• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.131-145
• /
• 2010

The framework for making a coordinated decision for large-scale facilities has become an important issue in supply chain(SC) management research. The competitive business environment requires companies to continuously search for the ways to achieve high efficiency and lower operational costs. In the areas of production/distribution planning, many researchers and practitioners have developedand evaluated the deterministic models to coordinate important and interrelated logistic decisions such as capacity management, inventory allocation, and vehicle routing. They initially have investigated the various process of SC separately and later become more interested in such problems encompassing the whole SC system. The accurate quotation of ATP(Available-To-Promise) plays a very important role in enhancing customer satisfaction and fill rate maximization. The complexity for intelligent manufacturing system, which includes all the linkages among procurement, production, and distribution, makes the accurate quotation of ATP be a quite difficult job. In addition to, many researchers assumed ATP model with integer time. However, in industry practices, integer times are very rare and the model developed using integer times is therefore approximating the real system. Various alternative models for an ATP system with time lags have been developed and evaluated. In most cases, these models have assumed that the time lags are integer multiples of a unit time grid. However, integer time lags are very rare in practices, and therefore models developed using integer time lags only approximate real systems. The differences occurring by this approximation frequently result in significant accuracy degradations. To introduce the ATP model with time lags, we first introduce the dynamic production function. Hackman and Leachman's dynamic production function in initiated research directly related to the topic of this paper. They propose a modeling framework for a system with non-integer time lags and show how to apply the framework to a variety of systems including continues time series, manufacturing resource planning and critical path method. Their formulation requires no additional variables or constraints and is capable of representing real world systems more accurately. Previously, to cope with non-integer time lags, they usually model a concerned system either by rounding lags to the nearest integers or by subdividing the time grid to make the lags become integer multiples of the grid. But each approach has a critical weakness: the first approach underestimates, potentially leading to infeasibilities or overestimates lead times, potentially resulting in excessive work-inprocesses. The second approach drastically inflates the problem size. We consider an optimized ATP system with non-integer time lag in supply chain management. We focus on a worldwide headquarter, distribution centers, and manufacturing facilities are globally networked. We develop a mixed integer programming(MIP) model for ATP process, which has the definition of required data flow. The illustrative ATP module shows the proposed system is largely affected inSCM. The system we are concerned is composed of a multiple production facility with multiple products, multiple distribution centers and multiple customers. For the system, we consider an ATP scheduling and capacity allocationproblem. In this study, we proposed the model for the ATP system in SCM using the dynamic production function considering the non-integer time lags. The model is developed under the framework suitable for the non-integer lags and, therefore, is more accurate than the models we usually encounter. We developed intelligent ATP System for this model using genetic algorithm. We focus on a capacitated production planning and capacity allocation problem, develop a mixed integer programming model, and propose an efficient heuristic procedure using an evolutionary system to solve it efficiently. This method makes it possible for the population to reach the approximate solution easily. Moreover, we designed and utilized a representation scheme that allows the proposed models to represent real variables. The proposed regeneration procedures, which evaluate each infeasible chromosome, makes the solutions converge to the optimum quickly.

• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.213-225
• /
• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.85-97
• /
• 2010

