• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.750-755
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• 2014

Product shortage which causes backordering and/or lost sales cost is very popular in chemical industries, especially in commodity polymer business. This study deals with backordering cost in the supply chain optimization model under the framework of process-inventory network. Classical economic order quantity model with backordering cost suggested optimal time delay and lot size of the final product delivery. Backordering can be compensated by advancing production/transportation of it or purchasing substitute product from third party as well as product delivery delay in supply chain network. Optimal solutions considering all means to recover shortage are more complicated than the classical one. We found three different solutions depending on parametric range and variable bounds. Optimal capacity of production/transportation processes associated with the product in backordering can be different from that when the product is not in backordering. The product shipping cycle time computed in this study was smaller than that optimized by the classical EOQ model.

• Archives of design research
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• v.16 no.1
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• pp.289-296
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• 2003

The thesis is a study of the information model for the information collection and systematization in the way of product design. In the past, the design was made by the designers hands, worked with the material directly, but today's product design, the material diverted to information, can be considered as it is made essentially through information collection and systematization processing. If the product design is considered the information processing, usually it means a qualitative change of the product design information, not a quantitative change of the information theory. A focus of the study is to grope for a way of changing the subject to information, dealing with when the product design intends to purposes, not the material, When a way of the product design was discussed to solve the problems rationally, in the past, if it is considered as quantitative, qualitative and organic methods and modeling as their means based on the process model, [analysis-generalization-estimation], are formal ism, the way of product design as information is that the product direction as a substance should go through the design information processing, making an alternative plan with the information model cycling to natural order. Because success or failure of the product design in the future depends on information as material.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.55-60
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• 2017

In order to provide intelligent services without human intervention in the Internet of Things environment, it is necessary to analyze the big data generated by the IoT device and learn the normal pattern, and to predict the abnormal symptoms such as faulty or malfunction based on the learned normal pattern. The purpose of this study is to implement a machine learning model that can predict product failure by analyzing big data generated in various devices of product process. The machine learning model uses the big data analysis tool R because it needs to analyze based on existing data with a large volume. The data collected in the product process include the information about product faulty, so supervised learning model is used. As a result of the study, I classify the variables and variable conditions affecting the product failure, and proposed a prediction model for the product failure based on the decision tree. In addition, the predictive power of the model was significantly higher in the conformity and performance evaluation analysis of the model using the ROC curve.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.107-126
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• 2021

In e-commerce, rapid and accurate automatic product classification according to product information is important. Recent developments in deep learning technology have been actively applied to automatic product classification. In order to develop a deep learning model with good performance, the quality of training data and data preprocessing suitable for the model are crucial. In this study, when categories are inferred based on text product data using a deep learning model, both effects of the data preprocessing and of the selection of training data are extensively compared and analyzed. We employ our CNN model as an example of deep learning model. In the experimental analysis, we use a real e-commerce data to ensure the verification of the study results. The empirical analysis and results shown in this study may be meaningful as a reference study for improving performance when developing a deep learning product classification model.

• Journal of Engineering Education Research
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• v.25 no.1
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• pp.46-54
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• 2022

This study attempted non-face-to-face collaborative product development practice that can respond to the spread of COVID-19 by expanding existing product data management system-based product development practice. For the complete non-face-to-face product development practice, it utilized prototype development using a 3D paper model, an online class management system and social media for classes and meetings. As a result of applying the non-face-to-face method, product developments of 26 practice teams have been completed without any failures. Therefore, through this study, the author can confirm that it is possible to provide the complete non-face-to-face collaborative product development practice based on product data management systems.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.103-112
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• 1996

Optimal pricing research in general has been focused on profit maximizing strategy under the given product life-time T. Here we have tried to study the effect of uncertain product life-time on dynamic optimal pricing strategy. In reality, the life-time of product is more likely to be uncertain and not known as well. In terms of approximating the model to the concerned reality, so-called model validity, it seems to be more desirable to consider the uncertainty of product life-time into the optimal pricing strategy model, For this purpose, we tried two different approaches. One is to consider diverse product life-time probability functions under fixed life-time T. In this case, we might have the same product life-time as the previous study, but the process could be different in the expectation of product's discontinuity. The other is that life-time itself is not determined and thus it is the situation in which we can only decide optimal price on incremental basis. The former is the situation in which although we got some strong guess on life-time of a certain product, the pattern of expected life-time probability could be different. The question is what could be optimal pricing strategies on such different product life-time situations. But since in the latter, we don't assume any idea on the life-time of product. proper optimal pricing could be derived only from the past prices and diffusion information. While the latter seems to be safer in the aspect of model assumption, the former could be more realistic because we might have more or less a prior knowledge on the product life-time itself.

• Journal of the Korean Graphic Arts Communication Society
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• v.30 no.3
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• pp.65-75
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• 2012

Appearing gagman on various ads are presently experienced in domestic market both high involvement and low involvement products ads. Therefore, this research attempts to analyze the effectiveness of using gagman ad model in terms of building brand preference and purchasing intention among target audience. Results of this study proposed the basement of practical use when gagman is selected as a ad model in the field. For testing hypotheses attractiveness, reliability, like/dislike as dependent variables and as independent variable purchasing intention were selected. The result of analysis shows that for high involvement product reliability influenced on brand like/dislike and attractiveness of gagman model is effective to enhance purchasing intention. For low involvement product only like/dislike was significant. This can be interpreted gagman ad model is useful for building brand like/dislike and purchasing intention of high involvement product when gagman has reliability and attractiveness respectively, while for low involvement product, like and dislike is the only variable to be considered in choosing gagman ad model.

• Journal of Biosystems Engineering
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• v.20 no.4
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• pp.343-350
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• 1995

In order to design efficient and effective pressure cooling system for fruits and vegetables, a numerical model for temperature prediction of fruits was developed. This model was extended to study the various factors affecting product cooling time, such as product depth, approach air temperature, entering air velocity and initial product temperature. Also, selection of these factors were examined with respect to the efficiency of the pressure cooling system, the overall precooling cost and the final quality of the product. When designing a pressure cooling system for a particular product, the range of the factors must be selected carefully according to the thermal and physiological properties.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.945-952
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• 2003

With the rapid growth of the B2B electronic commerce, digital catalogs for expressing product information effectively and exchanging it easily between companies become increasingly important. In this paper, we refer a product expression model to effectively integrate product information, and implement the model using XDK(XML Development Kit), which is given by Oracle 8i. We will first derive an XML DTD reflecting the model. We will then develop a system that stores and retrieves product information on/from Oracle 8i databases, which is written in XML, complying the XML DTD structure. Finally, We will give an experiment on the system.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05b
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• pp.637-642
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• 1996

This paper presents the analysis results for the core degradation processes and the fission product release of the PHEBUS FPT0 experiment using MELCOR1.8.3. The objective of this study is to assess models associated with the core damage and fission product behavior in MELCOR. The calculation results were much improved through sensitivity studies. Thermal/hydraulic behavior in the core and the circuit was well predicted under the intact core geometry. In non-eutectic model case. the UO$_2$ dissolution model in the MELCOR always showed such a tendency that the resulting dissolved UO$_2$ mass was small at the highly oxidized condition due to the model logic. Total H$_2$ generation mass was underpredicted because the stiffner was not modeled and the liner in the shroud was not allowed to be oxidized in MELCOR. Some difficulties were found in modeling the activation product were solved by manipulating the RN input associated with the initial fission product inventory. These problem were occurred because there are no control rod model in MELCOR. Generally the fission product release ratio showed a similar trend compared with the measured data except the activation product. which have no model to simulate in MELCOR.