• Journal of Biomedical Engineering Research
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• v.31 no.4
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• pp.280-284
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• 2010

Approvals of medical device increase every year as industry of medical device grows. Therefore KFDA keeps trying to improve approval systems. However, the firms of medical device are in trouble due to regulation amendment, a firm of small size, exchange of the person in charge. The staffs of KFDA increase their work load because applicants of approval of medical device aren't used to writing of document. Therefore the firm of medical device in business have a long term. KFDA develops eight guidance document item by item for one-step evaluation and approval for Medical Devices because applicants of approval of medical device write documents easily. KFDA reviewer can carry on quick reviewing in use of this eight guidances. This guidance are improved on satisfaction of applicants of approval of medical device.

• Spatial Information Research
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• v.20 no.6
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• pp.129-137
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• 2012

For construction project in extreme cold region, it is essential to establish basic data on the site such as topographical data from the early stage of construction of planning and designing, and it is needed to frequently perform site investigation when necessary. However, extreme cold regions are characteristic of being at long distance and difficult in approaching, and special regions such as Antarctica, in particular, are hard to conduct site investigation. Although a site investigation may be conducted, those who can visit Antarctica are sufficiently limited so that most of the staff may participate in construction without knowledge of the site and increase the risk of errors in decision making or designing. In order to resolve such problems, the authors in this study identified methods of building wide-area topographical data and bedrock classification data of exposed areas via remote sensing and of building precise topographical data on the construction site. Also, the authors attempted to present methods by which such data can be managed and visualized integrally via three-dimensional GIS technology and all the participants in construction can learn sense of field and conduct necessary analysis as frequent as possible. The areas around the Jangbogo Antarctic Station were selected to be the research area for conducting effective integrational management and three-dimensional visualization of various spatial data such as wide-area digital elevation model, ortho-images, bedrock classification data, local precise digital elevation model, and site images. The results of this study may enable construction firms to analyze local environments for construction whenever they need for construction in extreme cold regions and then support construction work including decision making or designing.

• Journal of Korean Society for Quality Management
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• v.46 no.4
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• pp.843-862
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• 2018

Purpose: The purpose of this study was to develop quality innovation techniques specialized for the small and medium-sized businesses. which account for the majority of Korean companies, were having a hard time utilizing the widely recognized quality innovation techniques due to resource constraints. Methods: First, we do review the existing Single PPM and 6 Sigma. And investigate the utilization of these methods including Toyota Production System. Second, we devised a four-step problem-solving methodology based on recent trends in quality innovation such as Simple, Speedy, and Smart. Third, we do survey on frequently used tools for quality innovation. Many opinion leaders including quality consultants and professors answered and gave us valuable comments about our selected quality tools. Finally, we do specify and map tools to each step of PASS. Results: In 2017, 167 companies participated in the quality innovation support business for small businesses according to the Korea Chamber of Commerce & Industry. We conducted performance checks on 167 companies that had completed the "PASS" projects. For the purpose of evaluating improvement performance, the survey was carried out using a structured questionnaire during the field visit of these companies mentioned above. For the reference, 165 out of 167 companies (98.8 % response rate) responded to the questionnaire and conducted performance analysis based on it. According to the survey, 97.6 percent of the respondents were very satisfied with their overall satisfaction with the quality innovation support projects for small and medium sized enterprises in 2017. Also, 93.3 % of the respondents were satisfied with the results of level of the target achievement. As a result, 160 companies (97.0 % of the participating companies) hope to partic ipate in the quality improvement project using "PASS" once again. Conclusion: In this paper, we introduce the new quality innovation methodology, which is named as 'PASS', It could support the long-range business plan of the small and medium-sized businesses to achieve total customer satisfaction resulting in increased market share and improved profit margin. The most small companies can use this "PASS" technique more easily, quickly and most efficiently than their existing known quality innovation techniques such as Six Sigma and Single PPM, etc.

• Journal of the Korea Organic Resources Recycling Association
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• v.12 no.3
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• pp.63-75
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• 2004

This research sought to determine the status of the installation and operation of domestic public resource-making facilities of resource-making facilities and come up with corresponding improvement measures. Currently compost is most numerous set-up out of facilties already established ever since, then the rest of them are feeds, anaerobic degradation, sewage combination, and combination of compost and feeds in order. As such, food waste is processed more into compost than into feeds, presumably because relevant facilities, which were originally designed for processing into feeds, were converted into composting facilities due to little demand for the processed feeds. The finding says that many related firms had yet to register their businesses in accordance with feeds and fertilizers management laws, and that food waste resources-making facilities used various basic facilities but few of them treated food waste in linkage with leaching water, bad odors, and energy. Some of current facilities were found to be 7 years old and thus outdated. Due to lack of skilled operational manpower, many facilities had less than 300 days of normal operation yearly, and some needed minor and serious repairs periodically. In overall facilities, 87% of the planned food waste was rolled in, thus requiring measures to treat the whole planned volume. For costs of resource-making facilities, some with a capacity of below 50 tons topped 100 million won, and facilities with a capacity of over 50 tons required less installation costs. Overall, installation costs ranged from 10 million to 20 million, and to 200 million won per ton, and this suggests a need to establish the installation cost calculation criteria, as well as to reshape the facility criteria. With operating costs varying greatly according to the size and treatment methods of facilities, the finding indicates a need to rationalize the operating costs, and to plan appropriate-size installation and operation of facilities to ensure economic operation.

