IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i15p3757-d1446003.html
   My bibliography  Save this article

Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM)-Attention-Based Prediction of the Amount of Silica Powder Moving in and out of a Warehouse

Author

Listed:
  • Dudu Guo

    (School of Transportation Engineering, Xinjiang University, Urumqi 830017, China
    Xinjiang Key Laboratory of Green Construction and Smart Traffic Control of Transportation Infrastructure, Xinjiang University, Urumqi 830017, China)

  • Pengbin Duan

    (School of Business, Xinjiang University, Urumqi 830017, China)

  • Zhen Yang

    (Xinjiang Hualing Logistics & Distribution Co., Urumgi 830017, China)

  • Xiaojiang Zhang

    (Xinjiang Xinte Energy Logistics Co., Urumqi 830017, China)

  • Yinuo Su

    (School of Business, Xinjiang University, Urumqi 830017, China)

Abstract

Raw material inventory control is indispensable for ensuring the cost reduction and efficiency of enterprises. Silica powder is an essential raw material for new energy enterprises. The inventory control of silicon powder is of great concern to enterprises, but due to the complexity of the market environment and the inadequacy of information technology, inventory control of silica powder has been ineffective. One of the most significant reasons for this is that existing methods encounter difficulty in effectively extracting the local and long-term characteristics of the data, which leads to significant errors in forecasting and poor accuracy. This study focuses on improving the accuracy of corporate inventory forecasting. We propose an improved CNN-BiLSTM-attention prediction model that uses convolutional neural networks (CNNs) to extract the local features from a dataset. The attention mechanism (attention) uses the point multiplication method to weigh the acquired features and the bidirectional long short-term memory (BiLSTM) network to acquire the long-term features of the dataset. The final output of the model is the predicted value of silica powder and the evaluation metrics. The proposed model is compared with five other models: CNN, LSTM, CNN-LSTM, CNN-BiLSTM, and CNN-LSTM-attention. The experiments show that the improved CNN-BiLSTM-attention prediction model can predict inbound and outbound silica powder very well. The accuracy of the prediction of the inbound test set is higher than that of the other five models by 7.429%, 11.813%, 15.365%, 10.331%, and 5.821%, respectively. The accuracy of the outbound storage prediction is higher than that of the other five models by 14.535%, 15.135%, 1.603%, 7.584%, and 18.784%, respectively.

Suggested Citation

  • Dudu Guo & Pengbin Duan & Zhen Yang & Xiaojiang Zhang & Yinuo Su, 2024. "Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM)-Attention-Based Prediction of the Amount of Silica Powder Moving in and out of a Warehouse," Energies, MDPI, vol. 17(15), pages 1-22, July.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3757-:d:1446003
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/15/3757/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/15/3757/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. van Wingerden, E. & Basten, R.J.I. & Dekker, R. & Rustenburg, W.D., 2014. "More grip on inventory control through improved forecasting: A comparative study at three companies," International Journal of Production Economics, Elsevier, vol. 157(C), pages 220-237.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Weina & Hekimoğlu, Mustafa & Dekker, Rommert, 2023. "Admission control for a capacitated supply system with real-time replenishment information," International Journal of Production Economics, Elsevier, vol. 266(C).
    2. Zhu, Sha & Jaarsveld, Willem van & Dekker, Rommert, 2020. "Spare parts inventory control based on maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    3. Zhu, Sha & Dekker, Rommert & van Jaarsveld, Willem & Renjie, Rex Wang & Koning, Alex J., 2017. "An improved method for forecasting spare parts demand using extreme value theory," European Journal of Operational Research, Elsevier, vol. 261(1), pages 169-181.
    4. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    5. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Fabian Taigel & Anselme K. Tueno & Richard Pibernik, 2018. "Privacy-preserving condition-based forecasting using machine learning," Journal of Business Economics, Springer, vol. 88(5), pages 563-592, July.
    7. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    8. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3757-:d:1446003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.