• Korean Journal of Construction Engineering and Management
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• v.6 no.3 s.25
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• pp.167-177
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• 2005

Value Engineering(VE) Workshop executes many tasks with VE team members from various experts of construction field on the basis of VE Job Plan for a short period of time. However, the VE team members are not useful with using VE information until now because it is not systematic in the accumulation and application of VE information. Especially, these problems constitute the obstacles to communication, reliability and quickness of VE information. It also causes an increase of time and cost at VE Workshop. Therefore, the results of this study suggests the conceptual model of Web-Based Information System for VE(WISE) in order to improve the effectiveness of VE Workshop and solve the problem as is stated above.

• Korean Journal of Construction Engineering and Management
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• v.15 no.2
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• pp.71-78
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• 2014

Value Engineering methodology was introduced about 30years ago and has significant achievement in Korea. However, many of workshop still focus on cost down process and do not spend enough time on function analysis and expert cooperation. As a result, limited range of creative ideas were produced. To improve effectiveness of VE workshop, this study developed web-based value engineering process and system. And this study applied it to real case study to evaluate effectiveness of proposed system. Developed VE system also provides automated FAST diagram function and decision-making supporting tools. The authors found that this developed web-based VE system can significantly help to save cost and improve performance of VE workshop by using the appropriate VE process, and quick cooperation between experts. In addition, this study shows an example of a VE case study that applies the web-based VE system process, which led to a very innovative and effective way to lead workshop.

• Journal of the Korea Institute of Building Construction
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• v.21 no.5
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• pp.433-443
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• 2021

Value Engineering(VE) is an organized effort to create the most value through a functional analysis and creation of alternatives. Depending on the VE job plan, a VE Workshop must be performed in a certain place within a certain period of time. A VE Workshop, which is an organized activity that aims to create the best value through functional analysis and creation of alternatives, should be held in a certain place and at a scheduled time according to the VE job plan. In the Pre-Study, VE Study and Post-Study phases of VE, functional assessments, performance evaluations and idea evaluations are driven by variable management techniques and analysis methods, respectively. Generally, VE is executed for about 3 to 5 days in a particular place to create value. However, there are many problems associated with limiting all VE processes to a specific place and schedule. Moreover, in Korea, VE teams are required to finish all VE processes in a limited time because of the short duration of VE workshops, the necessity of which has been overlooked. Therefore, an efficient VE support system is required to resolve the problem of time and space limitations. In this study, a VE support system based on the Mobile environment was developed to support the VE Workshop process. This VE support system enables participants to review design documents, drawing sheets and all VE-related documents using mobile devices. After the Workshop, participants can conveniently rearrange the result(evaluation of function and ideas) at the workshop. Not only can the members of VE team can review the design documents, drawing sheet and all VE-related documents in the step before the workshop, but also the result(evaluation of function and ideas) of the workshop can be easily rearranged in the phase after the workshop under the developed supporting system using mobile devices.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.44-51
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• 2008

Nowadays, the term, Knowledge Management is used at almost all areas and with various scopes and approaches just like a kind of campaign. Knowledge management addressee from a educated individuals to the inter-organizational network. And it also utilized to solve a present problem, to evaluate the past performances of a company or a organization, and to establish a strategy of the future. This study presented a knowledge management model for the construction management company based on comprehensive researches about the knowledge, the knowledge management, the knowledge management company. The results of this study would be helpful for the construction management company which is making efforts to set the knowledge management system up. And it also could be a recommendatory alternative to establish the knowledge management strategy for the company which is preparing for the knowledge management system.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Proceedings of the Korean DIstribution Association Conference
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• 2001.02a
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• pp.49-63
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• 2001

Digital revolution is switching existing paradigms such as analogue to digital, off-line to on-line. Electronic commerce is the single most significant element to change the economic and social environments. After the middle of 1990's, electronic commerce have introduced two scenes; dramatic change of the existing distribution surroundings ad noticeable achievement of the economic advance and social efficiency. But, in the same scene, electronic commerce can be a threat to business condition, especially logistics management. In this study, we've done an empirical study to the experts who are in the real field; B2C and TPL(third party logistics). By the result of the study, we found five factors which means logistics bottlenecks to electronic commerce between business to consumer(B2C) as like; specialization and information, service quality and service variety, benefit-cost, reliability, and legal and policy factor.