• Journal of Distribution Science
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• v.15 no.10
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• pp.29-39
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• 2017

Purpose - The purpose of this study is to promote sales of the renewable energy industry and to advance into the Southeast Asian market. Research design, data, and methodology - This study is to develop a highly efficient food waste treatment system for Southeast Asian renewable energy industry. The radiation treatment method was applied for this purpose. Results - To investigate effects of ionization on removal of non-degradable organic matter, the results from gamma irradiation and co-digestion process was compared to those from a co-digestion process. Based on the BMP test results, food wastes were oxidized by hydroxyl radicals, and the specific methane yield was 366 mL CH4/g VS. Methane composition was 82%. A WAS/food wastes co-digestion was developed for the treated of non-degradable organic matter in food wastes. The average efficiency of non-degradable organic matter were 92.2% using the food waste co-digestion. Conclusions - Performance of gamma irradiation and co-digestion process was superior to that of a co-digestion process (10-20%). This implies that food wastes can be high efficient co-digested by the gamma irradiation. It is believed that it will be possible to enter the Southeast Asian energy industry as a strategic technology in the overseas energy recovery industry.

• The Journal of Society for e-Business Studies
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• v.15 no.1
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• pp.89-101
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• 2010

In some industries, manufacturing firms' distinctive competences include not only product superiority but also service excellence. For example, in a highly volatile and dynamic market, flexibility and responsiveness are more valued than just the product cost. And sales order processing, in that respect is a very important process. However, previous studies on sale order processing have considered only due-date assignments or dealt with production scheduling to minimize production cost or delivery lead time. Due to recent advances in information technology, a new approach is possible in dealing with sales order processing. For example, RFID(Radio Frequency IDentification) and ubiquitous computing technology adds real time visibility and traceability to supply chain. In this study, we develop a new due-date re-negotiation process for sales order management and propose a new business integration model to support the re-negotiation process based on RFID, ubiquitous computing technology and RosettaNet's PIPs.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.375-381
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• 2007

Cognitive map (CM) has long been used as an effective way of constructing the human thinking process. In literature regarding CM, a number of successful researches were reported, where CM based what-if analysis could enhance firm's performance. However, there exit very few researches investigating the CM generation method. Therefore this study proposes a GENCOM (Genetic Algorithm based Cognitive Map Generator). In this model combined with CM and GA, GA will find the optimal weight and input vector so that the CM generation. To empirically prove the effectiveness of GENCOM, we collected valid questionnaires from expert in S/W sales cases. Empirical results showed that GENCOM could contribute to effective CM simulation and very useful method to extracting the tacit knowledge of sales experts.

• Korean Management Science Review
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• v.20 no.1
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• pp.103-124
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. There are various methods to adjust seasonal effect such as moving average, extrapolation, smoothing and X11. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the Xl1-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method.

• Journal of information and communication convergence engineering
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• v.16 no.1
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• pp.52-59
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• 2018

Odoo is an enterprise resource planning information system providing modules to support the basic business function in companies. This research will look into the development of an additional module at Odoo. This module is a data mining module using Market Basket Analysis (MBA) using FP-Growth algorithm in managing OLTP of sales transaction to be useful information for users to improve the analysis of company business strategy. The FP-Growth algorithm used in the application was able to produce multidimensional association rules. The company will know more about their sales and customers' buying habits. Performing sales trend analysis will give a valuable insight into the inner-workings of the business. The testing of the module is using the data from X Supermarket. The final result of this module is generated from a data mining process in the form of association rule. The rule is presented in narrative and graphical form to be understood easier.

• Journal of Applied Reliability
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• v.3 no.1
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• pp.13-40
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• 2003

The seasonal adjustment is an essential process in analyzing the time series of economy and business. One of the powerful adjustment methods is X11-ARIMA Model which is popularly used in Korea. This method was delivered from Canada. However, this model has been developed to be appropriate for Canadian and American environment. Therefore, we need to review whether the X11-ARIMA Model could be used properly in Korea. In this study, we have applied the method to the annual sales of refrigerator sales in A electronic company. We appreciated the adjustment by result analyzing the time series components such as seasonal component, trend-cycle component, and irregular component, with the proposed method. Additionally, in order to improve the result of seasonal adjusted time series, we suggest the demand forecasting method base on autocorrelation and seasonality with the X11-ARIMA PROC.

