• 디자인학연구
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• 제14권1호
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• pp.93-101
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• 2001

This study suggested how to apply it decision-making of product development rapidly by design evaluation process to objectify and the result to quantify with viewpoint of design evaluation sets to marketability. Coverage of this method limited to the evaluation stage of design concept. The procedure of study, first of all, referred to some type of design evaluation method and their feature. And next, referred to some kinds of demand forecasting for marketing. Above an, this study focused on the method of demand forecasting by buying intentions surveys proper to the marketability evaluation of new product design. On a case study, I had investigated preference survey and buying intentions surveys about the design proposal of "language master audio". I selected the best design proposal through the conjoint analysis and also investigated demand forecasting. First, on the basis of buying intentions surveys, choose population and had produced buying demand, awareness demand, potential demand. I could estimate some profit to take out expense and cost from the buying demand. This estimated profit is marketability judgement data of product design at the design concept stage and can be utilized to measurable data for decision-making of product development. Through the case study, this method could forecast a target demand, and even if it is some difference between real sales volume, but the case study could verified that this method is effective to the evaluation of marketability in case of completely new product got on the typical category and the product category could be set up the population clearly.

• 산경연구논집
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• 제9권7호
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• pp.7-18
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• 2018

Purpose - The purpose of this paper is to examine how clothing retail buyers (i.e., retail buyers, merchandisers, and storeowners), who are involved in assortment planning and retail buying use assortment criteria in their decisions. Comparisons are made between criteria used by men's wear and women's wear retail buyers as well as criteria used by male and female retail buyers. Research design, data, and methodology - A structured questionnaire was developed to collect data both in English and Korean. After conducting two pilot tests, the survey was conducted in Seoul, South Korea. Mantrala et al.'s 17 inputs of product assortment planning model with 23 assortment criteria from other previous studies were used. Results - Significant differences existed in consideration of assortment criteria between men's wear and women's wear retail buyers as well as between male and female retail buyers. Men's wear retail buyers rated the importance of sales history criteria (i.e., sales history, previous year's sales of same/similar styles) significantly lower than women's wear buyers did. Female retail buyers rated sales history criteria and weather criteria (i.e., unpredicted weather change, forecasting information of weather) significantly higher than male retail buyers did. Conclusions - This study provides guidelines for retail buyers regarding what criteria to use in what situations and how to organize assortment criteria from the most important criterion to the least one. In addition, the findings help them understand other retail buyers' buying behavior.

• 지능정보연구
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• 제7권1호
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• pp.97-121
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• 2001

본 연구에서는 웹 마이닝을 이용하여 기업과 소비자간 전자상거래(Business-To-Customer Electronic Commerce)환경에 기초한 가상상점(Cyber market)의 상품 관리자 입장에서 효율적인 상품관리를 가능케 하는 시스템적 접근방법을 통한 상품관리 방법론을 제시하고자 한다. 또한 이 상품 관리 방법론을 실제 웹 상에서 운영되고 있는 가상상점에 직접 적용하여 봄으로써 실증적인 예를 보여주고자 한다.

• Industrial Engineering and Management Systems
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• 제12권3호
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

• 한국빅데이터학회지
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• 제9권1호
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• pp.31-47
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• 2024

인공지능 기술의 발전으로 인해 여행 및 호텔 산업에서도 다양한 목적의 인공지능과 기계학습 기법이 활용되고 있다. 특히 관광 산업에서는 수요 예측이 매우 중요한 요소로 인식되는데, 이는 서비스 효율성과 수익 극대화에 직접적인 영향을 미치기 때문이다. 수요 예측 시 시간에 따라 변화하는 데이터 흐름을 고려해야 하며, 이를 위해 통계적 기법과 기계학습 모델이 사용된다. 최근에는 수요 예측 데이터의 다양성과 현실의 복잡성을 반영하고자 기존 모델의 변형과 통합 연구가 진행되고 있으며, 그 결과 불확실성과 변동성에 대한 예측 성능이 향상되었음이 보고되고 있다. 본 연구에서는 기존 호텔수요 예측 연구에서 시도되지 않았던 다양한 기계학습 접근법을 통합하여 호텔 판매 수요 예측 정확도를 높이는 새로운 모델을 제안한다. 구체적으로 DTW K-means 클러스터링을 통해 지역모델을 구축하고, 전체 데이터를 활용한 전역모델과 선택적으로 결합하는 XGBoost 기반 시계열 예측 모델을 제시한다. 제안 모델은 지역과 전역 모델의 장점을 살려 호텔 수요 예측 성능을 제고할 것으로 기대된다. 이는 호텔 및 여행 산업 성장에 기여할 뿐만 아니라, 향후 다른 경영 분야 예측에도 확장 적용될 수 있을 것이다.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2010년도 춘계학술대회
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• pp.402-408
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• 2010

