• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

• Journal of the Korean earth science society
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• v.32 no.7
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• pp.750-760
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• 2011

In this study, the reproducibility of the simulated current climate by using two regional climate models, such as Seoul National University Regional Climate Model (SNURCM) and Weather Resuearch and Forecasting (WRF), is evaluated in advance to produce the standard regional climate scenario of future climate. Within the evaluation framework of a COordinated Regional climate Downscaling EXperiment (CORDEX), 28-year-long (1978-2005) regional climate simulation was conducted by using the Hadley Centre Global Environmental Model (HadGEM2-AO) global simulation data of the National Institute of Meteorological Research (NIMR) as a lateral boundary forcing. The simulated annual surface temperatures were in good agreement with the observation; the spatial correlation coefficients between each model and observation were over 0.98. The cold bias, however, were shown over the northern boundary in the both simulated results. In evaluation of the simulated precipitation, the skill was reasonable and good. The spatial correlation coefficients for the precipitation over the land area were 0.85 and 0.79 in SNURCM and WRF, respectively. It is noted that two regional climate models (RCMs) have different characteristics for the distribution of precipitation over equatorial and midlatitude areas. SNURCM shows better distribution of the simulated precipitation associated with the East Asia summer monsoon in the mid-latitude areas, but WRF shows better in the equatorial areas in comparison to each other. The simulated precipitation is overestimated in summer season (JJA) rather than in spring season (MAM), whereas the spatial distribution of the precipitation in spring season corresponds to the observation better than in summer season. Also the RCMs were capable of reproducing the annual variability of the maximum amount and its timing in July, in which the skills over the inland area were in better agreement with the observation than over the maritime area. The simulated regional climates, however, have the limitation to represent the number of days for extremely hot temperature and heavy rainfall over South Korea.

• Korean Journal of Construction Engineering and Management
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• v.10 no.4
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• pp.87-100
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• 2009

The quality of early cost estimates is critical to the feasibility analysis and budget allocation decisions for public capital projects. Various researches have been attempted to develop cost prediction models in the early stage of a construction project. However, existing studies are limited on its applicability to actual projects because they focus primarily on a specific phase as well as utilize restricted information while the amount of information collectable differs from one another along with the project stages. This research aims to develop two-staged cost estimation model for the schematic planning and preliminary design process of a construction projects, considering the available information of each phase. In the schematic planning stage where outlined information of a project is only available, the Case-Based Reasoning model is used for easy and rapid elicitation of a project cost based on the extensive database of more than 90 actual highway construction projects. Then, the representing quantity-based model is proposed for the preliminary design stage where more information on the quantities and unit costs are collectable based on the alternative routes and cross-sections of a highway project. Real case studies are used to demonstrate and validate the benefits of the proposed approach. Through the two-stage cost estimation system, users are able to hold a timely prospect to presume the final cost within the budge such that feasibility study as well as budget allocation decisions are made on effectively and competitively.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.1047-1058
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• 2003

Seasonal occurrence of high ammonia nitrogen(NH3-N) concentrations has hampered chemical treatment processes of a water plant that intakes water at Buyeo site of Geum river. Thus it is often needed to quantify the effect of Daecheong Dam ouflow on the mitigation of $NH_3$-N contamination. In this study, multiple regression models were developed for forecasting daily $NH_3$-N concentrations using 8 years of water quality and dam outflow data, and verified with another 2 years of data set. During model development, the coefficients of determination($R^2$) and model efficiency($E_{m}$) were greater than 0.95. The verification results were also satisfactory although those statistical indices were slightly reduced to 0.84∼0.94 and 0.77∼0.93, respectively. The validated model was applied to assess the effect of different amounts of dam outflow on the reduction of $NH_3$-N concentrations in 2002. The NH3-N concentrations dropped by 0.332∼0.583 mg/L on average during January∼March as outflow increases from 5 to 50cms, and was most significant on February. The results of this research show that the multiple regression approach has potential for efficient cause and effect analysis between dam outflow and downstream water quality.

