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Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing

Author

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  • Chien-Chih Wang

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24303, Taiwan)

  • Hsin-Tzu Chang

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24303, Taiwan)

  • Chun-Hua Chien

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24303, Taiwan)

Abstract

Demand forecasting plays a crucial role in a company’s operating costs. Excessive inventory can increase costs and unnecessary waste can be reduced if managers plan for uncertain future demand and determine the most favorable decisions. Managers are demanding increasing accuracy in forecasting as technology advances. Most of the literature discusses forecasting results’ inaccuracy by suspending the model and reloading the data for model retraining and correction, which is extensively employed but causes a bottleneck in practice since users do not have the sufficient ability to correct the model. This study proposes an error compensation mechanism and uses the individuals and moving-range (I-MR) control chart to evaluate the requirement for compensation to solve the current bottleneck using forecasting models. The approach is validated using the case companies’ historical data, and the model is developed using a rolling long short-term memory (LSTM) to output the predicted values; then, five indicators are proposed for screening to determine the prediction statistics to be subsequently employed. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) compare the LSTM, rolling LSTM combined index, and LSTM-autoregressive moving average (ARMA) models. The results demonstrate that the RMSE, MAPE, and MAE of LSTM-ARMA are smaller than those of the other two models, indicating that the error compensation mechanism that is proposed in this study can enhance the prediction’s accuracy.

Suggested Citation

  • Chien-Chih Wang & Hsin-Tzu Chang & Chun-Hua Chien, 2022. "Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing," Mathematics, MDPI, vol. 10(13), pages 1-16, June.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2158-:d:843767
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    References listed on IDEAS

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    Cited by:

    1. Yongshi Liu & Xiaodong Yu & Jianjun Zhao & Changchun Pan & Kai Sun, 2022. "Development of a Robust Data-Driven Soft Sensor for Multivariate Industrial Processes with Non-Gaussian Noise and Outliers," Mathematics, MDPI, vol. 10(20), pages 1-16, October.
    2. Yue Tan & Liyi Gu & Senyu Xu & Mingchao Li, 2024. "Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach," Mathematics, MDPI, vol. 12(4), pages 1-30, February.

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