• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.120-134
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• 1991

Various technological forecasting models have been proposed to represent the time pattern of technological growths. Of six such models studied, some models do significantly better than others, especially at low penetration levels, in predicting future levels of growth. Criteria for selecting an appropriate model for technological growth model are examined in this study. Two major characteristics were selected which differentiate the various models ; the skew of the curve and the underlying assumptions regarding the variance of the error structure of the model. Although the use of statistical techniques stil requires some subjective input and interpretations, this study provides some practical procedures in the selection of technological growth models and helps to reduce or control the potential source of judgmental error inconsistencies in the analyst's decision.

• Journal of the Korea Safety Management & Science
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• v.12 no.2
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• pp.217-223
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• 2010

The research proposes the complementary methodology using integrated hypothesis testing and confidence interval models that can be identified the statistical difference and practical equivalence. The models developed in this study can be used in the quality improvement processes such as QC story 15 steps. For the expressions of CI4LSD(Confidence Interval for Least Significant Difference) and CI4TOST(Confidence Interval for Two One-Sided Tests) are simple, quality practioners can efficiently handle them. CI4TOST models as a complement can be applied when CI4LSD models are influenced by sample size and precision.

• IE interfaces
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• v.25 no.1
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• pp.79-86
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• 2012

Traditional business process management systems provide verification tools of process models to deploy and automate the models. However, there are not so many studies on how to maintain systematically collaborative process models such as supply chain processes when companies are willing to change and update the collaborative process models. In this paper, we analyze change patterns of collaborative processes and declare 19 change patterns. In addition, we apply the change patterns to the process interoperability patterns in order to identify the change problems in case of independent process changes of collaborative processes. As a result, we devise an independency checking algorithm of process changes in collaborative processes.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.5
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• pp.327-336
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• 2016

Prediction of customer failure rates plays an important role for establishing appropriate management policies and improving the profitability for industries. For these reasons, many LCD (Liquid crystal display) manufacturing industries have attempted to construct prediction models for customer failure rates. However, most traditional models are based on the parametric approaches requiring the assumption that the data follow a certain probability distribution. To address the limitation posed by the distributional assumption underpinning traditional models, we propose using parameter-free data mining models for predicting customer failure rates. In addition, we use various information associated with product attributes and field return for more comprehensive analysis. The effectiveness and applicability of the proposed method were demonstrated with a real dataset from one of the leading LCD companies in South Korea.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.151-167
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• 1994

Initial screening is one of the most important and difficult processes in new product development. Many new product screening models have been developed in management and marketing. However practical applications of these models have been limited in part due to their complexity and inflexibility, and in part due to their excessive data requirements. Thus simple judgment models have been popular in practice. However, these models suffer from inaccuracy and inconsistency originating form human cognitive limitations. In light of the problem swith traditional screening methods, we propose a new approach for screening based on managers' past experience and intuitive judgments-screening by analogy, and develop a computerized case-based system for screening new financial service concepts. Using the system, managers can predict the potential performance of a new product concept based on the performance of past products that are similar to it in terms of product characteristics, firm's resources, and market conditions. Based on this prediction, managers make a screening decision.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.3
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• pp.79-100
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• 1993

This paper proposes an idea of reverse modeling that analyzes LP models and then converts them into an object-oriented model repository. The process of reverse modeling consists of (1) identifying and analyzing source models by meta processor, (2) model decomposition and generalization to scan the models and divide them into model components, and (3) deriving model selection rules from the components by rule generator. Through the process, we can extract reusable model components and build a model base with model selectioon rules. Examples with models created by SML and MODLER modeling languages are given to illustrate the methods. The model base management capabilities provided by reverse modeling can increase the reusabioity of current modeling tools.

• Korean Management Science Review
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• v.14 no.1
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• pp.87-105
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• 1997

There have been lots of research on temporal data management for the past two decades. Most of them are based on some logical data model, especially on the relational data model, although there are some conceptual data models which are independent of logical data models. Also, many properties or issues regarding temporal data models and temporal query languages have been studied. But some of them were shown to be incompatible, which means there could not be a complete temporal data model, satisfying all the desired properties at the same time. Many modeling issues discussed in the papers, do not have to be done so, if they take object-oriented data model as a base model. Therefore, this paper proposes a temporal data model, which is based on the object-oriented data model, mainly discussing the most essential issues that are common to many temporal data models. Our new temporal data model and query language will be illustrated with a small database, created by a set of sample transaction.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.1
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• pp.87-87
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• 1989

There have been lots of research on temporal data management for the past two decades. Most of them are based on some logical data model, especially on the relational data model, although there are some conceptual data models which are independent of logical data models. Also, many properties or issues regarding temporal data models and temporal query languages have been studied. But some of them were shown to be incompatible, which means there could not be a complete temporal data model, satisfying all the desired properties at the same time. Many modeling issues discussed in the papers, do not have to be done so, if they take object-oriented data model as a base model. Therefore, this paper proposes a temporal data model, which is based on the object-oriented data model, mainly discussing the most essential issues that are common to many temporal data models. Our new temporal data model and query language will be illustrated with a small database, created by a set of sample transaction.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.39-42
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• 1996

Modeling techniques such as linear regression have been used to predict hurricane activity many months in advance of the start of the hurricane season with some success. In this paper, we construct feedforward neural networks to model Atlantic basin hurricane activity and compare the predictions of our neural network models to the predictions produced by statistical models found in the weather forecasting literature. We find that our neural network models produce reasonably accurate predictions that, for the most part, compare favorably to the predictions of statistical models.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.75-94
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• 2005

Research in the decision sciences has continued to develop a variety of mathematical models as well as software tools supporting corporate decision-making. Yet. in spite of their potential usefulness, the models are little used in real-world decision making since the model solution processes are too complex for ordinary users to get accustomed. This paper proposes an intelligent and flexible model-solver integration framework that enables the user to solve decision problems using multiple models and solvers without having precise knowledge of the model-solution processes. Specifically, for intuitive model-solution, the framework enables a decision support system to suggest the compatible solvers of a model autonomously without direct user intervention and to solve the model by matching the model and solver parameters intelligently without any serious conflicts. Thus, the framework would improve the productivity of institutional model solving tasks by relieving the user from the burden of leaning model and solver semantics requiring considerable time and efforts.