• IE interfaces
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• v.14 no.2
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• pp.120-126
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• 2001

Virtual manufacturing is a technology facilitating effective development and agile manufacturing of products via sophisticated computer models representing physical and logical schema and behavior of real manufacturing systems including manufacturing resources, environments, and products. Based on these models, virtual manufacturing supports decision making and error checking in the entire manufacturing processes from design to mass production. At first, we analyzed manufacturing preparation activities of the four major production shops such as press, body assembly, painting and final assembly, of a Korean automotive company. We then developed the workflow models out of the analysis by the IDEF methodology, and generated a strategic plan for the systematic application of the virtual manufacturing technologies. We identified many manufacturing preparation activities that can be improved by the application of virtual manufacturing technologies. Finally, we estimated the effect of improvement including time savings in car development processes and corresponding cost savings.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.165-165
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• 2023

Streamflow prediction is a critical task in water resources management and essential for planning and decision-making purposes. However, the streamflow prediction is challenging due to the complexity and non-linear nature of hydrological processes. The transfer learning is a powerful technique that enables a model to transfer knowledge from a source domain to a target domain, improving model performance with limited data in the target domain. In this study, we apply the transfer learning using the Informer model, which is a state-of-the-art deep learning model for streamflow prediction. The model was trained on a large-scale hydrological dataset in the source basin and then fine-tuned using a smaller dataset available in the target basin to predict the streamflow in the target basin. The results demonstrate that transfer learning using the Informer model significantly outperforms the traditional machine learning models and even other deep learning models for streamflow prediction, especially when the target domain has limited data. Moreover, the results indicate the effectiveness of streamflow prediction when knowledge transfer is used to improve the generalizability of hydrologic models in data-sparse regions.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.78-80
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• 2011

In the area of data fusion, dealing with heterogeneous data sources, numerous models have been proposed in last three decades to facilitate different application domains i.e. Department of Defense (DoD), monitoring of complex machinery, medical diagnosis and smart buildings. All of these models shared the theme of multiple levels processing to get more reliable and accurate information. In this paper, we consider five most widely acceptable fusion models (Intelligence Cycle, Joint Directors of Laboratories, Boyd control, Waterfall, Omnibus) applied to different areas for data fusion. When they are exposed to a real scenario, where large dataset from heterogeneous sources is utilize for object monitoring, then it may leads us to non-efficient and unreliable information for decision making. The proposed variation works better in terms of time and accuracy due to prior data diminution.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.10a
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• pp.3-20
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• 2006

The following lessons have been learned from the application of reliability methods in the practice of geotechnical engineering: 1. Establishing Goals Is Important; 2. Mitigating Consequences Can Be Effective; 3. Performance Depends on Systems; 4. Physical Factors Are Important in Statistical Models; 5. Too Much and Too Little Conservatism Are Both Problems; 6. Value of Information Depends on Decision Making; and 7. Effective Communication Is Essential. While the potential for application of reliability methods in the future is unlimited, there are major needs related to each of these lessons that will have to be addressed in order to realize this potential.

• Journal of Korean Society for Quality Management
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• v.35 no.1
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• pp.136-141
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• 2007

For many products failures depend on age and usage and, in this case, failures are random points in a two-dimensional plane with the two axes representing age and usage. Models play an important role in decision-making. In this research, an accelerate failure test (AFT) model is proposed to deal with the two-dimensional data. The parameters are proposed to be estimated through maximum likelihood estimators.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

• Journal of Distribution Science
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• v.16 no.7
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• pp.57-65
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• 2018

Purpose - Recently, internet access and social network utilization using smart phone are increasing. In such a smart environment, interactive activities such as information generation, information searching and information sending are increasing rapidly on-line environment. Therefore, consumers tend to purchase something according to eWOM and also meet the social consensus online environment. In connectivity society, consumers became accessible and engaged in the opinions of others easily. Many decisions that seem like personal decisions are actually social decisions on online connectivity. This paper seeks to explore factors that can help generate a social decision on purchasing of new products in an online environment. Research design, data, and methodology - The process of collecting a lot of wisdom and making an agreement online is called social decision. The purpose of this paper is to examine empirically the influence of factors such as online ties, online eWOM expectancy and online information behavior on online social decision. In addition, We studied online social decision by analyzing the moderating effect of new product innovation. To understand this structural relationship, research hypotheses and research models were set up and empirical analysis was conducted. In order to verify the hypothesis, 208 questionnaires were collected from the residents of Seoul city/Gyeonggi province. The answered questionnaire verifies reliability and validity using SPSS/AMOS and test hypotheses through path analysis and multiple regression analysis. Results - According to the research results, First, online ties don't have a positive impact on online social decision, Second, online eWOM expectancy have a positive impact on online social decision. Third, online information behaviors have a positive impact on online social decision. The degree of innovation of new products have a moderating effect between Independent variables of three factors and dependent variable of social decision. Conclusions - Social decisions have a positive impact on purchasing decisions about new product. There is a great significance in the fact that the online social influence and online social decision have been studied academically. It is meaningful that we have studied in depth the changing phenomenon of consumer purchase decision process in smart environment. The results of these studies provide academic and practical implications.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.472-482
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• 2012

There are many investors in the stock market, and more and more people get interested in the stock investment. In order to avoid risks and make profit in the stock investment, we have to determine several aspects using various information. That is, we have to select profitable stocks and determine appropriate buying/selling prices and holding period. This paper proposes a data mining tool for the investors' decision support. The data mining tool makes stock investors apply machine learning techniques and generate stock price prediction model. Also it helps determine buying/selling prices and holding period. It supports individual investor's own decision making using past data. Using the proposed tool, users can manage stock data, generate their own stock price prediction models, and establish trading policy via investment simulation. Users can select technical indicators which they think affect future stock price. Then they can generate stock price prediction models using the indicators and test the models. They also perform investment simulation using proper models to find appropriate trading policy consisting of buying/selling prices and holding period. Using the proposed data mining tool, stock investors can expect more profit with the help of stock price prediction model and trading policy validated on past data, instead of with an emotional decision.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.84-94
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• 2020

Modular architecture is very important not only in the design phase but also in the construction planning phase because it affects construction methods and module sizes depending on transport equipment. There are economic risks as well as quality, as there may be defects such as internal interiors or elimination of deadlines during transportation, and structural torsion caused by rainfall and shock. However, there is a lack of objective criteria or data to refer to in determining transport equipment that has a material effect on transport. Accordingly, there is no decision model to determine the optimum transportation equipment for each construction site. Therefore, it is necessary to develop a decision support model that can be compared to the review of transport equipment selection factors. The purpose of this study is to propose the transport equipment impact factors and decision support models for systematic review and objective decision making of each construction plan in the construction of small and medium-sized modulators. The decision model proposed in this study can be used as basic data for transport studies, ensuring objectivity and transparency in the equipment selection process.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.97-117
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• 2021

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.