Forecasting have qualitative and quantitative methods. Quantitative one analyze macro-economic factors such as the rate of exchange, oil price, interest rate and also predict the micro-economic factors such as sales and demands. Applying various statistical methods depends on the type of data. when data has seasonality and trend, Time Series analysis is proper but when it has casual relation, Regression analysis is good for this. Time Series and Regression can be used together. This study investigate artificial neural networks which is predictive technique for casual relation and try to compare the accuracy of forecasting between regression analysis and artificial neural network.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1998년도 The Korea Society for Simulation 98 춘계학술대회 논문집
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• pp.101-105
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• 1998

A large system predictor, which can perform prediction of sales trend in a huge number of distribution centers, is presented using neural predictive model. There are 20,000 number of distribution centers, and each distribution center need to forecast future demand in order to establish a reasonable inventory policy. Therefore, the number of forecasting models corresponds to the number of distribution centers, which is not possible to estimate that kind of huge number of accurate models in ERP (Enterprise Resource Planning)module. Multilayer neural net as universal approximation is employed for fitting the prediction model. In order to improve prediction accuracy, a sequential simulation procedure is performed to get appropriate network structure and also to improve forecasting accuracy. The proposed simulation procedure includes neural structure identification and virtual predictive model generation. The predictive model generation consists of generating virtual signals and estimating predictive model. The virtual predictive model plays a key role in tuning the real model by absorbing the real model errors. The complement approach, based on real and virtual model, could forecast the future demands of various distribution centers.

• 지능정보연구
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• 제8권2호
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• pp.159-175
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• 2002

웹 마이닝은 e-Business 환경하에서 존재하는 대량의 웹 데이터에 데이터 마이닝 기법을 적용하여 유용하고 이해 가능한 정보를 추출해내는 과정을 의미하는데, 성공적인 e-Business전개를 위한 핵심적인 기술이다. 본 논문은 인공지능 기법에 기반한 웹마이닝 기술을 활용하여 e-Business상의 온라인 고객의 특성을 분석할 수 있는 data visualization system과 구매 판매 예측시스템의 효과적인 구조와 핵심적인 분석절차를 제안하였다.

• Journal of information and communication convergence engineering
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• 제2권2호
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• pp.116-118
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• 2004

In the power industry after restructuring of Power industry will be appeared on-site type business, power retail sales business, and power wholesales business, power dealing business, customer inclination business ＆ delivery of power facilities. Among them, power trade business, customer inclination business and on-site type business will be rapidly increased and occupied attention. In addition, it is forecasted to advent the broker, provider, market place, power marketer, system operator and generator as a main player. Meanwhile, it needs protection of existing power industry and activation of new energy market for accomplishment of restructuring of power industry.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 춘계학술발표대회
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• pp.628-630
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• 2014

상품 판매량의 변화를 예측하는 것은 기업의 경영에 있어서 매우 중요한 요소이며, 상품의 재고 관리 등에 큰 도움을 줄 수 있다. 최근 여러 분야에서 그동안 수집된 방대한 양의 빅데이터를 분석하여 마케팅에 활용하려는 연구가 진행 중이다. 이 논문에서는 상품 판매 빅데이터로부터 고객의 특성에 따른 상품 판매량과 고객 특성별 상품 판매량의 변화 추이를 분석하고, 분석 결과를 바탕으로 각 상품별 판매량을 예측할 수 있는 방법을 제안한다. 이 방법을 활용하면 고객의 변화에 따른 상품의 판매량을 예측할 수 있으므로, 기업 경영에 있어서 생산관리, 전략수립, 마케팅 등에서 큰 효과를 얻을 수 있다.