A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones

In today’s competitive market, sales forecasting of newly released and short-term products is an important challenge because there is not enough sales data. To address these challenges, we propose a sales forecasting model for new-released and short-term products and study the case of mobile phones....

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Bibliographic Details
Published in:Electronics (Basel) 2023-08, Vol.12 (15), p.3256
Main Authors: Hwang, Seongbeom, Yoon, Goonhu, Baek, Eunjung, Jeon, Byoung-Ki
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Cell phones
Cellular telephones
Correlation coefficients
Crude oil prices
Deep learning
Forecasting
Machine learning
Mathematical models
Model accuracy
Neural networks
Product development
Product differentiation
Recurrent neural networks
Regression analysis
Root-mean-square errors
Sales
Sales forecasting
Supply and demand
Support vector machines
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Summary:In today’s competitive market, sales forecasting of newly released and short-term products is an important challenge because there is not enough sales data. To address these challenges, we propose a sales forecasting model for new-released and short-term products and study the case of mobile phones. The main approach is to develop an integrated sales forecasting model by training the sales patterns and product characteristics of the same product category. In particular, we analyze the performance of the latest 12 machine learning models and propose the best performance model. Machine learning models have been used to compare performance through the development of Ridge, Lasso, Support Vector Machine (SVM), Random Forest, Gradient Boosting Machine (GBM), AdaBoost, LightGBM, XGBoost, CatBoost, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). We apply a dataset consisting of monthly sales data of 38 mobile phones obtained in the Korean market. As a result, the Random Forest model was selected as an excellent model that outperforms other models in terms of prediction accuracy. Our model achieves remarkable results with a mean absolute percentage error (MAPE) of 42.6258, a root mean square error (RMSE) of 8443.3328, and a correlation coefficient of 0.8629.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12153256