• Journal of Digital Convergence
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• v.19 no.1
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• pp.67-75
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• 2021

Despite the increase in sales of imported vehicles in Korea, research on the sales forecast of parts logistics centers is very limited. This study aims to perform a sales prediction on bestselling goods in the automobile part logistics center. System dynamics was adopted as a methodology for the prediction method, which considered causal relationship of variables that affected the dynamic characteristics and feedback loops. The analysis results showed that the consumable sales amount of oil increased over time. As a result of conducting the MAPE, the model was assessed to be a reasonable predictive model of 31.3%. In addition, the sales of battery products increased from every October in both of actual and predicted data followed by the peak sales in December and then decrease from next February. This study has academic implications that it secured actual data of specific imported automobile part logistics center, which has not done before in previous studies and quantitatively analyzed the prediction of the quantity of released goods of future sales through system dynamics.

• JOURNAL OF ELECTRICAL WORLD
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• no.12 s.6
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• pp.31-39
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• 1966

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.179-186
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• 2022

This study aims to develop a predictive model to suggest a used sales price to sellers and buyers when trading used tablet PCs. For model development, we analyzed the real used tablet PC transaction data and additionally collected detailed product information. We developed several predictive models and selected the best predictive model among them. Specifically, we considered a multiple linear regression model using the used sales price as a dependent variable and other variables in the integrated data as independent variables, a multiple linear regression model including interactions, and the models from stepwise variable selection in each model. The model with the best predictive performance was finally selected through cross-validation. Through this study, we can predict the sales price of used tablet PCs and suggest appropriate used sales prices to sellers and buyers.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.209-214
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• 2014

Despite the recession of the global market, the domestic electronic appliance companies dominated TV market in North America. They took both the premium and mid-priced product market and achieved both profitability and volume due to strong product competitiveness and brand power. Despite doing well in the North American market, the domestic TV manufacturers are worried about product development, marketing and sales strategies to remain the continuous competitiveness in the TV market. This study proposes the a sales prediction model of electronic appliances using sales data of S company from the North American market. We develop the sales prediction models based on multiple regression analysis and artificial neural network and compare two models. Especially, this study analyzes the relevance between the TV sales and TV main features in order to improve the price competitiveness or improve the value of TV products.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.401-404
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• 2022

한국 문학이 세계로 뻗어나감에 따라 해외 시장에서 자리를 잡는 것이 중요해진 시점이다. 본 연구에서는 2016 년도부터 2020 년도까지 최근 5 년간 해외 출간된 도서들 중에서 굿셀러로 분류되는 누적 5 천부 이상 판매 여부를 예측하고자 했다. 굿셀러로 분류되는 도서는 전체 번역 도서 중 적은 비율을 차지하여 데이터 불균형이 발생하였으며, 본 연구에서는 SMOTE 기법과 앙상블 알고리즘을 적용하여 데이터 불균형 문제를 해결하였다. 그 결과, 데이터 클래스 비율이 1:1 에 가까울수록 성능 개선 효과가 나타났으며 LightGBM 모델이 99.83%의 AUC 값을 얻어 다른 앙상블 알고리즘에 비해 가장 좋은 예측 성능을 보임을 검증하였다. 또한 누적 5 천부 이상 판매 여부 예측에 있어 큰 영향을 미치는 변수로는 작가가 가장 중요한 요인으로 나타났으며 출간 국가, 그리고 평점 평균, 평점 참여자 수 같은 온라인 요인도 판매 예측에 유의미한 변수로 나타난 것을 확인할 수 있었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.355-356
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• 2021

본 논문에서는 Python 3의 Keras 모듈을 이용하여 특정 자동차에 대한 최적의 판매자권장소비자가격(MSRP)을 예측하는 시스템을 제안한다. 이 시스템은 2004년에 미국에서 시판된 428종류의 자동차에 대한 정보를 제조사, 차종, 생산지, 엔진 크기, 실린더 수, 시내 주행 시 연비, 고속도로 주행 시 연비, 마력, 차체 무게, 차체 길이의 독립변수를 사용하여 자체적으로 딥러닝한 회귀모델을 통해 특정 지표가 주어진 차량에 대해 종속변수인 판매자권장소비자가격을 예측한다. Optimizer를 adam으로, 학습률을 0.005으로 설정한 경우의 검증 MAE 값이 3842.98로 가장 낮게 산출되었고, 해당 모델의 결과는 예측값과 실제값의 오차율이 ±15% 정도 내외로 예측된 표본의 비율이 약 80.14%로 측정되었다. 위 모델은 향후 신차 가격 결정 및 중고차 시장에서 구매, 판매 결정을 돕는 등 특정 시장 내에서 다양한 자동차의 가치를 판단하기에 유용할 것으로 전망된다.

• The Journal of Bigdata
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• v.4 no.1
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• pp.29-38
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• 2019

In this paper, we propose a sales forecasting model that forecasts the sales volume of short sleeves and outerwear according to the temperature change by utilizing accumulated big data from the online shopping mall 'A' over the past five years to increase sales volume and efficient inventory management. The proposed model predicts sales of short sleeves and outerwear according to temperature changes in 2018 by analyzing sales volume of short sleeves and outerwear from 2014 to 2017. Using the proposed sales forecasting model, we compared the sales forecasts of 2018 with the actual sales volume and found that the error rates are ±1.5% and ±8% for short sleeve and outerwear respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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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.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.118-129
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• 2022

The purpose of this report is to study a strategic model of promotion activities through various analysis and sales forecasting by selecting wearable products for domestic online companies and collecting sales data. For data analysis, various algorithms are used for analysis and the results are selected as the optimal model. The gradation boosting model, which is selected as the best result, will allow nine independent variables to be entered, including promotion type, price, amount, gender, model, company, grade, sales date, and region, when predicting dependent variables through supervised learning. In this study, the review values set as dependent variables for each type of sales promotion were studied in more detail through the ensemble analysis technique, and the main purpose is to analyze and predict them. The purpose of this study is to study the grades. As a result of the analysis, the evaluation result is 95% of AUC, and F1 is about 93%. In the end, it was confirmed that among the types of sales promotion activities, value-added benefits affected the number of reviews and review grades, and that major variables affected the review and review grades.

• 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.