발간년도 : [2023]
| 논문정보 |
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| 논문명(한글) |
[Vol.18, No.1] Automobile Suspension Parts Demand Forecasting Model Based on Deep Learning Algorithms |
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| 논문투고자 |
Sang-Gil Lee, Jun-Woo Kim |
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| 논문내용 |
Demand forecasting provides important information for operations management activities such as production scheduling and inventory management. Time series prediction is one of well-known approaches for demand forecasting, which assumes future demand is a function of previous demand amounts. However, the relationship between future demand and previous demand amounts is often quite complex, and it is non-trivial to approximate future demand accurately. In recent, artificial intelligence and deep learning algorithms have emerged as powerful tools to analyze complex relationships between many variables within datasets with high dimensionalities. Demand forecasting is also an important application domain of deep learning algorithms. This paper aims to develop demand forecasting model for automotive parts manufacturer by applying deep learning algorithms such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). The deep learning algorithms are applied to historical data from automobile suspension parts manufacturer, and the experiment results reveal the superior performance of deep learning algorithms. The authors also discuss configurations and practical benefits of deep learning algorithms for demand forecasting. |
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