• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.157-178
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• 2016

The purpose of this study is to forecast the ship-management market size and to propose a management improvement scheme to support Korean ship management companies in the stagnating world shipping market. Recently, global shipping companies have begun outsourcing all ship management activities. However, the Korean ship-management market represents just 3.75% of ocean shipping companies' sales, making it necessary to enlarge this market. This study performs a business analysis of ship management companies in Korea. The findings show that these companies' profitability and financial structures are not very good, mainly because of insufficient management ability and small firm sizes. Therefore, we propose that the Korean government supports crew training programs and shipping financial systems.

• Journal of the Korean Institute of Gas
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• v.25 no.3
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• pp.16-26
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• 2021

The global warming problem has arose, the supply eco-friendly vehicles such as HFCEVs is increasing around world and Korea is fully supporting subsidies, tax cut to form an initial market for HFCEVs. The key to the safety of HFCEVs is pressure vessels stored hydrogen, and although these pressure vessels must be inspection regularly, the existing inspection stations are insufficient to meet the demand for inspection. Therefore, it is important to establishment of pressure vessels inspection station for safety management of HFCEVs. In this study, it estimates innovation coefficient, imitation coefficient in Bass model by using electric vehicle sales data, and foretasted the supply of HFCEVs by region & the demand for inspection by region using the Bass diffusion model. As a result, the inspection demand for pressure vessels in HFCEVs in 2040 was 690,759 units, and it was confirmed 191 new inspection stations and 1,124 inspectors were needed to prepare for this.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.217-229
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• 2018

This paper aims at applying the data mining such as decision tree, neural network, and logistic regression to an unsold apartment complex in Wirye new town and developing the model forecasting the result of initial sale contract by house unit. Raw data are divided into training data and test data. The order of predictability in training data is neural network, decision tree, and logistic regression. On the contrary, the results of test data show that logistic regression is the best model. This means that logistic regression has more data adaptability than neural network which is developed as the model optimized for training data. Determinants of initial sale are the location of floor, direction, the location of unit, the proximity of electricity and generator room, subscriber's residential region and the type of subscription. This suggests that using two models together is more effective in exploring determinants of initial sales. This paper contributes to the development of convergence field by expanding the scope of data mining.

• The Journal of Bigdata
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• v.5 no.2
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• pp.1-15
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• 2020

The duty-free industry before COVID-19 has continuously grown since 2000, along with the increase of demand in tourism industry. To cope with the increased demand, the duty free companies have kept the strategies which focused on the sales volume. Therefore, they have developed the ways to increase the volume and capacity, not the efficient operations. In the most of previous research, however, authors have proposed the better strategies for marketing and supporting policies. It is very hard to find the previous research which dealt with the operations like logistics and inventory management. Therefore, in this study, it has been predicted the future demand of frequently purchased items in airport duty free shops based on the estimated number of departing passengers by the linear regression, which concluded with the appropriate inventory level. In addition, it has been analyzed the expected effects by introducing the inventory management policy considering the cost and efficiency of operations. Based on the results of this study, it may be possible to reduce total cost and improve productivity by predicting the excessive inventory problems at duty-free shops and improving cycles of supplying items.

• The Journal of Bigdata
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• v.7 no.2
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• pp.195-204
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• 2022

The tourism industry is facing a crisis due to the recent COVID-19 pandemic, and it is vital to improving profitability to overcome it. In situations such as COVID-19, it would be more efficient to sell additional products other than guest rooms to customers who have visited to increase the unit price rather than adopting an aggressive sales strategy to increase room occupancy to increase profits. Previous tourism studies have used machine learning techniques for demand forecasting, but there have been few studies on cross-selling forecasting. Also, in a broader sense, a resort is the same accommodation industry as a hotel. However, there is no study specialized in the resort industry, which is operated based on a membership system and has facilities suitable for lodging and cooking. Therefore, in this study, we propose a cross-selling prediction model using various machine learning techniques with an actual resort company's accommodation data. In addition, by applying the explainable artificial intelligence XAI(eXplainable AI) technique, we intend to interpret what factors affect cross-selling and confirm how they affect cross-selling through empirical analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.90-99
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• 2016

