• Title/Summary/Keyword: Sales Prediction

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Development of a Sales Prediction Model of Electronic Appliances using Artificial Neural Networks (인공신경망을 이용한 가전제품의 판매예측모델 개발)

  • Seo, Kwang-Kyu
    • 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.

Sales Prediction of Electronic Appliances using a Convergence Model based on Artificial Neural Network and Genetic Algorithm (인공신경망과 유전자 알고리즘 기반의 융합모델을 이용한 가전제품의 판매예측)

  • Seo, Kwang-Kyu
    • Journal of Digital Convergence
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    • v.13 no.9
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    • pp.177-182
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    • 2015
  • The brand and product awareness of Korean electronics companies in the North American market has grown significantly and North American consumers has been recognized as an innovative technology products good performance of Korean electronics appliances. The consumer need of energy saving has led to a rise in market share because Korean electronics appliances have the excellence in energy saving aspects. The expansion of smartphones and mobile devices and the development of smart grid technology can affect electronics market. Domestic companies are continuously develop new product to provide consumers convenient with a variety of additional features combined consumer products. This study proposes a convergence model for sales prediction of electronic appliances using sales data of A company from the North American market. We develop the convergence model for sales prediction based on based on artificial neural network and genetic algorithm. In addition, we validate the superiority of the proposed convergence model by comparing the prediction performance of traditional prediction models.

Store Sales Prediction Using Gradient Boosting Model (그래디언트 부스팅 모델을 활용한 상점 매출 예측)

  • Choi, Jaeyoung;Yang, Heeyoon;Oh, Hayoung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.25 no.2
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    • pp.171-177
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    • 2021
  • Through the rapid developments in machine learning, there have been diverse utilization approaches not only in industrial fields but also in daily life. Implementations of machine learning on financial data, also have been of interest. Herein, we employ machine learning algorithms to store sales data and present future applications for fintech enterprises. We utilize diverse missing data processing methods to handle missing data and apply gradient boosting machine learning algorithms; XGBoost, LightGBM, CatBoost to predict the future revenue of individual stores. As a result, we found that using median imputation onto missing data with the appliance of the xgboost algorithm has the best accuracy. By employing the proposed method, fintech enterprises and customers can attain benefits. Stores can benefit by receiving financial assistance beforehand from fintech companies, while these corporations can benefit by offering financial support to these stores with low risk.

LSTM-based Sales Forecasting Model

  • Hong, Jun-Ki
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.4
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    • pp.1232-1245
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    • 2021
  • In this study, prediction of product sales as they relate to changes in temperature is proposed. This model uses long short-term memory (LSTM), which has shown excellent performance for time series predictions. For verification of the proposed sales prediction model, the sales of short pants, flip-flop sandals, and winter outerwear are predicted based on changes in temperature and time series sales data for clothing products collected from 2015 to 2019 (a total of 1,865 days). The sales predictions using the proposed model show increases in the sale of shorts and flip-flops as the temperature rises (a pattern similar to actual sales), while the sale of winter outerwear increases as the temperature decreases.

Pizza Sales Prediction by Using Big Data Analysis. (빅데이터 분석을 통한 피자 판매량 예측)

  • Lee, Daebum;Kim, Kyoungsup;Lee, Youngsoo;Kim, Hanahan;Byun, Dongsam;Park, Sungchul;Jeon, Hwaseong;Kim, Juntae
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.890-893
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    • 2014
  • IT산업의 새로운 패러다임으로 빅데이터 분석이 주요한 기술로 부각되고 있다. 본 논문에서는 빅데이터를 수집, 분석하여 이를 통해 피자 판매량을 예측하는 모델을 제안한다. 판매량 예측을 위하여 과거 판매 데이터와 함께 공휴일, 날씨, 뉴스기사, 경제지표, 트렌드, 스포츠 이벤트 등의 데이터를 수집하여 이용하였으며, 판매량 예측 방법으로는 회기분석과 인공신경망 학습 등을 사용하여 빅데이터를 사용하지 않은 경우와 정확도를 비교하였다. 실험 결과 빅데이터를 이용함으로써 예측 오차율이 5%이상 향상됨을 확인하였다.

Sales Volume Prediction Model for Temperature Change using Big Data Analysis (빅데이터 분석을 이용한 기온 변화에 대한 판매량 예측 모델)

  • Back, Seung-Hoon;Oh, Ji-Yeon;Lee, Ji-Su;Hong, Jun-Ki;Hong, Sung-Chan
    • 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.

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Research on Prediction of Consumable Release of Imported Automobile Utilizing System Dynamics - Focusing on Logistics Center of A Imported Automobile Part (시스템다이내믹스를 활용한 수입 자동차 소모품 출고예측에 관한 연구 - A 수입 자동차 부품 물류센터를 중심으로)

  • Park, Byooung-Jun;Yeo, Gi-Tae
    • 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.

T-Commerce Sale Prediction Using Deep Learning and Statistical Model (딥러닝과 통계 모델을 이용한 T-커머스 매출 예측)

  • Kim, Injung;Na, Kihyun;Yang, Sohee;Jang, Jaemin;Kim, Yunjong;Shin, Wonyoung;Kim, Deokjung
    • Journal of KIISE
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    • v.44 no.8
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    • pp.803-812
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    • 2017
  • T-commerce is technology-fusion service on which the user can purchase using data broadcasting technology based on bi-directional digital TVs. To achieve the best revenue under a limited environment in regard to the channel number and the variety of sales goods, organizing broadcast programs to maximize the expected sales considering the selling power of each product at each time slot. For this, this paper proposes a method to predict the sales of goods when it is assigned to each time slot. The proposed method predicts the sales of product at a time slot given the week-in-year and weather of the target day. Additionally, it combines a statistical predict model applying SVD (Singular Value Decomposition) to mitigate the sparsity problem caused by the bias in sales record. In experiments on the sales data of W-shopping, a T-commerce company, the proposed method showed NMAE (Normalized Mean Absolute Error) of 0.12 between the prediction and the actual sales, which confirms the effectiveness of the proposed method. The proposed method is practically applied to the T-commerce system of W-shopping and used for broadcasting organization.

Deep Learning-based Technology Valuation and Variables Estimation (딥러닝 기반의 기술가치평가와 평가변수 추정)

  • Sung, Tae-Eung;Kim, Min-Seung;Lee, Chan-Ho;Choi, Ji-Hye;Jang, Yong-Ju;Lee, Jeong-Hee
    • The Journal of the Korea Contents Association
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    • v.21 no.10
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    • pp.48-58
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    • 2021
  • For securing technology and business competences of companies that is the engine of domestic industrial growth, government-supported policy programs for the creation of commercialization results in various forms such as 『Technology Transaction Market Vitalization』 and 『Technology Finance-based R&D Commercialization Support』 have been carried out since 2014. So far, various studies on technology valuation theories and evaluation variables have been formalized by experts from various fields, and have been utilized in the field of technology commercialization. However, Their practicality has been questioned due to the existing constraint that valuation results are assessed lower than the expectation in the evaluation sector. Even considering that the evaluation results may differ depending on factors such as the corporate situation and investment environment, it is necessary to establish a reference infrastructure to secure the objectivity and reliability of the technology valuation results. In this study, we investigate the evaluation infrastructure built by each institution and examine whether the latest artificial neural networks and deep learning technologies are applicable for performing predictive simulation of technology values based on principal variables, and predicting sales estimates and qualitative evaluation scores in order to embed onto the technology valuation system.