• Journal of Distribution Research
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• v.14 no.1
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• pp.45-65
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• 2009

As a number of people using the internet for their shopping steadily rises, it is increasingly important for retailers to understand why consumers decide to buy products via online or offline. The main purpose of this study is to develop and test a model that enhance our understanding of how consumers respond future online and offline channels for their purchasing. Rather than merely adopting statistical models like most other studies in this field, the present study develops a model that combines double-cohort method with multinomial logit model. It is desirable if one can adopt an overall encompassing criterion in the study of consumer behaviors form diverse sales channels. This study uses the concept of cohort or aging to enable this comparison. It enables us to analyze how consumers respond to online and offline channels as people aged by measuring their shopping behavior for an online and offline retailers and their subsequent purchase intentions. Based on some empirical findings, this study concludes with policy implications and some necessary fields of future studies desirable.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.1
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• pp.17-25
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• 2013

In this paper we interpreted the changes in wind field over complex mountainous terrains. The results of our study can be applied for predicting the direction of fire spread and for establishing strategies for fire prevention. The study area is bounded by $12{\times}12$ km domains of the Samcheok's long-term ecological research (LTER) site located in the east coast, in which a large-fire had occurred from 7 to 13 April 2000. Because of the area's complex topography, we compared the result of the Weather Research and Forecasting (WRF) mesoscale model with those observed by four automated weather stations. The WRF simulation overestimated the wind speed by 5 to 8 m/s (~200%) in comparison with those from four automated weather stations. The wind directions observed by the AWSs were from various directions whereas those from WRF model were mostly west wind at all stations. Overall, the simulations by the WRF mesoscale models were not appropriate for the estimation of microscale wind fields over complex mountainous areas. To overcome such inadequacy of reproducing the wind fields, we employed the ENVI-met model over Samcheok's LTER site. In order to test the model's sensitivity with the terrain effects, experimental simulations were conducted with various initial conditions. The simulation results of the ENVI-met model showed a reasonable agreement in wind speeds (about 70% accuracy) with those of the four AWSs. Also, that the variations in wind directions agreed reasonably well with changes in terrain effect. We concluded that the ENVI-met model is more appropriate in representing the microscale wind field over complex mountain terrains, which is required to predict fire spread and to establish strategies for forest fire prevention.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.4
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• pp.95-104
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• 2010

The storage function model is one of the most commonly used models for flood forecasting and warning system in Korea. This paper studies the physical significance of the storage function model by comparing it with kinematic wave model. The results showed universal applicability of the storage function model to Korean basins. Through a comparison of the basic equations for the models, the storage function model parameters, K, P and $T_l$, are shown to be related with the kinematic wave model parameters, k and p. The analysis showed that P and p are identical and K and $T_l$ can be related to k, basin area, and coefficients of Hack's law. To apply the storage function model throughout the southern part of Korean peninsular, regional parameter relationships for K and $T_l$ were developed for watershed area using data from 17 watersheds and 101 flood events. These relationships combine the kinematic wave parameters with topographic information using Hack's Law.

• Journal of Korean Society of Transportation
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• v.31 no.3
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• pp.33-44
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• 2013

Personal Rapid Transit(PRT) has emerged as a promising transportation mode for transit-oriented sustainable communities. In this study, an alternative design of questionnaire survey is proposed in order to capture traveler's perception of an unexperienced transportation mode. This study aims at predicting the mode choice diversion behavior of potential PRT users who do not have experience of using it previously, considering their willingness-to-pay. The proposed model was applied to predict an aggregate forecast of PRT patronage for the city of Songdo where PRT is considered to be constructed. For validation of the proposed model, the price elasticity of PRT demand was analyzed, compared with existing models. The analysis results suggest that the proposed design of questionnaire survey is able to capture respondents' attitude and perception to unexperienced transportation mode in an effective manner. Also, they show that the proposed diversion rate model is more realistic than existing models in explaining the effects of users' willingness-to-pay for predicting PRT patronage.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.82-93
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• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).

• Smart Media Journal
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• v.7 no.1
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• pp.16-23
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• 2018

In this paper, we used the Deep Neural Network (DNN) to predict the number of daily spectators of Gwangju - KIA Champions Field in order to provide marketing data for the team and related businesses and for managing the inventories of the facilities in the stadium. In this study, the DNN model, which is based on an artificial neural network (ANN), was used, and four kinds of DNN model were designed along with dropout and batch normalization model to prevent overfitting. Each of four models consists of 10 DNNs, and we added extra models with ensemble model. Each model was evaluated by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The learning data from the model randomly selected 80% of the collected data from 2008 to 2017, and the other 20% were used as test data. With the result of 100 data selection, model configuration, and learning and prediction, we concluded that the predictive power of the DNN model with ensemble model is the best, and RMSE and MAPE are 15.17% and 14.34% higher, correspondingly, than the prediction value of the multiple linear regression model.