Internet communities are a typical space for knowledge creation and use on the Internet as people discuss their common interests within the internet communities. When we define 'Knowledge Communities' as internet communities that are related to knowledge creation and use, they are categorized into 4 different types such as 'Search Engine,' 'Open Communities,' 'Specialty Communities,' and 'Activity Communities.' Each type of knowledge community does not remain the same, for example. Rather, it changes with time and is also affected by the external business environment. Therefore, it is critical to develop processes for practical use of such changeable knowledge communities. Yet there is little research regarding a strategic framework for knowledge communities as a source of knowledge creation and use. The purposes of this study are (1) to find factors that can affect knowledge creation and use for each type of knowledge community and (2) to develop a strategic framework for practical use of the knowledge communities. Based on previous research, we found 7 factors that have considerable impacts on knowledge creation and use. They were 'Fitness,' 'Reliability,' 'Systemicity,' 'Richness,' 'Similarity,' 'Feedback,' and 'Understanding.' We created 30 different questions from each type of knowledge community. The questions included common sense, IT, business and hobbies, and were uniformly selected from various knowledge communities. Instead of using survey, we used these questions to ask users of the 4 representative web sites such as Google from Search Engine, NAVER Knowledge iN from Open Communities, SLRClub from Specialty Communities, and Wikipedia from Activity Communities. These 4 representative web sites were selected based on popularity (i.e., the 4 most popular sites in Korea). They were also among the 4 most frequently mentioned sitesin previous research. The answers of the 30 knowledge questions were collected and evaluated by the 11 IT experts who have been working for IT companies more than 3 years. When evaluating, the 11 experts used the above 7 knowledge factors as criteria. Using a stepwise linear regression for the evaluation of the 7 knowledge factors, we found that each factors affects differently knowledge creation and use for each type of knowledge community. The results of the stepwise linear regression analysis showed the relationship between 'Understanding' and other knowledge factors. The relationship was different regarding the type of knowledge community. The results indicated that 'Understanding' was significantly related to 'Reliability' at 'Search Engine type', to 'Fitness' at 'Open Community type', to 'Reliability' and 'Similarity' at 'Specialty Community type', and to 'Richness' and 'Similarity' at 'Activity Community type'. A strategic framework was created from the results of this study and such framework can be useful for knowledge communities that are not stable with time. For the success of knowledge community, the results of this study suggest that it is essential to ensure there are factors that can influence knowledge communities. It is also vital to reinforce each factor has its unique influence on related knowledge community. Thus, these changeable knowledge communities should be transformed into an adequate type with proper business strategies and objectives. They also should be progressed into a type that covers varioustypes of knowledge communities. For example, DCInside started from a small specialty community focusing on digital camera hardware and camerawork and then was transformed to an open community focusing on social issues through well-known photo galleries. NAVER started from a typical search engine and now covers an open community and a special community through additional web services such as NAVER knowledge iN, NAVER Cafe, and NAVER Blog. NAVER is currently competing withan activity community such as Wikipedia through the NAVER encyclopedia that provides similar services with NAVER encyclopedia's users as Wikipedia does. Finally, the results of this study provide meaningfully practical guidance for practitioners in that which type of knowledge community is most appropriate to the fluctuated business environment as knowledge community itself evolves with time.

• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.99-112
• /
• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.8
• /
• pp.555-564
• /
• 2013

Innovative evolution in mobile devices along with recent spread of Tablet PCs and Smart Phones makes a new change not only in individual life but also in enterprise applications. Especially, in the case of medium-large mobile applications for large enterprises which generally takes more than 3 months of development periods, importance and complexity increase significantly. Generally Agile-methodology is used for a development process for the medium-large scale mobile applications, but some issues arise such as high dependency on skilled developers and lack of detail development directives. In this paper, S-MADP (Smart Mobile Application Development Process) is proposed to mitigate these issues. S-MADP is a service oriented development process extending a object-oriented development process, for medium-large scale mobile applications. S-MADP provides detail development directives for each activities during the entire process for defining services as server-based or client-based and providing the way of reuse of services. Also, in order to support various user interfaces, S-MADP provides detail UI development directives. To evaluate the performance of S-MADP, three mobile application development projects were conducted and the results were analyzed. The projects are 'TBS(TB Mobile Service) 3.0' in TB company, mobile app-store in TS company, and mobile groupware in TG group. As a result of the projects, S-MADP accounts for more detailed design information about 'Minimizing the use of resources', 'Service-based designing' and 'User interface optimized for mobile devices' which are needed to be largely considered for mobile application development environment when we compare with existing Agile-methodology. Therefore, it improves the usability, maintainability, efficiency of developed mobile applications. Through field tests, it is observed that S-MADP outperforms about 25% than a Agile-methodology in the aspect of the required man-month for developing a medium-large mobile application.