Forecasting of box office performance after a film release is very important, from the viewpoint of increase profitability by reducing the production cost and the marketing cost. Analysis of psychological factors such as word-of-mouth and expert assessment is essential, but hard to perform due to the difficulties of data collection. Information technology such as web crawling and text mining can help to overcome this situation. For effective text mining, categorization of objects is required. In this perspective, the objective of this study is to provide a framework for classifying films according to their characteristics. Data including psychological factors are collected from Web sites using the web crawling. A clustering analysis is conducted to classify films and a series of one-way ANOVA analysis are conducted to statistically verify the differences of characteristics among groups. The result of the cluster analysis based on the review and revenues shows that the films can be categorized into four distinct groups and the differences of characteristics are statistically significant. The first group is high sales of the box office and the number of clicks on reviews is higher than other groups. The characteristic of the second group is similar with the 1st group, while the length of review is longer and the box office sales are not good. The third group's audiences prefer to documentaries and animations and the number of comments and interests are significantly lower than other groups. The last group prefer to criminal, thriller and suspense genre. Correspondence analysis is also conducted to match the groups and intrinsic characteristics of films such as genre, movie rating and nation.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.5 s.153
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• pp.788-799
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• 2006

The purpose of this study was to analyze fashion merchandising process, to apply customer information in merchandising process and to examine customer management strategies of fashion industry in on-line and off-line channel. In depth, face to face interviews with structured questionnaires were conducted with MD and customer managers from selected 4 brands, one from each categories of men's, women's, casual and sports wear. Key findings of the study were as follows: First, they followed fashion merchandising process of 18 steps and collected trend information and sales data were applied to planning, selling/promoting process to plan season concept, design, and promotion activity. Second, commonly applied customer information types in fashion merchandising process were all from indirect information collected from sales data and forecasting companies. However, casual and sports wear conducted consumer monitoring activity f3r collecting customer data directly from customer participation. Third, in off-line channel, customers are segmented by amount of purchase they make in a specific time period and all the categories show high interest in valuable customers. However, only men's and woman's wear conducted promotion activities for valuable customers as a differentiated marketing strategy. In on-line channel, companies were interacting with the customers through internet web site to determine their demands. In conclusion, this study has significance in that it propose the necessity and strategy of differentiated customer management approaching by analyzing and comparing fashion merchandising activity process cases.

• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.121-133
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• 2017

The propose of study is to examine the characteristics of companies with high possibility to form an internal control weakness using forecasting model. This study use the actual listed/unlisted companies' data from K_financial institution. The first conclusion is that discriminant model is more valid than logit model to predict internal control weak companies. A discriminant model for predicting the vulnerability of internal control has high classification accuracy and has low the Type II error that is incorrectly classifying vulnerable companies to normal companies. The second conclusion is that the characteristic of weak internal control companies have a low credit rating, low asset soundness assessment, high delinquency rates, lower operating cash flow, high debt ratios, and minus operating profit to the net sales ratio. As not only a case of listed companies but unlisted companies which did not occur in previous studies are extended in this study, research results including the forecasting model can be used as a predictive tool of financial institutions predicting companies with high potential internal control weakness to prevent asset losses.

• Korean journal of applied entomology
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• v.22 no.2 s.55
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• pp.106-129
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• 1983

An emphasized analysis and reviews on the progress of pesticide managements for the past 10 years through the statistics in Korea are summarized in this continued studies in connection with the fundmental aspects and direction of advanced pesticide industry and improved plantprotection policies for 1980's. Remarkable development and changes are observed in the plant species and varieties, plantation practices and production techniques as well as pest infestations and controls in the last decade, but no normal achievement and operations are recognised on the pesticide management and marketing system especially. Realistic plant protection adminstration and pesticide regulations in accordance to the industrial modernization and pest management advancement must be adjusted in accordance with national economic progress and desirable agricultural structure for 1980's. Special considerations are stated on the strengthening of research and inspection program for the quality products and control with the efficacy and safety use of pesticides. More serious attentions are noted on the over production and flooded stocks under struggled market demands and sales competitions with lethal financial difficulties by producers. Through the status analyzed for the last decade, the integrated past management and cooperative basic control pattern under positive self-forecasting system by farmers are also urged for the effective and economic pest control measures. The problems and solutions discussed here ell the advanced pesticide management as well as the cooperation on the self-ordered quality control and market managing systems in 1980's as it is a desired projection for the further improvement. Most of outstanding and necessary statistics and data in the past decade are also summarized here for references in connection with the previous report.