• Korea Trade Review
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• v.44 no.6
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• pp.201-216
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• 2019

At present, there are various standards used as the governing law of contracts for establishing, implementing, and resolving disputes between the parties to international sales contracts, called "Forum Shopping." Uncertainty and increased transaction costs, which may arise from these various norms, may hinder the activation of international commerce. This study examines the process of enacting and examining various international unification norms that have emerged through international organizations to eliminate trade barriers caused by choice of governing law concerning parties involved in the international sale of goods. Issues regarding the positive and negative perspectives are discussed to identify obstacles to international unification norms. In particular, by comparing and analyzing the differences between the regulations of the CISG and PICC, the representative international unification norms on international sales contracts, the possibility of unification of the norms on international sales contracts are reviewed. Direction for the establishment of a single international regulation is presented for reducing the transaction costs and uncertainties in the international sale of goods.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.217-232
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• 2021

The Korea film industry has matured and the number of movie-watching per capita has reached the highest level in the world. Since then, movie industry growth rate is decreasing and even the total sales of movies per year slightly decreased in 2018. The number of moviegoers is the first factor of sales in movie industry and also an important factor influencing additional sales. Thus it is important to predict the number of movie audiences. In this study, we predict the cumulative number of audiences of films using stacking, an ensemble method. Stacking is a kind of ensemble method that combines all the algorithms used in the prediction. We use box office data from Korea Film Council and web comment data from Daum Movie (www.movie.daum.net). This paper describes the process of collecting and preprocessing of explanatory variables and explains regression models used in stacking. Final stacking model outperforms in the prediction of test set in terms of RMSE.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.4
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• pp.422-431
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• 2012

The influence of a marketing mix on the consumer purchasing process is well documented in academic fields; however, studies of when consumers purchase fashion brands or products in terms of the day-of-the-week effect and 10-days-of-the-month effect on various channels to a given brand in practice are limited in the fashion industry. This study empirically describes the purchase timing behavior for athletic shoes using daily sales data from a domestic brand in Korea from January in 2006 to December in 2010. The results show that Saturday and Sunday have significantly higher sales than weekdays. In addition, the first and third 10-days-of-the-month yield a higher sales volume than the second 10-days-of-the-month. The department store's sales volume (compared with discount and franchised stores) was higher in the first 10-days-of-the-month; however, the discount and franchised stores have a higher sales volume in the second 10-days-of-the-month. Three store types have no significant differences in sales volume for the third 10-days-of-the-month. Based on the results, in practice, marketers for a specific domestic brand can develop a new marketing expenditure plan, store supply plan, and cash-in and cash-out plan to maximize profits. This research can introduce constructs such as purchase timing distribution, the day-of-the-week effect, and the ten-days-of-the-month effect for the fashion industry.

• Korean Journal of Construction Engineering and Management
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• v.12 no.5
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• pp.127-136
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• 2011

The purpose of this study is analyzed the importance ratio factors of sales price for apartment building construction projects. For this Study, Nine apartment buildings that sold in Seoul, Gyenggi, Chuncheong is selected. The study suggested eight influence factors through interview with experts, research related references, and analysis of cases. Based on those factors, Importance analysis is performed by using AHP method. The result of this study are as follows: 1) Influence factors of sales price are drawn as cost, site, housing, transportation, region, ground, policy, investment factor. 2) In the case of Seoul, The priority order of price influence factors consist of investment factor(16.0%), policy factor(14.6%), and cost factor(13.9%). In the case of Gyenggi, The priority order of sales price influence factors consist of region factor(15.9%), transportation factor(13.8%), and investment factor(13.4%). In the case of Chungcheong, The priority order of sales price influence factors consist of region factor(17.1%), transportation factor(16.4%), and housing factor(14.9%).