• KDI Journal of Economic Policy
• /
• v.14 no.2
• /
• pp.55-75
• /
• 1992

At the outset of import liberalization, most economists expected a significant drop in the prices of domestic goods that faced foreign competition. However, it is now generally acknowledge that a significant drop in prices of those goods has not occurred. A common claim is that the prices did not drop significantly because the major importers of many imported goods were also the domestic producers of competing goods. The objective of this paper is to analyze the welfare effect of importation by domestic firms that produce competing goods, to identify the factors that facilitate such business practices, and to formulate a policy that could improve the welfare. We proved that importation by competing domestic firms definitely raises the prices of both imported and domestic goods compared to the situation where foreign goods are imported by non-producers, ceteris paribus. The intuition behind this result is that since a producer-importer is essentially a cartel, its overall profit maximization requires reduced competition between the products that it sells. On the other hand, if a producer-importer is more efficient at distrinbution than a simple importer, the comparison between the two cases is a priori indeterminate. We also find that the industries in which domestic producers are actively involved in importing competing goods are the ones in which the distribution channels are tightly controlled by importer-producers. This finding suggests that exclusive dealing contracts, which work as an entry barrier, may be the source of importing by domestic producers. We argue that in a country such as Korea, where financial market is highly incomplete, tight control of the distribution channels by oligopolistic manufacturers is likely to be an effective entry barrier that leads to importing by domestic producers of similar goods. We further argue that seemingly superior distribution costs of importer-producers is likely to be a result of market foreclosure which would disappear once the entry barrier of exclusive dealing contracts is removed. Above findings suggest that market imperfections are the source of importation by domestic competitors, which in turn constitutes a market imperfection in itself and reduces consumer welfare. As potential remedies, we considered three alternatives; direct price control by the government over the imported goods sold by major domestic producers, regulation of trade itself between major producers, and regulation of exclusive dealing contracts. For reasons both theoretical and pratical, we find that the last alternative is the most attrative. Prohibiting exclusive contracts between manufacturers and dealers in industries where exclusive dealing contracts are a significant entry barrier is expected to break up the importer-producer cartel and improve the welfare.

• Korean Chemical Engineering Research
• /
• v.45 no.6
• /
• pp.638-647
• /
• 2007

In this study, we present the evaluation chart for assessing the applicability of $CO_2$ flooding method to oil reservoirs. The evaluation chart consists of four categories as source availability, miscibility, applicability and injecting method of miscible flooding. The applicability of reservoir and oil in the chart has basic items of the properties such as oil gravity, viscosity, oil saturation, reservoir temperature and permeability, and these are quantitatively graded. Meanwhile, for additional items of $CO_2$ purity, reservoir thickness and formation dip, they are graded as "highmediumlow". In the case of evaluating the injection method of either continuous injection or WAG ($CO_2$), the qualitative decision will be made according to formation dip, vertical permeability, reservoir thickness, etc. The recommended score in the chart was assigned by utilizing 51 oil producing fields which $CO_2$ flooding is successfully being applied. The evaluation chart developed in this work has been applied to the Captain oil producing field located in Scotland as well as to the Onado oil field of Venezuela, which Korean oil companies have participated in. For the Captain field, the reservoir quality in terms of permeability and porosity is considered to be very excellent to flow the oil. The oil in captain field contains heavier component of $C_{21+}$ as 54%. Therefore, this heavy oil could be immiscibly displaced, hence the evaluating result with the basis of immiscible criteria shows that $CO_2$ immiscible flooding in this field could be properly applied. In the case of Onado oil producing field, since the estimated minimum miscibility pressure is lower than the reservoir pressure, it was assessed that the Onado field would be efficiently conducted for $CO_2$ miscible flooding.

• Management & Information Systems Review
• /
• v.39 no.1
• /
• pp.93-114
• /
• 2020

Platform is considered as an alternative strategy to the traditional linear pipeline based business. Moreover, in the 4th industrial revolution period, efficiency driven pipeline business model needs to be changed to platform business. We have such success stories about platform as Apple, Google, Amazon, Uber, and so on. However, for those smaller corporations, it is not easy to find out the transformation strategy. The essence of platform business is to leverage network effect in management. Thus platform based management can be rephrased as network management across the business functions. Research on platform business is popular and related to diverse facets. But few scholars cover what the research trend of the domain is. The main purpose of this paper is to identify the research trend on platform business in Korea. To do that we first propose the analytical model for platform architecture whose components are consumers, suppliers, artifacts, and IT platform system. We conjecture that mapping of the research work on platform to the components of the model will make us understand the hidden domain of platform research. We propose three hypotheses regarding the characteristics of research and one proposition for the transitional path from pipeline to platform business model. The mapping is based on the research articles filtered from the Korea Citation Index, using keyword search. Research papers are searched through the keywords provided by authors using the word of "platform". The filtered articles are summarized in terms of the attributes such as major component of platform considered, platform type, main purpose of the research, and research method. Using the filtered data, we test the hypotheses in exploratory ways. The contribution of our research is as follows: First, based on the findings, scholars can find the areas of research on the domain: areas where research has been matured and territory where future research is actively sought. Second, the proposition provided can give business practitioners the guideline for changing their strategy from pipeline to platform oriented. This research needs to be considered as exploratory not inferential since subjective judgments are involved in data collection, classification, and interpretation of research articles.

