyna and Nastran, the input data of material is got bellow coupon test. In case of carbon composite, it is also put in lamina/laminate properties. There have big problem. If you want to simulate FW(filament winding or wind blade) how do you input material data. Each area of FW is different stacking conditions. It's too hard that each area is tested for inputting lamina or laminate properties. The composite structure increasing load is applied occurred as the matrix dependence of the crack-induced nonlinearity and nonlinear mobility appears since the initial damage. And uni-direction for this research applies the theory to have been confined to. On this study, we are going to get basically fiber properties and matrix than carbon composite properties for simulating according stacking method by GENOA-MCQ. It is help to simulate easily composite material. Also Calculate the matrix nonlinear for simulating non-linear.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.12
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• pp.21-27
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• 2020

Lithium-ion batteries are the heart of energy-storing devices and electric vehicles. Owing to their superior qualities, such as high capacity and energy efficiency, they have become quite popular, resulting in an increased demand for failure/damage prevention and useable life maximization. To prevent failure in Lithium-ion batteries, improve their reliability, and ensure productivity, prognosticative measures such as condition monitoring through sensors, condition assessment for failure detection, and remaining useful life prediction through data-driven prognostics and health management approaches have become important topics for research. In this study, the residual useful life of Lithium-ion batteries was predicted using two efficient artificial recurrent neural networks-ong short-term memory (LSTM) and gated recurrent unit (GRU). The proposed approaches were compared for prognostics accuracy and cost-efficiency. It was determined that LSTM showed slightly higher accuracy, whereas GRUs have a computational advantage.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• The Journal of Information Systems
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• v.26 no.4
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• pp.63-85
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• 2017

Purpose Recently, much attention in building smart factory has dramatically increased with an emergence of the Industry 4.0. As we noted a connectivity gap between main concerns of MIS and the automated manufacturing systems such as POP and MES, it is recommended that CPS (Cyber-Physical System) can be an important building block for the smart factory and enrich the depth of MIS knowledge. Therefore, first, this study attempted to identify the connectivity gap between the traditional field of MIS (ERP, SCM, CRM, etc.) and the automated manufacturing systems, and then recommended CPS as a technical bridge to fill the gap. Secondly, we studied concepts and research trend of CPS that is believed to be a virtual mechanism to manage manufacturing systems in an integrated manner. Finally, we suggested research and educational opportunities in MIS based on the CPS perspectives. Design/methodology/approach Since this paper introduced relatively new idea of CPS originally discussed in the field of engineering, traditional MIS research method such as survey and experiment may not fit well. Therefore this research collected technical cases through literature survey in engineering fields, video clips from Youtube, and field references from various ICT Exhibitions and Conventions. Then we analyzed and reorganized them to highlight the necessity of CPS and draw some insight to share with MIS academia. Findings This paper introduced CPS to bridge the connectivity gap between the traditional MIS and automated manufacturing system (smart factory), a concern far away from the MIS academia. Further, this paper suggested future research subjects of MIS such as developing software to share big production data and systems to support manufacturing decisions, and innovating MIS curricula including smart and intelligent manufacturing technology within the context of traditional enterprise systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.43-57
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• 2021

Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.