• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• International Journal of Quality Innovation
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• v.5 no.2
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• pp.1-16
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• 2004

Credit scoring is widely used to make credit decisions, to reduce the cost of credit analysis and enable faster decisions. However, traditional credit scoring models do not account for the influence of noises. This study proposes a robust credit scoring system based on Mahalanobis-Taguchi System (MTS). The MTS, primary proposed by Taguchi, is a diagnostic and forecasting method using multivariate data. The proposed approach's effectiveness is demonstrated by using real case data from a large Taiwanese bank. The results reveal that the robust credit scoring system can be successfully implemented using MTS technique.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.1916-1921
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• 2008

This paper presents the temporal classification method based on data mining techniques for discovering knowledge from measured load patterns of distribution transformers. Since the power load patterns have time-varying characteristics and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Therefore, we propose a temporal classification rule for analyzing and forecasting transformer load patterns. The main tasks include the load pattern mining framework and the calendar-based expression using temporal association rule and 3-dimensional cube mining to discover load patterns in multiple time granularities.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.74-78
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• 2003

In this paper, we have developed a technology-service relationship model which describes the diffusion process of a group of services and relevant technologies, and have applied the developed model to the prediction of the number of subscribers to the next generation mobile service. The technology-service relationship model developed in this paper incorporates the developing process of relevant technologies, a supply-side factor, into the diffusion process of specific services, while many diffusion models and multi-generation diffusion models in previous researches are mainly reflect the demand-side factors. So, the proposed model could effectively applied to the telecommunication services where the developing of the relevant technologies are very essential to the service penetration. In our application, the proposed model provides a competitive substitution between the next generation mobile service and the traditional mobile service.

• Proceedings of the KIPE Conference
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• 2012.11a
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• pp.33-34
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• 2012

As the distributed power system with PV and ESS is highlighted to be one of the most prominent structure to replace the traditional electric power system, power flow scheduling is expected to bring better system efficiency. Optimal energy management system (EMS) where the power from PV and the grid is managed in time-domain using ESS needs an optimization process. In this paper, main optimization method is implemented using dynamic programming (DP). To overcome the drawback of DP in which ideal future information is required, prediction stage precedes every EMS execution. A simple auto-regressive moving-average (ARMA) forecasting followed by a PI-controller updates the prediction data. Assessment of the on-line optimal EMS scheme has been evaluated on several cases.

• Proceedings of the Technology Innovation Conference
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• 1999.12a
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• pp.328-347
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• 1999

The substitution of Electronic Commerce(EC) for the traditional transactions triggers the changes of the industry structures and promotes the cost reductions of the firms in the areas of distributions and other administrative operations associated with purchase via EC. Our study clarifies the changes of the environments attributable to EC which are faced inter-and-externally by firms and try to exhibit the trend of EC market growth through such descriptions. Regardless of the rapid spread of EC, recent studies do not show appropriately its impact on the relevant industries and our domestic economy. Therefore, our study focuses on the forecasting of the impacts of EC on the domestic productions and imports. To this end, we develop an analytic framework using the existing data in Input/Output Analysis and the estimations of the EC market growth in the future. We, finally, identify the industrial sectors whose productions and imports are estimated to be accelerated by the extension of EC and forecast the whole effects of EC on domestic productions and imports.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.1
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• pp.87-99
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• 2004

In this paper, we have developed a technology-service relationship model which describes the diffusion process of a group of services and relevant technologies, and have applied the developed model to the prediction of the number of subscribers to the next generation mobile service. The technology-service relationship model developed in this paper incorporates the developing process of relevant technologies, a supply-side factor, into the diffusion process of specific services, while many diffusion models and multi-generation diffusion models in previous researches are mainly reflect the demand-side factors. So, the proposed model could effectively applied to the telecommunication services where the developing of the relevant technologies are very essential to the service Penetration. In our application, the Proposed model provides a competitive substitution between the next generation mobile service and the traditional mobile service.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.1-10
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• 1999

In recent industry society, it is revealed that, as an increase in the use of automated manufacturing and process inspection technology, the data from mass production system exhibits some degrees of autocorrelation. The operation characteristics of traditional control charts developed under the independence assumption are adversely affected by the presence of serial correlation. Therefore, when autocorrelated construction contacted with time-series models explain, the time-series models are the Box-Jenkins forecast models which have been proposed as the best forecasting tool which allows for partitioning of variation into result from the autocorrelation structure and variation due to unusual but assignable causes. In this paper, for the AR(1) process of Box-Jenkins forecast models, when the constant term ξ are zero and different from zero, I want to analyze the sensitivity of (equation omitted), CUSUM and EWMA control chart for forecast residuals.

• Asia pacific journal of information systems
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• v.8 no.3
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• pp.51-73
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• 1998

This paper is to evaluate the application potentials of data mining in the areas of Supply Chain Management (SCM) and to suggest the architectures of Decision Support Systems (DSS) that support data mining activities. We first briefly introduce data mining and review the recent literatures on SCM and then evaluate data mining applications to SCM in three aspects: marketing, operations management and information systems. By analyzing the cases about pricing models in distribution channels, demand forecasting and quality control, it is shown that artificial intelligence techniques such as artificial neural networks, case-based reasoning and expert systems, combined with traditional analysis models, effectively mine the useful knowledge from the large volume of SCM data. Agent-based information system is addressed as an important architecture that enables the pursuit of global optimization of SCM through communication and information sharing among supply chain constituents without loss of their characteristics and independence. We expect that the suggested architectures of intelligent DSS provide the basis in developing information systems for SCM to improve the quality of organizational decisions.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.1
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• pp.1-10
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• 2004

Advance in Internet technology has changed traditional production planning and control methods. In particular, collaborations between participants in supply chains are being increasingly addressed in industry for enhancing chain-wide productivity. A representative paradigm that emphasizes collaboration in production planning and control is CPFR(Collaborative Planning, Forecasting and Replenishment). In this paper, we present a multi-agent system architecture that supports the collaborations specified in CPFR. The multi-agent system architecture consists of event manager, data view agent, business rule agent, and collaboration agent. The collaboration agent systematically controls negotiation between supplier and buyer with the aid of collaboration protocol and blackboard. The multi-agent system has been implemented with EJB(Enterprise Java Beans).