• Journal of Digital Convergence
• /
• v.18 no.10
• /
• pp.81-93
• /
• 2020

This study is based on workplace learning, which has revealed its significant influence in the previous enterprise case studies. Why do small business owners have the opportunity to participate in workplace learning based on authenticity? It was intended to clarify whether it was necessary and to increase the growth and development potential of small business owners based on its contents. Moreover, this study is focused on identifying the influence of workplace learning on management performance through this series of processes. In order to investigate the influence of small enterprise workplace learning on management performance, research hypotheses were set based on a review of previous studies, and empirical analysis was carried out. A total of 203 questionnaires were empirically analyzed using SPSS 18.0 program. As a result, first, workplace learning had partially significant positive influence on job satisfaction. Second, workplace learning had significant positive influence on management performance. Third, job satisfaction had significant positive influence on management performance. Fourth, job satisfaction had partial mediating effect in the relationship between workplace learning and management performance. The analysis result showed that among sub-factors of workplace learning, only formal learning did not affect job satisfaction and that job satisfaction did not have mediating effect in the relationship between formal learning and management performance. According to analysis, this was because in poor small enterprise environments, opportunities to participate in formal learning like external training or in-house training were not kept. In other words, poor small enterprise environments were plainly revealed from the managerial, economic and social standpoint. Therefore, there is a need to establish the foundation of growth for them to solve problems and develop win-win development capabilities and an institutional system that can make a contribution to policy and education, and management, by helping small enterprises keep opportunities to participate in workplace learning. In spite of these significant study results, there can be a limitation. For improving this limitation, further research will need to target diverse fields focusing on samples, which can explain relations of many different variables. Also, working-level relation research connected to studies that can highly enhance management performance will be required.

• Journal of Bio-Environment Control
• /
• v.29 no.2
• /
• pp.110-119
• /
• 2020

This study investigates the improvement in the performance of a temperature and humidity measuring system developed by Company A using the Aspirated Radiation Shield (ARS). The shield has been used in the industry and its accuracy was verified recently. The study also experimentally examines the impact of the wind speed of the ARS device on temperature and humidity. The results are summarized as follows. Before the improvement of the system, the temperature of Company A's system was up to 10.2℃ higher than that measured by the ARS device, and the measured relative humidity was approximately 20.0% lower. After improving the system, the temperature and relative humidity of nodes 1 and 2 were found to be almost identical. The temperature deviations including the maximum, mean, and minimum temperatures between those measured in node 2 and by ARS were approximately 0.2 to 0.7℃, respectively; further, the values measured by ARS tended to be slightly lower or higher. In terms of relative humidity, the ARS measurements yielded values approximately 10.0% higher immediately after sunset; otherwise, the values were approximately 1.9% lower. Moreover, when node 1 was set to minimum-middle, middle-maximum, and maximum, the deviations including the maximum, mean, and minimum temperatures of nodes 1 and 2 were 0.1 to 0.4℃, 0.0 to 0.2℃, and 0.0 to 0.5℃, respectively. The deviations including the maximum, average, and minimum temperatures of the three points of node 1 and the ARS ranged from 0.2 to 0.5℃, 0.1 to 2.2℃, and 0.1 to 1.1℃, respectively, indicating that the temperature deviation according to the wind speed was negligible. In addition, considering the results of the previous study with those from this study, the optimal wind speed to improve the temperature deviation is considered to be in the range of 1.0~2.0 m·s^-1.