• 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.

• Archives of design research
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• v.18 no.4 s.62
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• pp.95-104
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• 2005

The purpose of this study is to estimate the determinants of product design and analyze the impacts of product design on quality competitiveness, product reliability, and consumer satisfaction in an attempt to provide a foundation for the theory of design management. For this empirical analysis, this study has derived the relevant measurement variables from a survey on 400 Korean manufacturing firms during the period of $August{\sim}October$ 2003. The empirical findings are summarized as follows: First, the determinants of product design are very significantly (at p<0.001) estimated to be the R&D capability, the level of R&D expenditure, the level of innovative activities(5S, TQM, 6Sigma, QC, etc.). This empirical result can support Pawar and Driva(1999)'s two principles by which the performance of product design and product development can be simultaneously evaluated in the context of CE(concurrent engineering) of NPD(newly product development) activities. Second, the hypothesis on the causality: product design${\rightarrow}$quality competitiveness${\rightarrow}$customer satisfaction${\rightarrow}$customer loyalty is very significantly (at p<0.001) accepted. This implies that product design positively affects consumer satisfaction, not directly but indirectly, by influencing quality competitiveness. This empirical result of this study can also support the studies of for example Flynn et al.(1994), Ahire et at.(1996), Afire and Dreyfus(2000) which conclude that design management is a significant determinant of product quality. The aforementioned empirical results are important in the following sense: the empirical result that quality competitiveness plays a bridging role between product design and consumer satisfaction can reconcile the traditional debate between QFD(quality function development) approach asserted by product developers and conjoint analysis maintained by marketers. The first empirical result is related to QFD approach whereas the second empirical result is related to conjoint analysis. At the same time, the empirical results of this study can support the rationale of design integration(DI) of Ettlie(1997), i.e., the coordination of the timing and substance of product development activities performed by the various disciplines and organizational functions of a product's life cycle. Finally, the policy implication (at the corporate level) from the empirical results is that successful design management(DM) requires not only the support of top management but also the removal of communication barriers, (i.e. the adoption of cross-functional teams) so that concurrent engineering(CE), the simultaneous development of product and process designs can assure product development speed, design quality, and market success.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.5
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• pp.119-134
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• 2018

Today, many firms face the environment of high uncertainty and severe competition due to the rapid technology development and the diverse needs of customers. In the business environment, one of the most important ways to gain sustainable competitive advantage and future growth engine is related to NPD (New Product Development), which is a very important issue for practice and academia. Thus, this study intends to provide new values to practitioners and researchers related to NPD by analyzing current research trends and future trends in NPD field. For this, we bibliometrically analyzed keyword networks which consist of keywords that were already published in the eminent journals from Scopus database to generate insights that have not been captured in the previous reviews on the topic. As a result, we could understand the extant research streams in NPD field, and suggest the changes of specific research topics based on the connected relationships among keywords over the time. In addition, we also foresaw the general future research trends in NPD field based on the keywords according to preferential attachment processes. Through this study, it was confirmed that NPD keyword network is a small world network that follows the distribution of power law and the growth of network is formed by link formation by keyword preferential attachment. In addition, through component analysis and centrality analysis, keywords such as Innovation, New product innovation, Risk management, Concurrent engineering, Research and development, and Product life cycle management are highly centralized in NPD keyword network. On the other hand, as a result of examining the change of preferential attachment of keywords over the time, we suggested the required new research direction including i) NPD collaboration with suppliers, ii) NPD considering market uncertainty, iii) NPD considering convergence with the other academic areas like technology management and knowledge management, iv) NPD from SME(Small and medium enterprises) perspective. The results of this study can be used to determine the research trends of NPD and the new research themes for interdisciplinary studies with other disciplines.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Information Systems Review
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• v.18 no.2
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• pp.79-106
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• 2016

Service innovation is one means of gaining an advantage in a highly competitive environment. Although numerous studies have stressed the importance of service innovation, traditional good-dominant logic is still used in service innovation literature. Furthermore, few studies have been conducted on the link between service innovation and its antecedents in terms of service-oriented approach. To fill the gap, this article theoretically and empirically examines service innovation and its antecedents and consequences. Based on service-dominant logic and resource advantage theory, the current study aims to understand the effect of antecedents on service innovation as well as to identify the effect of service innovation on firm performance (i.e., non-financial and financial performance). Three service innovation activities, namely service creation-focused innovation, service delivery-focused innovation, and customer interaction-focused innovation, and four antecedents of service innovation, including human resource management capability, collaboration capability, marketing capability, and information technology capability, are identified based on Den Hertog (2000)'s service innovation framework. By using the empirical data collected from 189 service firms in Korea, this study explores the causal relationship among antecedents, service innovation and firm performance. Findings indicate that human resource management and marketing capabilities influence the three types of service innovation, whereas collaboration and information technology capabilities have a significant effect on both service creation-focused innovation and service delivery-focused innovation. In particular, human resource management capability is strongly related to customer interaction-focused innovation. The three types of service innovation have a positive influence on non-financial performance, whereas service delivery-focused innovation and customer interaction-focused innovation positively influence financial performance. These results support the crucial effects of antecedents, such as human resource management, collaboration, marketing and information technology capabilities, on service innovation.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.